Abstract

Near-infrared (NIR) photoimmunotherapy (NIR-PIT) is an emerging cancer therapy based on a monoclonal antibody and phthalocyanine dye conjugate. Direct tumor necrosis and immunogenic cell death occur during NIR irradiation. However, the alteration of tumor blood vessels and blood volume inside the blood vessels induced by the NIR-PIT process is still unknown. In our study, a speckle variance (SV) algorithm combined with optical coherence tomography (OCT) technology was applied to monitor the change of blood vessels and the alterations of the blood volume inside the blood vessels during and after NIR-PIT treatment. Vascular density and the measurable diameter of the lumen in the blood vessel (the diameter of the region filled with blood) were extracted for quantitively uncovering the alterations of blood vessels and blood volume induced by NIR-PIT treatment. The results indicate that both the density and the diameter of the lumen in the blood vessels decrease during the NIR-PIT process, while histological results indicated the blood vessels were dilated. The increase of permeability of blood vessels could lead to the increase of the blood pool volume within the tumor (shown in histology) and results in the decrease of free-moving red blood cells inside the blood vessels (shown in SV-OCT).

© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Targeted cancer therapies offer the promise of more effective tumor treatment with lower side-effects than conventional cancer therapies [1]. Near infrared (NIR) photoimmunotherapy (NIR-PIT) is an emerging cancer therapy with low side effects, which is based on a targeted monoclonal antibody-photon absorber conjugate (APC) [mAb conjugated with a near-infrared phthalocyanine dye (IR700)] that induces highly selective and rapid cellular necrosis after exposure to NIR light [2,3]. The death of cancer cells will further lead to immunogenic cell death that initiates local immune reactions to released cancer antigens from dying cancer cells [4,5]. U.S. Food and Drug Administration (FDA) has initiated a clinical trial of EGFR targeting NIR-PIT in patients with inoperable head and neck cancer in April 2015, and entered into the third clinical efficacy trial in February 2020 [6]. The first EGFR targeting NIR-PIT agent (Akalux) has recently been conditionally approved with a laser system (Bioblade) for clinical use in September 2020 in Japan [7]. Since NIR-PIT has been proposed, numbers of studies have been performed to investigate its treatment effects. Results show PIT is a well effective treatment in treating tumors based on histopathological analysis and bioluminescence imaging [1,813]. However, real-time vascular changes during PIT treatment is still unknown. In this study, we focus on evaluating the alterations of blood vessels and the blood volume inside the blood vessels during and after NIR-PIT treatment.

Quantitative analysis of blood cells or vasculatures in clinic are mainly based on immunohistochemical staining [14,15] or fluorescent proteins [16,17], which are excellent endogenous fluorescence emitters to be used for depicting various biological processes in cells both in vitro and in vivo. However, histological method is invasive and time-consuming. By labeling circulating cells with fluorescent proteins, blood flow in blood vessels can be clearly monitored [18]. Fluorescent proteins labeling is a powerful tools in aiding development of anti-tumor drugs, but the use of fluorescence proteins requires virus-mediated in vivo gene transfection, which is unlikely to be permitted in humans. Thus they are unsuitable for monitoring therapy process in clinical studies. Imaging techniques such as Doppler ultrasound and Magnetic Resonance Imaging (MRI) have been applied to detect blood vessel information in clinics [19,20]. However, Doppler ultrasound as a nondestructive imaging method is mainly applied in imaging the blood vessels with large diameter (diameter > 200 μm) [19]. For blood vessels with small diameter (< 100 μm), the detection sensitivity of Doppler ultrasound is limited. MRI is able to image small blood vessels only with contrast agent [20]. It is necessary to develop a real-time label-free imaging technology to monitor the blood vessels and the blood inside the blood vessels in vivo, and could be potentially used to evaluate the cancer therapy efficacy.

Optical coherence tomography (OCT) is an established biomedical imaging technology for subsurface imaging of tissue samples with high image resolution of less than ten micrometers and a penetration depth of several millimeters, comparable to the size of a standard punch biopsy [21,22]. OCT is based on optical scattering characteristics of tissues with a certain contrast ratio with biological structure and phase information [23]. It has been widely used in biomedical research because of its high imaging speed, noninvasiveness, and high signal-to-noise ratio [24]. OCT imaging is analogous to ultrasound B-mode imaging, with the exception of measuring the echo time delay and intensity of backscattered light rather than sound [24]. In OCT, the optical beam is scanned across the tissue and the backscattered light is measured as a function of axial depth and transverse location. In this way, OCT can generate cross-sectional, tomographic images of subsurface tissue microstructures and obtain 3D tissue morphology reconstruction by stacking a series of 2D OCT tomograms [24]. OCT has been explored and extended not only for structural imaging but also for functional imaging, such as OCT angiography. OCT angiography is becoming a useful and important imaging technique due to its ability to provide volumetric microvascular networks in vivo without the need of exogenous contrast agent [25]. In clinical study, OCT angiography has been shown to help clinicians and physicians monitor the progression of ophthalmology diseases, such as retinopathy [2636], vascular dysfunction between normal and glaucomatous eyes [37,38]. In dermatology, OCT angiography allows the visualization of in vivo blood vessels and their distribution with lesions and offers promising functional information within dermis for up to a depth of 1-2 mm [25]. OCT angiography has been demonstrated to image the vascular networks of two distinct horizontal plexuses in the dermis in normal skin [39], lesion skin [40], and skin cancer [41]. OCT angiography has the potential to improve the diagnostic accuracy of skin diseases by distinguishing the tumor risk, and aiding in the early diagnosis of skin cancer [25]. Besides ophthalmology and dermatology, OCT angiography also showed its feasibility in neuroscience and brain imaging [42]. In addition, OCT angiography has recently been incorporated into endoscopic setup to image neoplastic progression of Barrett’s esophagus (BE) [43] and the microvasculature of the human palm [44], which will further broaden the potential clinical applications of the OCT angiography.

Since OCT signal contains both magnitude and phase information, there are three main types of angiographic methods classified based on utilized OCT signals [25,45]: the first type is based on complex OCT signal (both the magnitude and phase signal), such as optical microangiography [46], the second type is based on intensity signal, such as speckle variance (SV) [47], correlation mapping [25,48], and split-spectrum amplitude decorrelation angiography (SSADA) [49]; the third type is based on phase signal, such as phase variance [50]. Each type of these three OCT based angiographic methods has its own advantage due to its unique physics and mathematics behind the blood flow contrast mechanism [45]. Since both complex-signal-based and phase-signal-based methods are phase-sensitive, therefore a phase-stable system is required for high-contrast images [51]. On the other hand, intensity-signal-based OCT angiography does not suffer from phase noise artifacts and is relatively less sensitive to phase noise, making it particularly helpful in situations where the phase stability of light source is an issue [25,45,49]. In our study, since a swept-source laser is used, thus we selected the intensity-signal-based method. In a previous quantitative comparison study, SV OCT angiography has demonstrated better performance in terms of vessel connectivity, image contrast, and signal-to-noise ratio (SNR) than other intensity-signal-based methods, such as correlation mapping and SSADA [45]. Besides, less computation time is needed for SV OCT angiography due to the simple subtraction operation [45]. Taking all the factors into consideration, SV OCT angiography was selected for monitoring PIT process in this study.

The first application of using speckle analysis in OCT images to acquire a depth-resolved flow signals is reported in 2005 [52]. And then, SV algorithm has been used to image blood vessels based on the amplitude information in OCT structural images [5356]. In a previous study, the hemodynamic alterations in a single large vessel with diameter over 150 μm were monitored during PIT by OCT, including SV analysis and Doppler flow measurement, the results show that blood velocity in peripheral tumor vessels quickly drops below the detection limit during PIT [57].

In this study, we employed conventional structural OCT together with SV-OCT to monitor the lumen in tumor blood vessels with various sizes in real time during and after NIR-PIT in situ and in vivo. Quantitative parameters of tumor blood vessels, such as vascular density and diameters of lumen in the blood vessels, were analyzed to further evaluate the NIR-PIT therapy. This technology could potentially be used to optimize the effectiveness of treatment.

2. Materials and methods

2.1. OCT system and SV-OCT

A swept-source OCT system (VEG220C1, Thorlabs, Newton, New Jersey, USA) was employed in our experiments [58]. The swept-source laser is centered at 1300 nm with a spectrum bandwidth of 100 nm. The axis resolution in the air can reach 16 µm, the lateral resolution is 13 µm [58]. The imaging depth (coherence length) can reach 8 mm in air. The laser source operates at a sweep rate of 100 kHz with 12 mW output power. A three-dimensional data set was acquired in monitoring PIT process (pixels number: 625 [X] by 625 [Y] by 100 [Z]; dimension: 1.25 mm [X] by 1.25 mm [Y] by 0.6 mm [Z]. The effective imaging frame rate of this system was 66 frames/s and the acquisition time for one 3D dataset took less than 10 seconds.

To characterize the tumor structure and the lumen of blood vessels in tumor, speckle variance (SV) and conventional structural OCT were applied in our experiments. SV-OCT images microvasculature by calculating the interline or interframe speckle variance of the intensity-based structural OCT images [47,51]. The SV analysis was performed as follow [51,59,60]:

$${\rm{SV}}({{\rm{i}},{\rm{\;j}},{\rm{\;k}}} )= \frac{1}{N}\mathop \sum \nolimits_{i = 1}^N {\left[ {I({i,\;j,\;k} )- \frac{1}{N}\mathop \sum \nolimits_{i = 1}^N I({i,\;j,\;k} )} \right]^2}$$
where I is the OCT signal intensity at each pixel, and $i,j,k$ are indices for the B scan (frame, transverse, and axial pixels). N is the number of frames used in variance calculation and 4 frames were used in our experiment.

2.2. Phantom design

To demonstrate the accuracy of SV-OCT system for characterizing the blood inside the blood vessels, three silicone capillaries with an inner diameter of 200 ± 20 μm (ID: 0.2 mm, OD: 2.4 mm, VitroCom Inc.) were fabricated as vessel phantom [61]. These three silicone tubes were filled with intralipid solution of 1% volume concentration to mimic the blood. Two sides of each silicone tube were sealed by glue. The filled capillaries were fixed side by side on an empty petri dish. Then, 1.5-g agar powder (Now Inc.), 0.5-mL Intralipid solution (Hospira Inc.), and 49.5-mL PBS buffer were mixed and stirred well. After heating in a microwave for ∼2 minutes, the mixed liquid gel was poured into the petri dish on which the glass capillaries were fixed until the capillaries were submerged in the liquid gel. After the liquid gel cooled and solidified, the vessel phantom was ready to be imaged.

2.3. Synthesis of mAb and IR700

Conjugation of IR700 dyes with mAbs was performed as described previously [10]. In brief, panitumumab (1.0 mg, 6.8 nmol) was incubated with IRDye 700DX NHS ester (66.8 μg, 34.2 nmol) in 0.1 M Na2HPO4 (pH 8.6) at room temperature for 1 h. The mixture was purified with a Sephadex G25 column (PD-10; GE Healthcare Life Sciences, Pittsburgh, PA, USA). Coomassie Plus protein assay kit (Thermo Fisher Scientific, Waltham, MA, USA) was applied to determine protein concentration by measuring the absorption at 595 nm with UV–Vis (8453 Value System; Agilent Technologies, Santa Clara, CA, USA). The concentration of IR700 was measured by absorption at 689 nm to confirm the number of fluorophore molecules per mAb. This synthesis was controlled so that there are about two IR700 molecules bound to a single antibody on average. IR700 conjugated to EGFR-targeting panitumumab were abbreviate as pan-IR700. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) was performed as a quality control for the conjugate. Conjugate was separated by SDS-PAGE with a 4–20% gradient polyacrylamide gel (Life Technologies, Gaithersburg, MD, USA). A standard marker (Crystalgen Inc., Commack, NY, USA) was used as a protein molecular weight marker. After electrophoresis at 80 V for 2.5 h, the gel was imaged with a Pearl Imager (LI-COR Biosciences, Lincoln, Nebraska, USA) using a 700 nm fluorescence channel. Diluted panitumumab was used as a control. The gel was stained with Colloidal Blue staining to determine the molecular weight of conjugate.

