Abstract

Assessment of liver function reserve (LFR) is essential to determine liver resection scope and predict prognosis for patients with liver disease. Indocyanine green (ICG) concentration change is a classic marker to reflect liver function reserve as ICG is selectively taken up and eliminated by liver. Here we proposed a noninvasive approach for LFR assessment based on a real-time photoacoustic tomography (PAT) system. This feasibility study was to detect ICG concentration change by PAT in phantom and in vivo using both normal and partial hepatectomy (PH) rabbits. A linear relationship between photoacoustic signal intensity of ICG and ICG concentration was found in vitro. In vivo ICG concentration change over time after ICG injection was observed by PAT in normal rabbits, which was consistent with the findings measured by invasive spectrophotometry. Finally, clear difference in ICG clearance between the control and PH models was identified by PAT. Taken together, our study indicated the clinical potential of PAT to in vivo evaluate LFR noninvasively.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2020 (1)

J. Lavaud, M. Henry, P. Gayet, A. Fertin, J. Vollaire, Y. Usson, J.-L. Coll, and V. Josserand, “Noninvasive monitoring of liver metastasis development via combined multispectral photoacoustic imaging and fluorescence diffuse optical tomography,” Int. J. Biol. Sci. 16(9), 1616–1628 (2020).
[Crossref]

2019 (5)

2018 (8)

W. Choi, E. Y. Park, S. Jeon, and C. Kim, “Clinical photoacoustic imaging platforms,” Biomed. Eng. Lett. 8(2), 139–155 (2018).
[Crossref]

P. H. Alizai, A. Haelsig, P. Bruners, F. Ulmer, C. D. Klink, C. H. C. Dejong, U. P. Neumann, and M. Schmeding, “Impact of liver volume and liver function on posthepatectomy liver failure after portal vein embolization– A multivariable cohort analysis,” Ann. Med. Surg. (Lond) 25, 6–11 (2018).
[Crossref]

J. K. Heimbach, L. M. Kulik, R. S. Finn, C. B. Sirlin, M. M. Abecassis, L. R. Roberts, A. X. Zhu, M. H. Murad, and J. A. Marrero, “AASLD guidelines for the treatment of hepatocellular carcinoma,” Hepatology 67(1), 358–380 (2018).
[Crossref]

Y.-Y. Wang, X.-H. Zhao, L. Ma, J.-Z. Ye, F.-X. Wu, J. Tang, X.-M. You, B.-D. Xiang, and L.-Q. Li, “Comparison of the ability of Child-Pugh score, MELD score, and ICG-R15 to assess preoperative hepatic functional reserve in patients with hepatocellular carcinoma,” J. Surg. Oncol. 118(3), 440–445 (2018).
[Crossref]

Y. Mizutani, T. Hirai, S. Nagamachi, A. Nanashima, K. Yano, K. Kondo, M. Hiyoshi, N. Imamura, and T. Terada, “Prediction of Posthepatectomy Liver Failure Proposed by the International Study Group of Liver Surgery: Residual Liver Function Estimation With 99mTc-Galactosyl Human Serum Albumin Scintigraphy,” Clin. Nucl. Med. 43(2), 77–81 (2018).
[Crossref]

K. Okumura, K. Yoshida, K. Yoshioka, S. Aki, N. Yoneda, D. Inoue, A. Kitao, T. Ogi, K. Kozaka, T. Minami, W. Koda, S. Kobayashi, Y. Takuwa, and T. Gabata, “Photoacoustic Imaging of Tumour Vascular Permeability With Indocyanine Green in a Mouse Model,” Eur. Radiol. Exp. 2(1), 5 (2018).
[Crossref]

M.-W. You, H. J. Kim, H.-S. Lim, S. Y. Kim, J. H. Byun, K. W. Kim, D. W. Hwang, and Y.-J. Lee, “Assessment of liver function using pharmacokinetic parameters of Gd-EOB-DTPA: Experimental study in rat hepatectomy model,” Contrast Media Mol. Imaging 2018, 6321316 (2018).
[Crossref]

Y. Zhu, G. Xu, J. Yuan, J. Jo, G. Gandikota, H. Demirci, T. Agano, N. Sato, Y. Shigeta, and X. Wang, “Light Emitting Diodes based Photoacoustic Imaging and Potential Clinical Applications,” Sci. Rep. 8(1), 9885 (2018).
[Crossref]

2017 (7)

X. Shu, H. Li, B. Dong, C. Sun, and H. F. Zhang, “Quantifying Melanin Concentration in Retinal Pigment Epithelium Using Broadband Photoacoustic Microscopy,” Biomed. Opt. Express 8(6), 2851–2865 (2017).
[Crossref]

G. S. Sangha, E. H. Phillips, and C. J. Goergen, “In vivo photoacoustic lipid imaging in mice using the second near-infrared window,” Biomed. Opt. Express 8(2), 736–742 (2017).
[Crossref]

E. Mercep, X. L. Dean-Ben, and D. Razansky, “Combined Pulse-Echo Ultrasound and Multispectral Optoacoustic Tomography with a Multi-Segment Detector Array,” IEEE Trans. Med. Imaging. 36(10), 2129–2137 (2017).
[Crossref]