2.4. Animal and tumor models

Six 6-week-old female nude mice were purchased from Charles River (NCI-Frederick, Frederick, Maryland). All in vivo animals’ procedures were performed in compliance with the Guide for the Care and Use of Laboratory Animal Resources (1996), US National Research Council, and approved by the Institutional Animal Care and Use Committees (IACUCs) at the University of Maryland and NIH. Two million A431 cells were inoculated to two sides of dorsum of each nude mouse. Tumor volumes were measured and calculated using the following formula: tumor volume = ½(length ×width2) [3,57]. Mice were monitored for their general health and tumor sizes every day. Tumor volumes reached 100 mm3 were selected for experiment. 100 μg of pan-IR700 was injected into mice via the lateral tail vein. 24 h after injection, mice were prepared for experiment. Before imaging, the skin above the tumor was removed and a window chamber was used to fix the tumor for minimizing the imaging artifact from animal movement as previous reported [56,62].

Near infrared LED used in our experiments has a wavelength spectrum of 680 - 700 nm (Tech-LED, Marubeni America Corp., Santa Clara, CA, USA) [63,64]. LED was turned on for ∼30 min with a dose of ∼50 J/cm2 to irradiate the tumor [64]. To record the alterations of tumor vasculature during and after NIR-PIT caused by LED, the data acquisition protocol is as follows: An image of tumor was taken every 10 min for the first 30 min before LED was turned on. This 30 min period of recording without LED irradiation was considered as control group. For the following 30 min, the LED was turned on and images were captured every 5 min, the NIR-PIT treatment was completed after 30 min of LED exposure. To record the tumor vasculature's post-treatment alterations, the response after NIR-PIT were continued to be observed. Images were taken every 5 min, which also lasted 30 min. Tumor xenografts were excised from mice after experiments. They were fixed with 10% formalin, embedded in paraffin, sectioned (10 µm thick) and stained with hematoxylin and eosin (H & E) for histological analysis. To clarify the shapes of blood vessels, anti-CD31 (clone D8V9E, 1:200 dilution; Cell Signaling Technology, Danvers, MA, USA) immunohistochemical staining was performed with Bond RXm auto stainer (Leica Biosystems, Wetzlar, Germany). Light microscopy with an Olympus BX61 (Olympus Corporation) was applied to evaluate histological changes.

2.5. Vessel characterization

To quantitatively analyze the change of tumor vasculatures in the NIR-PIT treatment, two parameters were extracted in this study, namely, the vascular density (VD) and the diameter of lumen in blood vessels (DLBV). The signal obtained from SV-OCT indicated the region inside the blood vessels that was filled with blood. The vascular density (VD) is defined as VD = $\frac{{{A_{vessel}}}}{{{A_{total}}}}$, where ${A_{vessel}}$ is the area of vessels filled with blood, ${A_{total}}$ is the total area of image. In general, the DLBV value at the beginning of the experiment was normalized to 1, and the values at other time points were compared with the results at the beginning. The measurable diameter of lumen in blood vessel (MDLBV, the diameter of the region filled with blood) was calculated based on the centerline algorithm [65]. First, the vessel image was segmented and turned into binary image. Then the centerline and boundary in each vessel were determined by ‘bwmorph’ and ‘bwboundaries’ functions in MATLAB toolbox, respectively. After that, the shortest distance from each centerline point to the boundary was calculated as radius at the corresponding position.

2.6. Data analysis

Data are expressed as mean ± standard deviation. A two-sample t-test with unequal means was completed to determine whether the difference was significant in the statistical parameters between any two sample groups. Differences were regarded as statistically significant when p<0.05.

3. Results

3.1. Phantom study

Vessel phantom diameters obtained from conventional structural OCT mode and SV-OCT mode are represented in Fig. 1. Figure 1(a) shows the cross-sectional image of three capillary tubes with inner diameter ∼200 μm in conventional structural OCT mode, which were embedded at different depth in the agar gel. Tubes 1-3 shown in Fig. 1(a) are located from shallow to deep. The embedded glass capillaries are located at different depths from surface with ∼25 μm, 50 μm, and 112 μm for tubes 1-3, respectively. Figure 1(b) displays the projection view of three capillaries tubes in SV-OCT mode. Since agar phantom is solid, there is no particle motion, and it generates limited signal in SV mode. While the intralipid particles can freely move inside the capillaries, they can be well exhibited through the SV calculation. An en face image in conventional structural OCT mode of phantom is displayed in Fig. 1(c). Both glass capillaries and agar phantom can be observed in conventional structural OCT mode. On the other hand, SV-OCT can reflect the space with free moving particles within vessels (inner diameter or lumen of the vessels), but not the vessels wall. Figure 1(d) shows the binary image and centerline processed from the SV-OCT image. Figure 1(e) compares the diameters of lumen in vessel phantom measured by conventional structural OCT and SV-OCT mode in three glass capillaries. For obtaining the accurate diameter of the tubes, tube 1 was measured from en face image at the depth of 165 µm, tube 2 was measured at about 190 µm, and tube 3 was measured at about 250 µm in conventional structural OCT mode. While in SV-OCT mode, the diameters were obtained from projection view. Since Intralipid has filled the entire glass capillaries, the diameter of intralipid obtained by SV-OCT can represent the size of the inner diameter of capillaries. Ten measurements from ten different locations in the same en face image of each capillary were averaged for statistical analysis. From Fig. 1(d), the diameter of tube 3 is smaller than that of tube 1 or 2. Tube 3 also seems uneven. It may be due to the fabrication variance while still within the 10% variance range the factory claimed. Results show the value is consistent from two imaging modes (conventional structural OCT and SV-OCT mode). Therefore, it is feasible to measure the diameter of blood vessels using conventional structural OCT and the diameter of the region filled with blood (lumen) using SV-OCT mode.

 figure: Fig. 1.

Fig. 1. Calibration of silicone tubes. (a) Cross-sectional OCT image of the vessel phantom. (b) Projection view of capillary tubes in SV-OCT mode. (c) En face image in conventional structural OCT mode. (d) Binary and centerline of (b). (e) Measured value of lumen in vessel phantom diameters from conventional structural OCT in largest en face image of each capillaries tube and SV-OCT in projection view image. Scales bar: 100 μm. N=10. NS denotes P > 0.05.

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3.2. Vessel measurement in normal tissues

Vessel images of normal skin tissue from SV-OCT and conventional structural OCT are displayed in Fig. 2. Figure 2(a) is projection image of lumen in blood vessels (the region filled with blood), which is reconstructed from SV-OCT images from depth 0 µm-480 μm. The image contains different sizes of vessels from different depths of tissue.

 figure: Fig. 2.

Fig. 2. Skin vessel images. (a) Projection view image of skin blood vessels in SV mode with multilayer superposition. (b) Blood vessels in SV mode pseudo-colored by depth from 0 μm to 480 μm. (c) En face image of skin blood vessel in structure OCT mode. (d) 3D images of fused SV-OCT and conventional structural OCT results. (e) Cross-sectional image of bio-tissue in SV mode corresponding the position indicated by the white dashed square in (d). (f) Cross-sectional image in conventional structural OCT mode corresponding the position indicated by the white dashed square in (d). (g) Fused cross-sectional images of SV mode and conventional structural OCT mode from the white arrow in (d); Scale bar: 100 μm.

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From Fig. 2(a), SV-OCT can clearly distinguish the micro-vessels as small as 28 μm. Blood vessels distributed at different depths can also be clearly resolved. To further analyze the distribution of vessels at different depths, blood vessels are pseudo-colored with depth information ranging from 0 μm to 480 μm, as shown in Fig. 2(b). The surface (0 μm) is indicated by red color and the deepest position (480 μm) is indicated by blue color. Figure 2(c) shows the image from conventional structural OCT. Large vessels can be easily distinguished, while the microvasculature is indistinguishable compared to Fig. 2(a) using SV-OCT mode. The 3D overlay image of SV-OCT and conventional OCT image volume is displayed in Fig. 2(d). Figure 2(e), (f), and (g) display the cross-sectional image of SV-OCT mode, conventional structural OCT mode, and the fused image from the cross-section indicated in Fig. 2 (d), respectively. The location of blood vessels on tissue can be directly visualized on the fused SV-OCT and conventional structural OCT image.

3.3. Vascular changes in tumor without LED illumination

Before exposed to LED, tumors with pan-IR700 were imaged by OCT. The group that without LED exposure was taken as the control data (the baseline data of the same group of animals). The results of no-LED-exposure from SV-OCT mode are shown in Fig. 3. Figure 3(a) shows the vessels filled with blood pseudo-colored with depth from 0 μm to 480 μm. Figure 3(b) is the corresponding 3D images. The changes are negligible in the vessels. To further depict the changes, the VD and MDLBV from 0 to 30 min without LED exposure were calculated. Changes of VD over time are displayed in Fig. 3(c). Results show the density almost experience no change from 0 min to 30 min. Changes of MDLBV over time are shown in Fig. 3(d). Four blood vessels were selected which are labeled by the white arrow 1, 2, 3, and 4 in Fig. 3(b). Results demonstrate that there are negligible changes in both VD and MDLBV without LED exposure.

 figure: Fig. 3.

Fig. 3. Images of tumor vasculature changes without LED exposure from 0 to 30 min. (a) The vessel lumen pseudo-colored with depth from 0 μm to 480 μm. (b) The vessels in 3D mode. (c) Vascular density without LED exposure from 0 to 30 min. (d) Measurable diameter of lumen in blood vessel with different sizes (at locations 1-4 indicated by arrows in b) from 0 to 30 min. Scale bar = 200μm. N=6.

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3.4. Vascular changes in tumor with LED exposure

After obtaining the images of tumor vasculatures without LED exposure, the LED was turned on for PIT treatment. The results of tumor vascular changes with LED exposure are displayed in Fig. 4. Figure 4(a) shows the pseudo-colored SV-OCT image with depth from 0 μm to 480 μm at 0 min, 10 min, 20 min, and 30 min during LED exposure, respectively. Figure 4(b) is the corresponding 3D images. After 10 minutes of LED exposure, there was a slight decrease in the vascular density compared to that at 0 min (when the LED was turned on), especially in the position indicated by the 400 μm * 400 μm white square. After that, the vascular density decreased over time, and some small vessels vanished as labeled by the white arrows in Fig. 4(a). After 30 minutes of irradiation, the LED was turned off. The changes of vasculatures were continued to be monitored for another 30 minutes. The results of tumor vasculatures alteration after turning off the LED are shown in Fig. 4(c) and (d). Results show the density of vessels continues to decrease after turning off the LED, and some small vessels continue to vanish (indicated in the white square from 0 min to 60 min).

 figure: Fig. 4.

Fig. 4. Images of tumor vasculature changes during and after NIR-PIT process. (a) The lumen in blood vessels pseudo-colored with depth from 0 μm to 480 μm during LED-ON for 30 min. (b) The corresponding 3D vessels with SV-OCT during LED-ON for 30 min. (c) The lumen in blood vessels pseudo-colored with depth from 0 μm to 480 μm within 30 min after turning off LED. (d) The corresponding 3D vessels within 30 min after turning off LED. Scale bar = 200μm.

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Cross-sectional OCT images can clearly display the location of tumor vessels. Structure information from cross-sectional images were also analyzed in this study. Cross-sectional images of tumor from both conventional structural OCT mode and SV mode during and after NIR-PIT treatment are displayed in Fig. 5. Three time points were demonstrated: right before LED irradiation (0 min), right after LED irradiation (30 min), and 30 min after LED was turned off. The first row shows the cross-sectional images from conventional structural OCT mode, the second row shows the corresponding lumen in blood vessels (the region filled with blood) from SV mode, and the third row is the fused image of the two modes. The conventional structural OCT mode reflects the blood vessel position (labeled by white arrows). In the SV-OCT mode, both diameter and density of lumen in blood vessels decrease can be clearly visualized at 30 min and 60 min. The fused image can give better visualization about the change of lumen in blood vessels and the location of the whole blood vessels during and after PIT treatment (the third row).

 figure: Fig. 5.

Fig. 5. Change of tumor vasculatures during and after NIR-PIT process in cross-sectional view. The first row is tumor structure obtained by conventional structural OCT mode, the second row is lumen blood vessel structure (the region filled with blood) obtained by SV mode, and the third row is the overlay of two modes. First column is at time point right before LED irradiation; the second column is right after 30 min LED irradiation. The third column is 30 min after LED was turned off. Scale bar: 200 μm.

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To quantify the changes of the blood volume inside the blood vessels, we calculated the VD (the density of area filled with blood) and MDLBV (the measurable diameter of the region filled with blood) during and after NIR-PIT treatment as shown in Fig. 6. Normalized VD was displayed in Fig. 6(b). In our study, density value at 0 min was normalized as 1. Density values at other time points were compared with the density value at 0 min. Results show that the density changed slightly at the first 5 min of LED irradiation, then gradually decreased after 10 min. The same trend continued after the LED was turned off. At 60 min, the density value decreased to 0.83 compared to the initial value at 0 min. Figure 6(c) displays the diameter change of blood vessels lumen in 60 min with various vessel sizes labeled in Fig. 6(a). Results indicate that all the vessel diameters display shrinking trends despite of the size. The large vessels have decreased in size, while the small vessels or capillaries (< 50 µm in diameter) gradually vanish and cannot be detected at/after 50 min. Figure 6(d) shows the reduction ratio of diameter at various sizes at 60 min or the last time point that it could be detected before vanishing. The reduction in diameter is about 21% for diameter of 30-50 µm, about 20% for diameter of 60-80 µm, about 24% for diameter of 90-110 µm, and about 28% for diameter of 130-150 µm. Results show that the larger vessels filled with blood have more reduction.

 figure: Fig. 6.

Fig. 6. Changes in tumor blood vessels during NIR-PIT process (a) 3D vessels with blood image. (b) Normalized vascular density changes. (c) MDLBV changes with different sizes. (d) MDLBV reduction with different sizes. Scale bar: 100 μm. N=6. NS denotes P > 0.05, * denotes P < 0.05, ** denotes P <0.01, *** denotes P <0.001.

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Histology images with H&E and CD31 staining are shown in Fig. 7. With H&E staining, the red blood cells do not display a nucleus and can be found in the well-preserved blood vessels as indicated by the red arrows [57]. They can be found in both control and irradiated groups. The blood vessels have well-defined boundary and can be identified easily in both the control and NIR-PIT-treated group indicating the blood vessels are not damaged by NIR-PIT treatment, which is consistent with our in vivo data from conventional structural OCT and SV-OCT. The tumor cells collapsed after NIR light exposure. CD31 is primarily used to demonstrate the presence of endothelial tissue and can also be used to demonstrate hemangiomas. CD31 staining was used to label the endothelial cell of blood vessels in our experimental. Result demonstrates that the blood vessels in the tumors are markedly dilated in the NIR-PIT-treated group compared with the control group (see the black arrow), which is consistent with previously reported results [57].

 figure: Fig. 7.

Fig. 7. Histological results of the NIR-PIT treated tumor and control group (no LED exposure). (a) and (b) are H&E staining images. (c) and (d) are CD31 staining images. (a) and (c) are control group (no LED exposure). (b) and (d) are PIT-treated tumor. Scale bar: 100 μm.

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4. Discussion

Unlike some fluorescent imaging technology, OCT does not require any contrast agent and has been approved by FDA to image retinal blood vessels in ophthalmic patients in clinic [66,67]. SV-OCT, which relies on calculation of intensity variance over consecutively acquired frames, is a powerful approach for recognizing fluid flow and has advantages for imaging the blood inside the blood vessels [68]. SV-OCT and Doppler-OCT have been used for monitoring a single vessel change during the PIT process in our previous study [57]. Due to the limitation of imaging speed in the previous system, only a small region and a single large peripheral tumor vessel (>150 μm) was observed. Small vessels or capillaries could not be detected. In this study, OCT was applied to monitor the changes of lumen vessels with different sizes during and after NIR-PIT treatment through SV mode and conventional structural OCT mode. The diameters of tumor lumen vessels above 20 μm can be well resolved. Furthermore, we quantitively revealed the change of density of lumen in blood vessels and the alterations of the diameter of lumen in blood vessels during and after NIR-PIT treatment.

The density and diameter of lumen in the blood vessels remained nearly unchanged without LED irradiation as shown in the SV-OCT images from Fig. 3, which indicates that our OCT laser light (with central wavelength at 1310 nm) did not interact with the Pan-mAb and trigger response on the tumor.

From Fig. 4 and Fig. 6, the vascular changes were not obvious in the first 10 minutes of LED irradiation, and then the MDLBV of both big vessels and some small vessels/capillaries gradually decreased with time. After LED was turned off, the diameters continued to decrease or vanish, indicating the decrease of blood inside the blood vessels. Results indicate all vessels with different dimensions have changed. The larger blood vessels have shown the greater reduction. Although we only monitored the NIR-PIT process for 60 min, we could distinctly observe the changes of the blood vessels. While on the other hand, histological results (shown in Fig. 7) show the dilation of the blood vessels. Unlike other ablative cancer therapies, both in vivo SV-OCT imaging (Fig. 4) and histology images (Fig. 7) verified that the tumor blood vessels remained intact during and after NIR-PIT treatment. The dilation of the blood vessels may relate to the previously reported super enhanced permeability and retention (SUPR) effect in NIR-PIT treatment [69]. The dilation of the blood vessels and the SUPR effect will increase the permeability of the blood vessels and decrease the intravascular pressure in the blood vessels, and further increase the blood pool volume in the tumor. This process will result in decreased blood flow and blood volume in the tumor blood vessels, which causes the reduction in the signal that we can detect from SV-OCT. If the signal caused by the decreased blood flow and blood volume is below the detection limit of our OCT system, the red blood cells inside the blood vessels cannot be detected anymore, which can explain the vanishing of small blood vessels/capillaries. The vanishing of small blood vessels/capillaries could also be caused by the obstruction of the vessels, since an obstructed vessel could result in the absence of free moving scatters and the vessel would lose its variance contrast relative to static tissue. Indeed, peripheral tumor blood flow after NIR-PIT dramatically slowed down [57]. We realized the limitations of this study. First, the SV-OCT method is sensitive to particles/blood inside the blood vessels, it cannot image the change of blood vessels and blood flow speed directly. On the other hand, the histological results can only indicate the blood vessels size, while it cannot provide change of the same blood vessel before and after NIR-PIT treatment since it is a terminal procedure. The results from SV-OCT and histology cannot verify each other since they are measuring different parameters. To prove our hypothesis above and thoroughly study the change of the blood vessels size, the change of blood flow speed on the same blood vessels before and after NIR-PIT treatment, intravital multiphoton microscope will be used in the next step as we did in the kidney imaging [70,71].

We need to notice some images of the blood vessels in this study are fuzzy, which may mainly come from the motion artifact due to animal breathing and heartbeat although a dorsal window chamber was used to fix the tumor as previously reported [56,62]. Since the tumor cells were inoculated to dorsum of the mouse in this study, the motion artifact from animal breathing and heartbeat is evitable. This motion artifact can also be seen in Fig. 5: some blood vessels were not displayed at 0 min, but were displayed at 30 min and 60 min. In the future, we will try to minimize the imaging artifact by inoculating the tumor to other regions less sensitive to motion artifact such as mouse ear. In terms of OCT system, we will further optimize the B-scan repetition number and adapt new laser with higher imaging speed, which in principle can further reduce the artifacts caused by motion [45,56].

5. Conclusion

In conclusion, SV-OCT mode and conventional structural OCT mode were applied to monitor the change of tumor vasculatures during and after NIR-PIT treatment. Results show the reduction in both density and diameter of the blood vessels lumen in SV-OCT mode. Dilated vessels have been observed in histological analysis.

Although the study is on a small number of samples, these results indicate that the changes of blood vessels and the alterations of the blood volume inside the blood vessels during the NIR-PIT treatment process. It can aid researchers in deciding the course of therapy, determining the need for treatment.

Funding

National Natural Science Foundation of China (81901787).

Acknowledgments

Qinggong Tang would like to acknowledge grant from the Research Council of the University of Oklahoma Norman Campus.

Disclosures

The authors declare no conflicts of interest related to this article.

References

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References

  • View by:

  1. Q. Tang, T. Nagaya, Y. Liu, H. Horng, J. Lin, K. Sato, H. Kobayashi, and Y. Chen, “3D mesoscopic fluorescence tomography for imaging micro-distribution of antibody-photon absorber conjugates during near infrared photoimmunotherapy in vivo,” J. Controlled Release 279, 171–180 (2018).
    [Crossref]
  2. M. Makoto, N. Takahito, S. Kohei, K. M. Gabriela, P. L. Choyke, and K. Hisataka, “Immediatein vivotarget-specific cancer cell death after near infrared photoimmunotherapy,” BMC Cancer 12(1), 345 (2012).
    [Crossref]
  3. M. Mitsunaga, M. Ogawa, N. Kosaka, L. T. Rosenblum, P. L. Choyke, and H. Kobayashi, “Cancer cell-selective in vivo near infrared photoimmunotherapy targeting specific membrane molecules,” Nat. Med. 17(12), 1685–1691 (2011).
    [Crossref]
  4. H. Kobayashi, A. Furusawa, A. Rosenberg, and P. L. Choyke, “Near-infrared photoimmunotherapy of cancer: a new approach that kills cancer cells and enhances anti-cancer host immunity,” Int. Immunol. 33(1), 7–15 (2021).
    [Crossref]
  5. H. Kobayashi and P. L. Choyke, “Near-infrared photoimmunotherapy of cancer,” Acc. Chem. Res. 52(8), 2332–2339 (2019).
    [Crossref]
  6. “FDA PIT clinical, https://clinicaltrials.gov/ct2/show/NCT02422979 ..”
  7. “ https://www.biospace.com/article/releases/rakuten-medical-japan-announces-marketing-approval-of-akalux-iv-infusion-250mg-and-bioblade-laser-system-from-the-ministry-of-labour-health-and-welfare-in-japan-for-locoregional-cancer-treatment/ .”
  8. T. Nagaya, K. Sato, T. Harada, Y. Nakamura, P. L. Choyke, and H. Kobayashi, “Near infrared photoimmunotherapy targeting EGFR positive triple negative breast cancer: optimizing the conjugate-light regimen,” PLoS One 10(8), e0136829 (2015).
    [Crossref]
  9. M. Sakuma, S. Kita, and H. Higuchi, “Quantitative evaluation of malignant gliomas damage induced by photoactivation of IR700 dye,” Sci. Technol. Adv. Mater. 17(1), 473–482 (2016).
    [Crossref]
  10. K. Sato, H. Hanaoka, R. Watanabe, T. Nakajima, P. L. Choyke, and H. Kobayashi, “Near infrared photoimmunotherapy in the treatment of disseminated peritoneal ovarian cancer,” Mol. Cancer Ther. 14(1), 141–150 (2015).
    [Crossref]
  11. T. Nagaya, Y. Nakamura, K. Sato, T. Harada, P. L. Choyke, and H. Kobayashi, “Improved micro-distribution of antibody-photon absorber conjugates after initial near infrared photoimmunotherapy (NIR-PIT),” J. Controlled Release 232, 1–8 (2016).
    [Crossref]
  12. Y. Maruoka, T. Nagaya, Y. Nakamura, K. Sato, F. Ogata, S. Okuyama, P. L. Choyke, and H. Kobayashi, “Evaluation of early therapeutic effects after near-infrared photoimmunotherapy (NIR-PIT) using luciferase–luciferin photon-counting and fluorescence imaging,” Mol. Pharmaceutics 14(12), 4628–4635 (2017).
    [Crossref]
  13. K. Kawamura, Y. Tanaka, H. Nakasone, Y. Ishihara, and Y. Kanda, “Development of a unique T cell receptor gene-transferred tax-redirected T cell immunotherapy for adult T cell leukemia,” Biol. Blood Marrow Transplant. 26(8), 1377–1385 (2020).
    [Crossref]
  14. A. Nimgaonkar, O. Segurado, W.-S. Tsai, S.-T. Pang, and R. Mei, “A novel circulating tumor cell blood test for early detection of colorectal, prostate, and breast cancers: Results from 709 samples,” J. Clin. Oncol. 36(15_suppl), e13549 (2018).
    [Crossref]
  15. F. D. Maggio, P. Arumugam, F. R. Delvecchio, S. Batista, and H. M. Kocher, “Pancreatic stellate cells regulate blood vessel density in the stroma of pancreatic ductal adenocarcinoma,” Pancreatology 16(6), 995–1004 (2016).
    [Crossref]
  16. K. Yamauchi, M. Yang, and P. Jiang, “Real-time in vivo dual-color imaging of intracapillary cancer cell and nucleus deformation and migration,” Cancer Res. 65(10), 4246–4252 (2005).
    [Crossref]
  17. M. Yang, P. Jiang, and R. M. Hoffman, “Whole-body subcellular multicolor imaging of tumor-host interaction and drug response in real time,” Cancer Res. 67(11), 5195–5200 (2007).
    [Crossref]
  18. Ihler Friedrich, Bertlich Mattis, Weiss Bernhard, Dietzel Steffen, Martin, and Canis, “Two-photon microscopy allows imaging and characterization of cochlear microvasculature in vivo,” BioMed Res. Int. 2015, 1–8 (2015).
    [Crossref]
  19. C. Joo Hee, M. Woo Kyung, C. Nariya, C. S. Yang, P. Seong Ho, P. Jeong Mi, H. Boo Kyung, C. Yeon Hyun, C. Gyunggoo, and I. Jung-Gi, “Differentiation of benign from malignant solid breast masses: conventional US versus spatial compound imaging,” Radiology 237(3), 841–846 (2005).
    [Crossref]
  20. L. Lin, W. Kai, S. Xilin, W. Kezheng, S. Yingying, Z. Guangfeng, and S. Baozhong, “Parameters of dynamic contrast-enhanced MRI as imaging markers for angiogenesis and proliferation in human breast cancer,” Med. Sci. Monit. 21, 376–382 (2015).
    [Crossref]
  21. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
    [Crossref]
  22. P. H. Tomlins and R. K. Wang, “Theory, developments and applications of optical coherence tomography,” J. Phys. D: Appl. Phys. 38(15), 2519–2535 (2005).
    [Crossref]
  23. R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, Z. Liu, D. T. Miller, and J. S. Werner, “A review of adaptive optics optical coherence tomography: technical advances, scientific applications, and the future,” Invest. Ophthalmol. Visual Sci. 57(9), OCT51–OCT68 (2016).
    [Crossref]
  24. W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
    [Crossref]
  25. C.-L. Chen and R. K. Wang, “Optical coherence tomography based angiography [Invited],” Biomed. Opt. Express 8(2), 1056–1082 (2017).
    [Crossref]
  26. T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
    [Crossref]
  27. A. Ishibazawa, T. Nagaoka, A. Takahashi, T. Omae, T. Tani, K. Sogawa, H. Yokota, and A. Yoshida, “Optical Coherence Tomography Angiography in Diabetic Retinopathy: A Prospective Pilot Study,” Am. J. Ophthalmol. 160(1), 35–44.e1 (2015).
    [Crossref]
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2021 (1)

H. Kobayashi, A. Furusawa, A. Rosenberg, and P. L. Choyke, “Near-infrared photoimmunotherapy of cancer: a new approach that kills cancer cells and enhances anti-cancer host immunity,” Int. Immunol. 33(1), 7–15 (2021).
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2020 (4)

K. Kawamura, Y. Tanaka, H. Nakasone, Y. Ishihara, and Y. Kanda, “Development of a unique T cell receptor gene-transferred tax-redirected T cell immunotherapy for adult T cell leukemia,” Biol. Blood Marrow Transplant. 26(8), 1377–1385 (2020).
[Crossref]

R. Schwartz, H. Khalid, S. Sivaprasad, L. Nicholson, E. Anikina, P. Sullivan, P. J. Patel, K. Balaskas, and P. A. Keane, “Objective evaluation of proliferative diabetic retinopathy using OCT,” Ophthalmol. Retina 4(2), 164–174 (2020).
[Crossref]

D. Chen, W. Yuan, H.-C. Park, and X. Li, “In vivo assessment of vascular-targeted photodynamic therapy effects on tumor microvasculature using ultrahigh-resolution functional optical coherence tomography,” Biomed. Opt. Express 11(8), 4316–4325 (2020).
[Crossref]

S. Wu, H. Guo, H. Horng, Y. Liu, H. Li, P. Daneshpajouhnejad, A. Rosenberg, C. Albanese, S. Ranjit, P. M. Andrews, M. Levi, Q. Tang, and Y. Chen, “Morphological and functional characteristics of aging kidneys based on two-photon microscopy in vivo,” J. Biophotonics 13, e201900246 (2020).
[Crossref]

2019 (3)

L. M. Wurster, R. N. Shah, F. Placzek, S. Kretschmer, M. Niederleithner, L. Ginner, J. Ensher, M. P. Minneman, E. E. Hoover, H. Zappe, W. Drexler, R. A. Leitgeb, and Ç. Ataman, “Endoscopic optical coherence tomography angiography using a forward imaging piezo scanner probe,” J. Biophotonics 12(4), e201800382 (2019).
[Crossref]

K. B. Schaal, M. R. Munk, I. Wyssmueller, L. E. Berger, M. S. Zinkernagel, and S. Wolf, “Vascular abnormalities in diabetic retinopathy assessed with swept-source optical coherence tomography angiography widefield imaging,” Retina 39(1), 79–87 (2019).
[Crossref]

H. Kobayashi and P. L. Choyke, “Near-infrared photoimmunotherapy of cancer,” Acc. Chem. Res. 52(8), 2332–2339 (2019).
[Crossref]

2018 (7)

Q. Tang, T. Nagaya, Y. Liu, H. Horng, J. Lin, K. Sato, H. Kobayashi, and Y. Chen, “3D mesoscopic fluorescence tomography for imaging micro-distribution of antibody-photon absorber conjugates during near infrared photoimmunotherapy in vivo,” J. Controlled Release 279, 171–180 (2018).
[Crossref]

A. Nimgaonkar, O. Segurado, W.-S. Tsai, S.-T. Pang, and R. Mei, “A novel circulating tumor cell blood test for early detection of colorectal, prostate, and breast cancers: Results from 709 samples,” J. Clin. Oncol. 36(15_suppl), e13549 (2018).
[Crossref]

Q. Zhang, K. A. Rezaei, S. S. Saraf, Z. Chu, F. Wang, and R. K. Wang, “Ultra-wide optical coherence tomography angiography in diabetic retinopathy,” Quant. Imaging Med. Surg. 8(8), 743–753 (2018).
[Crossref]

J. Pan, D. Chen, X. Yang, R. Zou, K. Zhao, D. Cheng, S. Huang, T. Zhou, Y. Yang, and F. Chen, “Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography,” Am. J. Ophthalmol. 192, 146–156 (2018).
[Crossref]

G. Liu, D. Xu, and F. Wang, “New insights into diabetic retinopathy by OCT angiography,” Diabetes Res. Clin. Pract. 142, 243–253 (2018).
[Crossref]

H. Guo, H.-W. Wang, Q. Tang, E. Anderson, R. Falola, T. Smith, Y. Liu, M. Levi, P. M. Andrews, and Y. Chen, “Intravital imaging of adriamycin-induced renal pathology using two-photon microscopy and optical coherence tomography,” J. Innovative Opt. Health Sci. 11(05), 1850030 (2018).
[Crossref]

P. Nelis, I. Kleffner, M. C. Burg, C. R. Clemens, M. Alnawaiseh, J. Motte, M. Marziniak, N. Eter, and F. Alten, “OCT-Angiography reveals reduced vessel density in the deep retinal plexus of CADASIL patients,” Sci. Rep. 8(1), 8148 (2018).
[Crossref]

2017 (8)

O. A. Grishina, S. Wang, and I. V. Larina, “Speckle variance optical coherence tomography of blood flow in the beating mouse embryonic heart,” J. Biophotonics 10(5), 735–743 (2017).
[Crossref]

S. Caujolle, R. Cernat, G. Silvestri, M. Marques, A. Bradu, T. Feuchter, G. Robinson, D. K. Griffin, and A. Podoleanu, “Speckle variance OCT for depth resolved assessment of the viability of bovine embryos,” Biomed. Opt. Express 8(11), 5139–5150 (2017).
[Crossref]

Q. Tang, Y. Liu, V. Tsytsarev, J. Lin, B. Wang, U. Kanniyappan, Z. Li, and Y. Chen, “High-dynamic-range fluorescence laminar optical tomography (HDR-FLOT),” Biomed. Opt. Express 8(4), 2124–2137 (2017).
[Crossref]

Q. Tang, T. Nagaya, Y. Liu, J. Lin, K. Sato, H. Kobayashi, and Y. Chen, “Real-time monitoring of microdistribution of antibody-photon absorber conjugates during photoimmunotherapy in vivo,” J. Controlled Release 260, 154–163 (2017).
[Crossref]

H.-C. Lee, O. O. Ahsen, K. Liang, Z. Wang, M. Figueiredo, M. G. Giacomelli, B. Potsaid, Q. Huang, H. Mashimo, and J. G. Fujimoto, “Endoscopic optical coherence tomography angiography microvascular features associated with dysplasia in Barrett's esophagus (with video),” Gastrointest. Endosc. 86(3), 476–484.e3 (2017).
[Crossref]

C.-L. Chen and R. K. Wang, “Optical coherence tomography based angiography [Invited],” Biomed. Opt. Express 8(2), 1056–1082 (2017).
[Crossref]

A. Shahlaee, B. K. Hong, and A. C. Ho, “Optical coherence tomography angiography features of branch retinal vein occlusion,” Retin Cases Brief Rep. 11(1), 90–93 (2017).
[Crossref]

Y. Maruoka, T. Nagaya, Y. Nakamura, K. Sato, F. Ogata, S. Okuyama, P. L. Choyke, and H. Kobayashi, “Evaluation of early therapeutic effects after near-infrared photoimmunotherapy (NIR-PIT) using luciferase–luciferin photon-counting and fluorescence imaging,” Mol. Pharmaceutics 14(12), 4628–4635 (2017).
[Crossref]

2016 (9)

T. Nagaya, Y. Nakamura, K. Sato, T. Harada, P. L. Choyke, and H. Kobayashi, “Improved micro-distribution of antibody-photon absorber conjugates after initial near infrared photoimmunotherapy (NIR-PIT),” J. Controlled Release 232, 1–8 (2016).
[Crossref]

M. Sakuma, S. Kita, and H. Higuchi, “Quantitative evaluation of malignant gliomas damage induced by photoactivation of IR700 dye,” Sci. Technol. Adv. Mater. 17(1), 473–482 (2016).
[Crossref]

F. D. Maggio, P. Arumugam, F. R. Delvecchio, S. Batista, and H. M. Kocher, “Pancreatic stellate cells regulate blood vessel density in the stroma of pancreatic ductal adenocarcinoma,” Pancreatology 16(6), 995–1004 (2016).
[Crossref]

T. E. de Carlo, M. A. Bonini Filho, C. R. Baumal, E. Reichel, A. Rogers, A. J. Witkin, J. S. Duker, and N. K. Waheed, “Evaluation of preretinal neovascularization in proliferative diabetic retinopathy using optical coherence tomography angiography,” Ophthalmic Surg. Lasers Imaging 47(2), 115–119 (2016).
[Crossref]

A. Ishibazawa, T. Nagaoka, H. Yokota, A. Takahashi, T. Omae, Y.-S. Song, T. Takahashi, and A. Yoshida, “Characteristics of retinal neovascularization in proliferative diabetic retinopathy imaged by optical coherence tomography angiography,” Invest. Ophthalmol. Visual Sci. 57(14), 6247–6255 (2016).
[Crossref]

R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, Z. Liu, D. T. Miller, and J. S. Werner, “A review of adaptive optics optical coherence tomography: technical advances, scientific applications, and the future,” Invest. Ophthalmol. Visual Sci. 57(9), OCT51–OCT68 (2016).
[Crossref]

U. Baran and R. K. Wang, “Review of optical coherence tomography based angiography in neuroscience,” Neurophotonics 3(1), 010902 (2016).
[Crossref]

T. Akagi, Y. Iida, H. Nakanishi, N. Terada, S. Morooka, H. Yamada, T. Hasegawa, S. Yokota, M. Yoshikawa, and N. Yoshimura, “Microvascular density in glaucomatous eyes with hemifield visual field defects: an optical coherence tomography angiography study,” Am. J. Ophthalmol. 168, 237–249 (2016).
[Crossref]

M. Ulrich, L. Themstrup, N. de Carvalho, M. Manfredi, C. Grana, S. Ciardo, R. Kästle, J. Holmes, R. Whitehead, and G. B. Jemec, “Dynamic optical coherence tomography in dermatology,” Dermatology 232(3), 298–311 (2016).
[Crossref]

2015 (9)

A. Zhang, Q. Zhang, C.-L. Chen, and R. K. Wang, “Methods and algorithms for optical coherence tomography-based angiography: a review and comparison,” J. Biomed. Opt. 20(10), 100901 (2015).
[Crossref]

T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
[Crossref]

A. Ishibazawa, T. Nagaoka, A. Takahashi, T. Omae, T. Tani, K. Sogawa, H. Yokota, and A. Yoshida, “Optical Coherence Tomography Angiography in Diabetic Retinopathy: A Prospective Pilot Study,” Am. J. Ophthalmol. 160(1), 35–44.e1 (2015).
[Crossref]

L. Liu, Y. Jia, H. L. Takusagawa, A. D. Pechauer, B. Edmunds, L. Lombardi, E. Davis, J. C. Morrison, and D. Huang, “Optical coherence tomography angiography of the peripapillary retina in glaucoma,” JAMA Ophthalmol. 133(9), 1045–1052 (2015).
[Crossref]

J. Xu, S. Han, C. Balaratnasingam, Z. Mammo, K. S. Wong, S. Lee, M. Cua, M. Young, A. Kirker, and D. Albiani, “Retinal angiography with real-time speckle variance optical coherence tomography,” Br. J. Ophthalmol. 99(10), 1315–1319 (2015).
[Crossref]

Ihler Friedrich, Bertlich Mattis, Weiss Bernhard, Dietzel Steffen, Martin, and Canis, “Two-photon microscopy allows imaging and characterization of cochlear microvasculature in vivo,” BioMed Res. Int. 2015, 1–8 (2015).
[Crossref]

L. Lin, W. Kai, S. Xilin, W. Kezheng, S. Yingying, Z. Guangfeng, and S. Baozhong, “Parameters of dynamic contrast-enhanced MRI as imaging markers for angiogenesis and proliferation in human breast cancer,” Med. Sci. Monit. 21, 376–382 (2015).
[Crossref]

K. Sato, H. Hanaoka, R. Watanabe, T. Nakajima, P. L. Choyke, and H. Kobayashi, “Near infrared photoimmunotherapy in the treatment of disseminated peritoneal ovarian cancer,” Mol. Cancer Ther. 14(1), 141–150 (2015).
[Crossref]

T. Nagaya, K. Sato, T. Harada, Y. Nakamura, P. L. Choyke, and H. Kobayashi, “Near infrared photoimmunotherapy targeting EGFR positive triple negative breast cancer: optimizing the conjugate-light regimen,” PLoS One 10(8), e0136829 (2015).
[Crossref]

2014 (3)

W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
[Crossref]

C.-P. Liang, T. Nakajima, R. Watanabe, K. Sato, P. L. Choyke, Y. Chen, and H. Kobayashi, “Real-time monitoring of hemodynamic changes in tumor vessels during photoimmunotherapy using optical coherence tomography,” J. Biomed. Opt. 19(9), 098004 (2014).
[Crossref]

Y. Huang, Q. Zhang, M. R. Thorell, L. An, M. K. Durbin, M. Laron, U. Sharma, G. Gregori, P. J. Rosenfeld, and R. K. Wang, “Swept-source OCT angiography of the retinal vasculature using intensity differentiation-based optical microangiography algorithms,” Ophthalmic Surg. Lasers Imaging Retina 45(5), 382–389 (2014).
[Crossref]

2013 (2)

M. S. Mahmud, D. W. Cadotte, B. Vuong, C. Sun, T. W. H. Luk, A. Mariampillai, and V. X. D. Yang, “Review of speckle and phase variance optical coherence tomography to visualize microvascular networks,” J. Biomed. Opt. 18(5), 050901 (2013).
[Crossref]

K. Sano, T. Nakajima, P. L. Choyke, and H. Kobayashi, “Markedly enhanced permeability and retention effects induced by photo-immunotherapy of tumors,” ACS Nano 7(1), 717–724 (2013).
[Crossref]

2012 (5)

2011 (5)

M. Mitsunaga, M. Ogawa, N. Kosaka, L. T. Rosenblum, P. L. Choyke, and H. Kobayashi, “Cancer cell-selective in vivo near infrared photoimmunotherapy targeting specific membrane molecules,” Nat. Med. 17(12), 1685–1691 (2011).
[Crossref]

J. Enfield, E. Jonathan, and M. Leahy, “In vivo imaging of the microcirculation of the volar forearm using correlation mapping optical coherence tomography (cmOCT),” Biomed. Opt. Express 2(5), 1184–1193 (2011).
[Crossref]

J. Qin, J. Jiang, L. An, D. Gareau, and R. K. Wang, “In vivo volumetric imaging of microcirculation within human skin under psoriatic conditions using optical microangiography,” Lasers Surg. Med. 43(2), 122–129 (2011).
[Crossref]

D. Y. Kim, J. Fingler, J. S. Werner, D. M. Schwartz, S. E. Fraser, and R. J. Zawadzki, “In vivo volumetric imaging of human retinal circulation with phase-variance optical coherence tomography,” Biomed. Opt. Express 2(6), 1504–1513 (2011).
[Crossref]

N. Sudheendran, S. Syed, M. Dickinson, I. Larina, and K. Larin, “Speckle variance OCT imaging of the vasculature in live mammalian embryos,” Laser Phys. Lett. 8(3), 247–252 (2011).
[Crossref]

2010 (2)

A. Mariampillai, M. K. K. Leung, M. Jarvi, B. A. Standish, K. Lee, B. C. Wilson, A. Vitkin, and V. X. D. Yang, “Optimized speckle variance OCT imaging of microvasculature,” Opt. Lett. 35(8), 1257–1259 (2010).
[Crossref]

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2009 (1)

2008 (1)

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T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
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J. Xu, S. Han, C. Balaratnasingam, Z. Mammo, K. S. Wong, S. Lee, M. Cua, M. Young, A. Kirker, and D. Albiani, “Retinal angiography with real-time speckle variance optical coherence tomography,” Br. J. Ophthalmol. 99(10), 1315–1319 (2015).
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U. Baran and R. K. Wang, “Review of optical coherence tomography based angiography in neuroscience,” Neurophotonics 3(1), 010902 (2016).
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F. D. Maggio, P. Arumugam, F. R. Delvecchio, S. Batista, and H. M. Kocher, “Pancreatic stellate cells regulate blood vessel density in the stroma of pancreatic ductal adenocarcinoma,” Pancreatology 16(6), 995–1004 (2016).
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T. E. de Carlo, M. A. Bonini Filho, C. R. Baumal, E. Reichel, A. Rogers, A. J. Witkin, J. S. Duker, and N. K. Waheed, “Evaluation of preretinal neovascularization in proliferative diabetic retinopathy using optical coherence tomography angiography,” Ophthalmic Surg. Lasers Imaging 47(2), 115–119 (2016).
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C. Joo Hee, M. Woo Kyung, C. Nariya, C. S. Yang, P. Seong Ho, P. Jeong Mi, H. Boo Kyung, C. Yeon Hyun, C. Gyunggoo, and I. Jung-Gi, “Differentiation of benign from malignant solid breast masses: conventional US versus spatial compound imaging,” Radiology 237(3), 841–846 (2005).
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Burg, M. C.

P. Nelis, I. Kleffner, M. C. Burg, C. R. Clemens, M. Alnawaiseh, J. Motte, M. Marziniak, N. Eter, and F. Alten, “OCT-Angiography reveals reduced vessel density in the deep retinal plexus of CADASIL patients,” Sci. Rep. 8(1), 8148 (2018).
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Cadotte, A.

Cadotte, D. W.

Canis,

Ihler Friedrich, Bertlich Mattis, Weiss Bernhard, Dietzel Steffen, Martin, and Canis, “Two-photon microscopy allows imaging and characterization of cochlear microvasculature in vivo,” BioMed Res. Int. 2015, 1–8 (2015).
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Cernat, R.

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D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
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C.-L. Chen and R. K. Wang, “Optical coherence tomography based angiography [Invited],” Biomed. Opt. Express 8(2), 1056–1082 (2017).
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D. Chen, W. Yuan, H.-C. Park, and X. Li, “In vivo assessment of vascular-targeted photodynamic therapy effects on tumor microvasculature using ultrahigh-resolution functional optical coherence tomography,” Biomed. Opt. Express 11(8), 4316–4325 (2020).
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J. Pan, D. Chen, X. Yang, R. Zou, K. Zhao, D. Cheng, S. Huang, T. Zhou, Y. Yang, and F. Chen, “Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography,” Am. J. Ophthalmol. 192, 146–156 (2018).
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J. Pan, D. Chen, X. Yang, R. Zou, K. Zhao, D. Cheng, S. Huang, T. Zhou, Y. Yang, and F. Chen, “Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography,” Am. J. Ophthalmol. 192, 146–156 (2018).
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S. Wu, H. Guo, H. Horng, Y. Liu, H. Li, P. Daneshpajouhnejad, A. Rosenberg, C. Albanese, S. Ranjit, P. M. Andrews, M. Levi, Q. Tang, and Y. Chen, “Morphological and functional characteristics of aging kidneys based on two-photon microscopy in vivo,” J. Biophotonics 13, e201900246 (2020).
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H. Guo, H.-W. Wang, Q. Tang, E. Anderson, R. Falola, T. Smith, Y. Liu, M. Levi, P. M. Andrews, and Y. Chen, “Intravital imaging of adriamycin-induced renal pathology using two-photon microscopy and optical coherence tomography,” J. Innovative Opt. Health Sci. 11(05), 1850030 (2018).
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J. Pan, D. Chen, X. Yang, R. Zou, K. Zhao, D. Cheng, S. Huang, T. Zhou, Y. Yang, and F. Chen, “Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography,” Am. J. Ophthalmol. 192, 146–156 (2018).
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C.-P. Liang, T. Nakajima, R. Watanabe, K. Sato, P. L. Choyke, Y. Chen, and H. Kobayashi, “Real-time monitoring of hemodynamic changes in tumor vessels during photoimmunotherapy using optical coherence tomography,” J. Biomed. Opt. 19(9), 098004 (2014).
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K. Sano, T. Nakajima, P. L. Choyke, and H. Kobayashi, “Markedly enhanced permeability and retention effects induced by photo-immunotherapy of tumors,” ACS Nano 7(1), 717–724 (2013).
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M. Mitsunaga, T. Nakajima, K. Sano, P. L. Choyke, and H. Kobayashi, “Near-infrared theranostic photoimmunotherapy (PIT): repeated exposure of light enhances the effect of immunoconjugate,” Bioconjugate Chem. 23(3), 604–609 (2012).
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M. Makoto, N. Takahito, S. Kohei, K. M. Gabriela, P. L. Choyke, and K. Hisataka, “Immediatein vivotarget-specific cancer cell death after near infrared photoimmunotherapy,” BMC Cancer 12(1), 345 (2012).
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Q. Zhang, K. A. Rezaei, S. S. Saraf, Z. Chu, F. Wang, and R. K. Wang, “Ultra-wide optical coherence tomography angiography in diabetic retinopathy,” Quant. Imaging Med. Surg. 8(8), 743–753 (2018).
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P. Nelis, I. Kleffner, M. C. Burg, C. R. Clemens, M. Alnawaiseh, J. Motte, M. Marziniak, N. Eter, and F. Alten, “OCT-Angiography reveals reduced vessel density in the deep retinal plexus of CADASIL patients,” Sci. Rep. 8(1), 8148 (2018).
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J. Xu, S. Han, C. Balaratnasingam, Z. Mammo, K. S. Wong, S. Lee, M. Cua, M. Young, A. Kirker, and D. Albiani, “Retinal angiography with real-time speckle variance optical coherence tomography,” Br. J. Ophthalmol. 99(10), 1315–1319 (2015).
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S. Wu, H. Guo, H. Horng, Y. Liu, H. Li, P. Daneshpajouhnejad, A. Rosenberg, C. Albanese, S. Ranjit, P. M. Andrews, M. Levi, Q. Tang, and Y. Chen, “Morphological and functional characteristics of aging kidneys based on two-photon microscopy in vivo,” J. Biophotonics 13, e201900246 (2020).
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Davis, E.

L. Liu, Y. Jia, H. L. Takusagawa, A. D. Pechauer, B. Edmunds, L. Lombardi, E. Davis, J. C. Morrison, and D. Huang, “Optical coherence tomography angiography of the peripapillary retina in glaucoma,” JAMA Ophthalmol. 133(9), 1045–1052 (2015).
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de Carlo, T. E.

T. E. de Carlo, M. A. Bonini Filho, C. R. Baumal, E. Reichel, A. Rogers, A. J. Witkin, J. S. Duker, and N. K. Waheed, “Evaluation of preretinal neovascularization in proliferative diabetic retinopathy using optical coherence tomography angiography,” Ophthalmic Surg. Lasers Imaging 47(2), 115–119 (2016).
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M. Ulrich, L. Themstrup, N. de Carvalho, M. Manfredi, C. Grana, S. Ciardo, R. Kästle, J. Holmes, R. Whitehead, and G. B. Jemec, “Dynamic optical coherence tomography in dermatology,” Dermatology 232(3), 298–311 (2016).
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F. D. Maggio, P. Arumugam, F. R. Delvecchio, S. Batista, and H. M. Kocher, “Pancreatic stellate cells regulate blood vessel density in the stroma of pancreatic ductal adenocarcinoma,” Pancreatology 16(6), 995–1004 (2016).
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T. E. de Carlo, M. A. Bonini Filho, C. R. Baumal, E. Reichel, A. Rogers, A. J. Witkin, J. S. Duker, and N. K. Waheed, “Evaluation of preretinal neovascularization in proliferative diabetic retinopathy using optical coherence tomography angiography,” Ophthalmic Surg. Lasers Imaging 47(2), 115–119 (2016).
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Y. Huang, Q. Zhang, M. R. Thorell, L. An, M. K. Durbin, M. Laron, U. Sharma, G. Gregori, P. J. Rosenfeld, and R. K. Wang, “Swept-source OCT angiography of the retinal vasculature using intensity differentiation-based optical microangiography algorithms,” Ophthalmic Surg. Lasers Imaging Retina 45(5), 382–389 (2014).
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Edmunds, B.

L. Liu, Y. Jia, H. L. Takusagawa, A. D. Pechauer, B. Edmunds, L. Lombardi, E. Davis, J. C. Morrison, and D. Huang, “Optical coherence tomography angiography of the peripapillary retina in glaucoma,” JAMA Ophthalmol. 133(9), 1045–1052 (2015).
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Enfield, J.

Ensher, J.

L. M. Wurster, R. N. Shah, F. Placzek, S. Kretschmer, M. Niederleithner, L. Ginner, J. Ensher, M. P. Minneman, E. E. Hoover, H. Zappe, W. Drexler, R. A. Leitgeb, and Ç. Ataman, “Endoscopic optical coherence tomography angiography using a forward imaging piezo scanner probe,” J. Biophotonics 12(4), e201800382 (2019).
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Eter, N.

P. Nelis, I. Kleffner, M. C. Burg, C. R. Clemens, M. Alnawaiseh, J. Motte, M. Marziniak, N. Eter, and F. Alten, “OCT-Angiography reveals reduced vessel density in the deep retinal plexus of CADASIL patients,” Sci. Rep. 8(1), 8148 (2018).
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Falola, R.

H. Guo, H.-W. Wang, Q. Tang, E. Anderson, R. Falola, T. Smith, Y. Liu, M. Levi, P. M. Andrews, and Y. Chen, “Intravital imaging of adriamycin-induced renal pathology using two-photon microscopy and optical coherence tomography,” J. Innovative Opt. Health Sci. 11(05), 1850030 (2018).
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Feuchter, T.

Figueiredo, M.

H.-C. Lee, O. O. Ahsen, K. Liang, Z. Wang, M. Figueiredo, M. G. Giacomelli, B. Potsaid, Q. Huang, H. Mashimo, and J. G. Fujimoto, “Endoscopic optical coherence tomography angiography microvascular features associated with dysplasia in Barrett's esophagus (with video),” Gastrointest. Endosc. 86(3), 476–484.e3 (2017).
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Fingler, J.

Flaxel, C. J.

T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
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Flotte, T.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
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Fraser, S. E.

Friedrich, Ihler

Ihler Friedrich, Bertlich Mattis, Weiss Bernhard, Dietzel Steffen, Martin, and Canis, “Two-photon microscopy allows imaging and characterization of cochlear microvasculature in vivo,” BioMed Res. Int. 2015, 1–8 (2015).
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H.-C. Lee, O. O. Ahsen, K. Liang, Z. Wang, M. Figueiredo, M. G. Giacomelli, B. Potsaid, Q. Huang, H. Mashimo, and J. G. Fujimoto, “Endoscopic optical coherence tomography angiography microvascular features associated with dysplasia in Barrett's esophagus (with video),” Gastrointest. Endosc. 86(3), 476–484.e3 (2017).
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J. Xu, S. Han, C. Balaratnasingam, Z. Mammo, K. S. Wong, S. Lee, M. Cua, M. Young, A. Kirker, and D. Albiani, “Retinal angiography with real-time speckle variance optical coherence tomography,” Br. J. Ophthalmol. 99(10), 1315–1319 (2015).
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T. Akagi, Y. Iida, H. Nakanishi, N. Terada, S. Morooka, H. Yamada, T. Hasegawa, S. Yokota, M. Yoshikawa, and N. Yoshimura, “Microvascular density in glaucomatous eyes with hemifield visual field defects: an optical coherence tomography angiography study,” Am. J. Ophthalmol. 168, 237–249 (2016).
[Crossref]

Yamauchi, K.

K. Yamauchi, M. Yang, and P. Jiang, “Real-time in vivo dual-color imaging of intracapillary cancer cell and nucleus deformation and migration,” Cancer Res. 65(10), 4246–4252 (2005).
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C. Joo Hee, M. Woo Kyung, C. Nariya, C. S. Yang, P. Seong Ho, P. Jeong Mi, H. Boo Kyung, C. Yeon Hyun, C. Gyunggoo, and I. Jung-Gi, “Differentiation of benign from malignant solid breast masses: conventional US versus spatial compound imaging,” Radiology 237(3), 841–846 (2005).
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M. Yang, P. Jiang, and R. M. Hoffman, “Whole-body subcellular multicolor imaging of tumor-host interaction and drug response in real time,” Cancer Res. 67(11), 5195–5200 (2007).
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K. Yamauchi, M. Yang, and P. Jiang, “Real-time in vivo dual-color imaging of intracapillary cancer cell and nucleus deformation and migration,” Cancer Res. 65(10), 4246–4252 (2005).
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Yang, V. X. D.

Yang, X.

J. Pan, D. Chen, X. Yang, R. Zou, K. Zhao, D. Cheng, S. Huang, T. Zhou, Y. Yang, and F. Chen, “Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography,” Am. J. Ophthalmol. 192, 146–156 (2018).
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J. Pan, D. Chen, X. Yang, R. Zou, K. Zhao, D. Cheng, S. Huang, T. Zhou, Y. Yang, and F. Chen, “Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography,” Am. J. Ophthalmol. 192, 146–156 (2018).
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Yeon Hyun, C.

C. Joo Hee, M. Woo Kyung, C. Nariya, C. S. Yang, P. Seong Ho, P. Jeong Mi, H. Boo Kyung, C. Yeon Hyun, C. Gyunggoo, and I. Jung-Gi, “Differentiation of benign from malignant solid breast masses: conventional US versus spatial compound imaging,” Radiology 237(3), 841–846 (2005).
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L. Lin, W. Kai, S. Xilin, W. Kezheng, S. Yingying, Z. Guangfeng, and S. Baozhong, “Parameters of dynamic contrast-enhanced MRI as imaging markers for angiogenesis and proliferation in human breast cancer,” Med. Sci. Monit. 21, 376–382 (2015).
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A. Ishibazawa, T. Nagaoka, H. Yokota, A. Takahashi, T. Omae, Y.-S. Song, T. Takahashi, and A. Yoshida, “Characteristics of retinal neovascularization in proliferative diabetic retinopathy imaged by optical coherence tomography angiography,” Invest. Ophthalmol. Visual Sci. 57(14), 6247–6255 (2016).
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A. Ishibazawa, T. Nagaoka, A. Takahashi, T. Omae, T. Tani, K. Sogawa, H. Yokota, and A. Yoshida, “Optical Coherence Tomography Angiography in Diabetic Retinopathy: A Prospective Pilot Study,” Am. J. Ophthalmol. 160(1), 35–44.e1 (2015).
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T. Akagi, Y. Iida, H. Nakanishi, N. Terada, S. Morooka, H. Yamada, T. Hasegawa, S. Yokota, M. Yoshikawa, and N. Yoshimura, “Microvascular density in glaucomatous eyes with hemifield visual field defects: an optical coherence tomography angiography study,” Am. J. Ophthalmol. 168, 237–249 (2016).
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A. Ishibazawa, T. Nagaoka, H. Yokota, A. Takahashi, T. Omae, Y.-S. Song, T. Takahashi, and A. Yoshida, “Characteristics of retinal neovascularization in proliferative diabetic retinopathy imaged by optical coherence tomography angiography,” Invest. Ophthalmol. Visual Sci. 57(14), 6247–6255 (2016).
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A. Ishibazawa, T. Nagaoka, A. Takahashi, T. Omae, T. Tani, K. Sogawa, H. Yokota, and A. Yoshida, “Optical Coherence Tomography Angiography in Diabetic Retinopathy: A Prospective Pilot Study,” Am. J. Ophthalmol. 160(1), 35–44.e1 (2015).
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T. Akagi, Y. Iida, H. Nakanishi, N. Terada, S. Morooka, H. Yamada, T. Hasegawa, S. Yokota, M. Yoshikawa, and N. Yoshimura, “Microvascular density in glaucomatous eyes with hemifield visual field defects: an optical coherence tomography angiography study,” Am. J. Ophthalmol. 168, 237–249 (2016).
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T. Akagi, Y. Iida, H. Nakanishi, N. Terada, S. Morooka, H. Yamada, T. Hasegawa, S. Yokota, M. Yoshikawa, and N. Yoshimura, “Microvascular density in glaucomatous eyes with hemifield visual field defects: an optical coherence tomography angiography study,” Am. J. Ophthalmol. 168, 237–249 (2016).
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J. Xu, S. Han, C. Balaratnasingam, Z. Mammo, K. S. Wong, S. Lee, M. Cua, M. Young, A. Kirker, and D. Albiani, “Retinal angiography with real-time speckle variance optical coherence tomography,” Br. J. Ophthalmol. 99(10), 1315–1319 (2015).
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D. Y. Kim, J. Fingler, J. S. Werner, D. M. Schwartz, S. E. Fraser, and R. J. Zawadzki, “In vivo volumetric imaging of human retinal circulation with phase-variance optical coherence tomography,” Biomed. Opt. Express 2(6), 1504–1513 (2011).
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A. Zhang, Q. Zhang, C.-L. Chen, and R. K. Wang, “Methods and algorithms for optical coherence tomography-based angiography: a review and comparison,” J. Biomed. Opt. 20(10), 100901 (2015).
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Q. Zhang, K. A. Rezaei, S. S. Saraf, Z. Chu, F. Wang, and R. K. Wang, “Ultra-wide optical coherence tomography angiography in diabetic retinopathy,” Quant. Imaging Med. Surg. 8(8), 743–753 (2018).
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A. Zhang, Q. Zhang, C.-L. Chen, and R. K. Wang, “Methods and algorithms for optical coherence tomography-based angiography: a review and comparison,” J. Biomed. Opt. 20(10), 100901 (2015).
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Y. Huang, Q. Zhang, M. R. Thorell, L. An, M. K. Durbin, M. Laron, U. Sharma, G. Gregori, P. J. Rosenfeld, and R. K. Wang, “Swept-source OCT angiography of the retinal vasculature using intensity differentiation-based optical microangiography algorithms,” Ophthalmic Surg. Lasers Imaging Retina 45(5), 382–389 (2014).
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Zhao, K.

J. Pan, D. Chen, X. Yang, R. Zou, K. Zhao, D. Cheng, S. Huang, T. Zhou, Y. Yang, and F. Chen, “Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography,” Am. J. Ophthalmol. 192, 146–156 (2018).
[Crossref]

Zhou, T.

J. Pan, D. Chen, X. Yang, R. Zou, K. Zhao, D. Cheng, S. Huang, T. Zhou, Y. Yang, and F. Chen, “Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography,” Am. J. Ophthalmol. 192, 146–156 (2018).
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K. B. Schaal, M. R. Munk, I. Wyssmueller, L. E. Berger, M. S. Zinkernagel, and S. Wolf, “Vascular abnormalities in diabetic retinopathy assessed with swept-source optical coherence tomography angiography widefield imaging,” Retina 39(1), 79–87 (2019).
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J. Pan, D. Chen, X. Yang, R. Zou, K. Zhao, D. Cheng, S. Huang, T. Zhou, Y. Yang, and F. Chen, “Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography,” Am. J. Ophthalmol. 192, 146–156 (2018).
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Acc. Chem. Res. (1)

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ACS Nano (1)

K. Sano, T. Nakajima, P. L. Choyke, and H. Kobayashi, “Markedly enhanced permeability and retention effects induced by photo-immunotherapy of tumors,” ACS Nano 7(1), 717–724 (2013).
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Am. J. Ophthalmol. (3)

T. Akagi, Y. Iida, H. Nakanishi, N. Terada, S. Morooka, H. Yamada, T. Hasegawa, S. Yokota, M. Yoshikawa, and N. Yoshimura, “Microvascular density in glaucomatous eyes with hemifield visual field defects: an optical coherence tomography angiography study,” Am. J. Ophthalmol. 168, 237–249 (2016).
[Crossref]

A. Ishibazawa, T. Nagaoka, A. Takahashi, T. Omae, T. Tani, K. Sogawa, H. Yokota, and A. Yoshida, “Optical Coherence Tomography Angiography in Diabetic Retinopathy: A Prospective Pilot Study,” Am. J. Ophthalmol. 160(1), 35–44.e1 (2015).
[Crossref]

J. Pan, D. Chen, X. Yang, R. Zou, K. Zhao, D. Cheng, S. Huang, T. Zhou, Y. Yang, and F. Chen, “Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography,” Am. J. Ophthalmol. 192, 146–156 (2018).
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Bioconjugate Chem. (1)

M. Mitsunaga, T. Nakajima, K. Sano, P. L. Choyke, and H. Kobayashi, “Near-infrared theranostic photoimmunotherapy (PIT): repeated exposure of light enhances the effect of immunoconjugate,” Bioconjugate Chem. 23(3), 604–609 (2012).
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Biol. Blood Marrow Transplant. (1)

K. Kawamura, Y. Tanaka, H. Nakasone, Y. Ishihara, and Y. Kanda, “Development of a unique T cell receptor gene-transferred tax-redirected T cell immunotherapy for adult T cell leukemia,” Biol. Blood Marrow Transplant. 26(8), 1377–1385 (2020).
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BioMed Res. Int. (1)

Ihler Friedrich, Bertlich Mattis, Weiss Bernhard, Dietzel Steffen, Martin, and Canis, “Two-photon microscopy allows imaging and characterization of cochlear microvasculature in vivo,” BioMed Res. Int. 2015, 1–8 (2015).
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Biomed. Opt. Express (8)

C.-L. Chen and R. K. Wang, “Optical coherence tomography based angiography [Invited],” Biomed. Opt. Express 8(2), 1056–1082 (2017).
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D. Chen, W. Yuan, H.-C. Park, and X. Li, “In vivo assessment of vascular-targeted photodynamic therapy effects on tumor microvasculature using ultrahigh-resolution functional optical coherence tomography,” Biomed. Opt. Express 11(8), 4316–4325 (2020).
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S. Caujolle, R. Cernat, G. Silvestri, M. Marques, A. Bradu, T. Feuchter, G. Robinson, D. K. Griffin, and A. Podoleanu, “Speckle variance OCT for depth resolved assessment of the viability of bovine embryos,” Biomed. Opt. Express 8(11), 5139–5150 (2017).
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Q. Tang, Y. Liu, V. Tsytsarev, J. Lin, B. Wang, U. Kanniyappan, Z. Li, and Y. Chen, “High-dynamic-range fluorescence laminar optical tomography (HDR-FLOT),” Biomed. Opt. Express 8(4), 2124–2137 (2017).
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K. K. Lee, A. Mariampillai, X. Joe, D. W. Cadotte, B. C. Wilson, B. A. Standish, and V. X. Yang, “Real-time speckle variance swept-source optical coherence tomography using a graphics processing unit,” Biomed. Opt. Express 3(7), 1557–1564 (2012).
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J. Enfield, E. Jonathan, and M. Leahy, “In vivo imaging of the microcirculation of the volar forearm using correlation mapping optical coherence tomography (cmOCT),” Biomed. Opt. Express 2(5), 1184–1193 (2011).
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D. Y. Kim, J. Fingler, J. S. Werner, D. M. Schwartz, S. E. Fraser, and R. J. Zawadzki, “In vivo volumetric imaging of human retinal circulation with phase-variance optical coherence tomography,” Biomed. Opt. Express 2(6), 1504–1513 (2011).
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BMC Cancer (1)

M. Makoto, N. Takahito, S. Kohei, K. M. Gabriela, P. L. Choyke, and K. Hisataka, “Immediatein vivotarget-specific cancer cell death after near infrared photoimmunotherapy,” BMC Cancer 12(1), 345 (2012).
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Br. J. Ophthalmol. (1)

J. Xu, S. Han, C. Balaratnasingam, Z. Mammo, K. S. Wong, S. Lee, M. Cua, M. Young, A. Kirker, and D. Albiani, “Retinal angiography with real-time speckle variance optical coherence tomography,” Br. J. Ophthalmol. 99(10), 1315–1319 (2015).
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Cancer Res. (2)

K. Yamauchi, M. Yang, and P. Jiang, “Real-time in vivo dual-color imaging of intracapillary cancer cell and nucleus deformation and migration,” Cancer Res. 65(10), 4246–4252 (2005).
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M. Yang, P. Jiang, and R. M. Hoffman, “Whole-body subcellular multicolor imaging of tumor-host interaction and drug response in real time,” Cancer Res. 67(11), 5195–5200 (2007).
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Dermatology (1)

M. Ulrich, L. Themstrup, N. de Carvalho, M. Manfredi, C. Grana, S. Ciardo, R. Kästle, J. Holmes, R. Whitehead, and G. B. Jemec, “Dynamic optical coherence tomography in dermatology,” Dermatology 232(3), 298–311 (2016).
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Diabetes Res. Clin. Pract. (1)

G. Liu, D. Xu, and F. Wang, “New insights into diabetic retinopathy by OCT angiography,” Diabetes Res. Clin. Pract. 142, 243–253 (2018).
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Gastrointest. Endosc. (1)

H.-C. Lee, O. O. Ahsen, K. Liang, Z. Wang, M. Figueiredo, M. G. Giacomelli, B. Potsaid, Q. Huang, H. Mashimo, and J. G. Fujimoto, “Endoscopic optical coherence tomography angiography microvascular features associated with dysplasia in Barrett's esophagus (with video),” Gastrointest. Endosc. 86(3), 476–484.e3 (2017).
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Int. Immunol. (1)

H. Kobayashi, A. Furusawa, A. Rosenberg, and P. L. Choyke, “Near-infrared photoimmunotherapy of cancer: a new approach that kills cancer cells and enhances anti-cancer host immunity,” Int. Immunol. 33(1), 7–15 (2021).
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Invest. Ophthalmol. Visual Sci. (2)

A. Ishibazawa, T. Nagaoka, H. Yokota, A. Takahashi, T. Omae, Y.-S. Song, T. Takahashi, and A. Yoshida, “Characteristics of retinal neovascularization in proliferative diabetic retinopathy imaged by optical coherence tomography angiography,” Invest. Ophthalmol. Visual Sci. 57(14), 6247–6255 (2016).
[Crossref]

R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, Z. Liu, D. T. Miller, and J. S. Werner, “A review of adaptive optics optical coherence tomography: technical advances, scientific applications, and the future,” Invest. Ophthalmol. Visual Sci. 57(9), OCT51–OCT68 (2016).
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J. Biomed. Opt. (5)

W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
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M. S. Mahmud, D. W. Cadotte, B. Vuong, C. Sun, T. W. H. Luk, A. Mariampillai, and V. X. D. Yang, “Review of speckle and phase variance optical coherence tomography to visualize microvascular networks,” J. Biomed. Opt. 18(5), 050901 (2013).
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A. Zhang, Q. Zhang, C.-L. Chen, and R. K. Wang, “Methods and algorithms for optical coherence tomography-based angiography: a review and comparison,” J. Biomed. Opt. 20(10), 100901 (2015).
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L. An, H. M. Subhush, D. J. Wilson, and R. K. Wang, “High-resolution wide-field imaging of retinal and choroidal blood perfusion with optical microangiography,” J. Biomed. Opt. 15(2), 026011 (2010).
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C.-P. Liang, T. Nakajima, R. Watanabe, K. Sato, P. L. Choyke, Y. Chen, and H. Kobayashi, “Real-time monitoring of hemodynamic changes in tumor vessels during photoimmunotherapy using optical coherence tomography,” J. Biomed. Opt. 19(9), 098004 (2014).
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J. Biophotonics (3)

O. A. Grishina, S. Wang, and I. V. Larina, “Speckle variance optical coherence tomography of blood flow in the beating mouse embryonic heart,” J. Biophotonics 10(5), 735–743 (2017).
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S. Wu, H. Guo, H. Horng, Y. Liu, H. Li, P. Daneshpajouhnejad, A. Rosenberg, C. Albanese, S. Ranjit, P. M. Andrews, M. Levi, Q. Tang, and Y. Chen, “Morphological and functional characteristics of aging kidneys based on two-photon microscopy in vivo,” J. Biophotonics 13, e201900246 (2020).
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L. M. Wurster, R. N. Shah, F. Placzek, S. Kretschmer, M. Niederleithner, L. Ginner, J. Ensher, M. P. Minneman, E. E. Hoover, H. Zappe, W. Drexler, R. A. Leitgeb, and Ç. Ataman, “Endoscopic optical coherence tomography angiography using a forward imaging piezo scanner probe,” J. Biophotonics 12(4), e201800382 (2019).
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J. Clin. Oncol. (1)

A. Nimgaonkar, O. Segurado, W.-S. Tsai, S.-T. Pang, and R. Mei, “A novel circulating tumor cell blood test for early detection of colorectal, prostate, and breast cancers: Results from 709 samples,” J. Clin. Oncol. 36(15_suppl), e13549 (2018).
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J. Controlled Release (3)

T. Nagaya, Y. Nakamura, K. Sato, T. Harada, P. L. Choyke, and H. Kobayashi, “Improved micro-distribution of antibody-photon absorber conjugates after initial near infrared photoimmunotherapy (NIR-PIT),” J. Controlled Release 232, 1–8 (2016).
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Q. Tang, T. Nagaya, Y. Liu, H. Horng, J. Lin, K. Sato, H. Kobayashi, and Y. Chen, “3D mesoscopic fluorescence tomography for imaging micro-distribution of antibody-photon absorber conjugates during near infrared photoimmunotherapy in vivo,” J. Controlled Release 279, 171–180 (2018).
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Q. Tang, T. Nagaya, Y. Liu, J. Lin, K. Sato, H. Kobayashi, and Y. Chen, “Real-time monitoring of microdistribution of antibody-photon absorber conjugates during photoimmunotherapy in vivo,” J. Controlled Release 260, 154–163 (2017).
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J. Innovative Opt. Health Sci. (1)

H. Guo, H.-W. Wang, Q. Tang, E. Anderson, R. Falola, T. Smith, Y. Liu, M. Levi, P. M. Andrews, and Y. Chen, “Intravital imaging of adriamycin-induced renal pathology using two-photon microscopy and optical coherence tomography,” J. Innovative Opt. Health Sci. 11(05), 1850030 (2018).
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J. Phys. D: Appl. Phys. (1)

P. H. Tomlins and R. K. Wang, “Theory, developments and applications of optical coherence tomography,” J. Phys. D: Appl. Phys. 38(15), 2519–2535 (2005).
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JAMA Ophthalmol. (1)

L. Liu, Y. Jia, H. L. Takusagawa, A. D. Pechauer, B. Edmunds, L. Lombardi, E. Davis, J. C. Morrison, and D. Huang, “Optical coherence tomography angiography of the peripapillary retina in glaucoma,” JAMA Ophthalmol. 133(9), 1045–1052 (2015).
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Laser Phys. Lett. (1)

N. Sudheendran, S. Syed, M. Dickinson, I. Larina, and K. Larin, “Speckle variance OCT imaging of the vasculature in live mammalian embryos,” Laser Phys. Lett. 8(3), 247–252 (2011).
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Lasers Surg. Med. (1)

J. Qin, J. Jiang, L. An, D. Gareau, and R. K. Wang, “In vivo volumetric imaging of microcirculation within human skin under psoriatic conditions using optical microangiography,” Lasers Surg. Med. 43(2), 122–129 (2011).
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Med. Sci. Monit. (1)

L. Lin, W. Kai, S. Xilin, W. Kezheng, S. Yingying, Z. Guangfeng, and S. Baozhong, “Parameters of dynamic contrast-enhanced MRI as imaging markers for angiogenesis and proliferation in human breast cancer,” Med. Sci. Monit. 21, 376–382 (2015).
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Mol. Cancer Ther. (1)

K. Sato, H. Hanaoka, R. Watanabe, T. Nakajima, P. L. Choyke, and H. Kobayashi, “Near infrared photoimmunotherapy in the treatment of disseminated peritoneal ovarian cancer,” Mol. Cancer Ther. 14(1), 141–150 (2015).
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Mol. Pharmaceutics (1)

Y. Maruoka, T. Nagaya, Y. Nakamura, K. Sato, F. Ogata, S. Okuyama, P. L. Choyke, and H. Kobayashi, “Evaluation of early therapeutic effects after near-infrared photoimmunotherapy (NIR-PIT) using luciferase–luciferin photon-counting and fluorescence imaging,” Mol. Pharmaceutics 14(12), 4628–4635 (2017).
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Nat. Med. (1)

M. Mitsunaga, M. Ogawa, N. Kosaka, L. T. Rosenblum, P. L. Choyke, and H. Kobayashi, “Cancer cell-selective in vivo near infrared photoimmunotherapy targeting specific membrane molecules,” Nat. Med. 17(12), 1685–1691 (2011).
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Neurophotonics (1)

U. Baran and R. K. Wang, “Review of optical coherence tomography based angiography in neuroscience,” Neurophotonics 3(1), 010902 (2016).
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Ophthalmic Surg. Lasers Imaging (1)

T. E. de Carlo, M. A. Bonini Filho, C. R. Baumal, E. Reichel, A. Rogers, A. J. Witkin, J. S. Duker, and N. K. Waheed, “Evaluation of preretinal neovascularization in proliferative diabetic retinopathy using optical coherence tomography angiography,” Ophthalmic Surg. Lasers Imaging 47(2), 115–119 (2016).
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Ophthalmic Surg. Lasers Imaging Retina (1)

Y. Huang, Q. Zhang, M. R. Thorell, L. An, M. K. Durbin, M. Laron, U. Sharma, G. Gregori, P. J. Rosenfeld, and R. K. Wang, “Swept-source OCT angiography of the retinal vasculature using intensity differentiation-based optical microangiography algorithms,” Ophthalmic Surg. Lasers Imaging Retina 45(5), 382–389 (2014).
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Ophthalmol. Retina (1)

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Opt. Express (3)

Opt. Lett. (2)

Pancreatology (1)

F. D. Maggio, P. Arumugam, F. R. Delvecchio, S. Batista, and H. M. Kocher, “Pancreatic stellate cells regulate blood vessel density in the stroma of pancreatic ductal adenocarcinoma,” Pancreatology 16(6), 995–1004 (2016).
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PLoS One (1)

T. Nagaya, K. Sato, T. Harada, Y. Nakamura, P. L. Choyke, and H. Kobayashi, “Near infrared photoimmunotherapy targeting EGFR positive triple negative breast cancer: optimizing the conjugate-light regimen,” PLoS One 10(8), e0136829 (2015).
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Quant. Imaging Med. Surg. (1)

Q. Zhang, K. A. Rezaei, S. S. Saraf, Z. Chu, F. Wang, and R. K. Wang, “Ultra-wide optical coherence tomography angiography in diabetic retinopathy,” Quant. Imaging Med. Surg. 8(8), 743–753 (2018).
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Radiology (1)

C. Joo Hee, M. Woo Kyung, C. Nariya, C. S. Yang, P. Seong Ho, P. Jeong Mi, H. Boo Kyung, C. Yeon Hyun, C. Gyunggoo, and I. Jung-Gi, “Differentiation of benign from malignant solid breast masses: conventional US versus spatial compound imaging,” Radiology 237(3), 841–846 (2005).
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Retin Cases Brief Rep. (1)

A. Shahlaee, B. K. Hong, and A. C. Ho, “Optical coherence tomography angiography features of branch retinal vein occlusion,” Retin Cases Brief Rep. 11(1), 90–93 (2017).
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Retina (2)

K. B. Schaal, M. R. Munk, I. Wyssmueller, L. E. Berger, M. S. Zinkernagel, and S. Wolf, “Vascular abnormalities in diabetic retinopathy assessed with swept-source optical coherence tomography angiography widefield imaging,” Retina 39(1), 79–87 (2019).
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T. S. Hwang, Y. Jia, S. S. Gao, S. T. Bailey, A. K. Lauer, C. J. Flaxel, D. J. Wilson, and D. Huang, “Optical coherence tomography angiography features of diabetic retinopathy,” Retina 35(11), 2371–2376 (2015).
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P. Nelis, I. Kleffner, M. C. Burg, C. R. Clemens, M. Alnawaiseh, J. Motte, M. Marziniak, N. Eter, and F. Alten, “OCT-Angiography reveals reduced vessel density in the deep retinal plexus of CADASIL patients,” Sci. Rep. 8(1), 8148 (2018).
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Sci. Technol. Adv. Mater. (1)

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Science (1)

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[Crossref]

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“ https://www.biospace.com/article/releases/rakuten-medical-japan-announces-marketing-approval-of-akalux-iv-infusion-250mg-and-bioblade-laser-system-from-the-ministry-of-labour-health-and-welfare-in-japan-for-locoregional-cancer-treatment/ .”

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Figures (7)

Fig. 1.
Fig. 1. Calibration of silicone tubes. (a) Cross-sectional OCT image of the vessel phantom. (b) Projection view of capillary tubes in SV-OCT mode. (c) En face image in conventional structural OCT mode. (d) Binary and centerline of (b). (e) Measured value of lumen in vessel phantom diameters from conventional structural OCT in largest en face image of each capillaries tube and SV-OCT in projection view image. Scales bar: 100 μm. N=10. NS denotes P > 0.05.
Fig. 2.
Fig. 2. Skin vessel images. (a) Projection view image of skin blood vessels in SV mode with multilayer superposition. (b) Blood vessels in SV mode pseudo-colored by depth from 0 μm to 480 μm. (c) En face image of skin blood vessel in structure OCT mode. (d) 3D images of fused SV-OCT and conventional structural OCT results. (e) Cross-sectional image of bio-tissue in SV mode corresponding the position indicated by the white dashed square in (d). (f) Cross-sectional image in conventional structural OCT mode corresponding the position indicated by the white dashed square in (d). (g) Fused cross-sectional images of SV mode and conventional structural OCT mode from the white arrow in (d); Scale bar: 100 μm.
Fig. 3.
Fig. 3. Images of tumor vasculature changes without LED exposure from 0 to 30 min. (a) The vessel lumen pseudo-colored with depth from 0 μm to 480 μm. (b) The vessels in 3D mode. (c) Vascular density without LED exposure from 0 to 30 min. (d) Measurable diameter of lumen in blood vessel with different sizes (at locations 1-4 indicated by arrows in b) from 0 to 30 min. Scale bar = 200μm. N=6.
Fig. 4.
Fig. 4. Images of tumor vasculature changes during and after NIR-PIT process. (a) The lumen in blood vessels pseudo-colored with depth from 0 μm to 480 μm during LED-ON for 30 min. (b) The corresponding 3D vessels with SV-OCT during LED-ON for 30 min. (c) The lumen in blood vessels pseudo-colored with depth from 0 μm to 480 μm within 30 min after turning off LED. (d) The corresponding 3D vessels within 30 min after turning off LED. Scale bar = 200μm.
Fig. 5.
Fig. 5. Change of tumor vasculatures during and after NIR-PIT process in cross-sectional view. The first row is tumor structure obtained by conventional structural OCT mode, the second row is lumen blood vessel structure (the region filled with blood) obtained by SV mode, and the third row is the overlay of two modes. First column is at time point right before LED irradiation; the second column is right after 30 min LED irradiation. The third column is 30 min after LED was turned off. Scale bar: 200 μm.
Fig. 6.
Fig. 6. Changes in tumor blood vessels during NIR-PIT process (a) 3D vessels with blood image. (b) Normalized vascular density changes. (c) MDLBV changes with different sizes. (d) MDLBV reduction with different sizes. Scale bar: 100 μm. N=6. NS denotes P > 0.05, * denotes P < 0.05, ** denotes P <0.01, *** denotes P <0.001.
Fig. 7.
Fig. 7. Histological results of the NIR-PIT treated tumor and control group (no LED exposure). (a) and (b) are H&E staining images. (c) and (d) are CD31 staining images. (a) and (c) are control group (no LED exposure). (b) and (d) are PIT-treated tumor. Scale bar: 100 μm.

Equations (1)

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S V ( i , j , k ) = 1 N i = 1 N [ I ( i , j , k ) 1 N i = 1 N I ( i , j , k ) ] 2

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