M. Nakagawa, T. Namimoto, K. Shimizu, K. Morita, F. Sakamoto, S. Oda, T. Nakaura, D. Utsunomiya, S. Shiraishi, and Y. Yamashita, “Measuring hepatic functional reserve using T1 mapping of Gd-EOB-DTPA enhanced 3 T MR imaging: A preliminary study comparing with 99mTc GSA scintigraphy and signal intensity based parameters,” Eur. J. Radiol. 92, 116–123 (2017).
[Crossref]

N. Brillant, M. Elmasry, N. C. Burton, J. M. Rodriguez, J. W. Sharkey, S. Fenwick, H. Poptani, N. R. Kitteringham, C. E. Goldring, A. Kipar, B. Kevin Park, and D. J. Antoine, “Dynamic and accurate assessment of acetaminophen-induced hepatotoxicity by integrated photoacoustic imaging and mechanistic biomarkers in vivo,” Toxicol. Appl. Pharmacol. 332, 64–74 (2017).
[Crossref]

G. Diot, S. Metz, A. Noske, E. Liapis, B. Schroeder, S. V. Ovsepian, R. Meier, E. Rummeny, and V. Ntziachristos, “Multispectral Optoacoustic Tomography (MSOT) of human breast cancer,” Clin. Cancer Res. 23(22), 6912–6922 (2017).
[Crossref]

M. Yang, L. Zhao, X. He, N. Su, C. Zhao, H. Tang, T. Hong, W. Li, F. Yang, L. Lin, B. Zhang, R. Zhang, Y. Jiang, and C. Li, “Photoacoustic/ultrasound dual imaging of human thyroid cancers: an initial clinical study,” Biomed. Opt. Express 8(7), 3449–3457 (2017).
[Crossref]

2016 (1)

2015 (1)

N. Beziere, N. Lozano, A. Nunes, J. Salichs, D. Queiros, K. Kostarelos, and V. Ntziachristos, “Dynamic imaging of PEGylated indocyanine green (ICG) liposomes within the tumor microenvironment using multi-spectral optoacoustic tomography (MSOT),” Biomaterials 37, 415–424 (2015).
[Crossref]

2014 (5)

S. Park, J. Kim, M. Jeon, J. Song, and C. Kim, “In vivo photoacoustic and fluorescence cystography using clinically relevant dual modal indocyanine green,” Sensors 14(10), 19660–19668 (2014).
[Crossref]

G. Xu, Z.-X. Meng, J. D. Lin, J. Yuan, P. L. Carson, B. Joshi, and X. Wang, “The functional pitch of an organ: quantification of tissue texture with photoacoustic spectrum analysis,” Radiology 271(1), 248–254 (2014).
[Crossref]

G. G. Lockwood, N. L. S. Fung, and J. G. Jones, “Evaluation of a computer program for non-invasive determination of pulmonary shunt and ventilation-perfusion mismatch,” J. Clin. Monit. Comput. 28(6), 581–590 (2014).
[Crossref]

M. C. Lim, C. H. Tan, J. Cai, J. Zheng, and A. W. C. Kow, “CT volumetry of the liver: Where does it stand in clinical practice,” Clin. Radiol. 69(9), 887–895 (2014).
[Crossref]

T. Hori, S. Yagi, Y. Okamua, T. Iida, K. Ogawa, H. Tanaka, S. Kageyama, H. Hirao, T. Hata, I. Kirino, K. Nagai, T. Kubora, K. Jobara, K. Endo, and S. Uemoto, “How to successfully resect 70% of the liver in pigs to model an extended hepatectomy with an insufficient remnant or liver transplantation with a small-for-size graft,” Surg. Today 44(11), 2201–2207 (2014).
[Crossref]

2013 (1)

2012 (1)

L. V. Wang and S. Hu, “Photoacoustic tomography: In vivo imaging from organelles to organs,” Science 335(6075), 1458–1462 (2012).
[Crossref]

2011 (1)

N. N. Rahbari, O. J. Garden, R. Padbury, M. Brooke-Smith, M. Crawford, R. Adam, M. Koch, M. Makuuchi, R. P. Dematteo, C. Christophi, S. Banting, V. Usatoff, M. Nagino, G. Maddern, T. J. Hugh, J.-N. Vauthey, P. Greig, M. Rees, Y. Yokoyama, S. T. Fan, Y. Nimura, J. Figueras, L. Capussotti, M. W. Büchler, and J. Weitz, “Posthepatectomy liver failure: A definition and grading by the International Study Group of Liver Surgery (ISGLS),” Surgery 149(5), 713–724 (2011).
[Crossref]

2009 (1)

Y. Seyama and N. Kokudo, “Assessment of liver function for safe hepatic resection,” Hepatol. Res. 39(2), 107–116 (2009).
[Crossref]

2006 (1)

C. M. Leevy, J. Bender, M. Silverberg, and J. Naylor, “Physiology of dye extraction by the liver: comparative studies of sulfobromophthalein and indocyanine green,” Ann. N. Y. Acad. Sci. 111(1), 161–175 (2006).
[Crossref]

2005 (3)

G. Ku and L. V. Wang, “Deeply penetrating photoacoustic tomography in biological tissues enhanced with an optical contrast agent,” Opt. Lett. 30(5), 507–509 (2005).
[Crossref]

H. Imamura, K. Sano, Y. Sugawara, N. Kokudo, and M. Makuuchi, “Assessment of hepatic reserve for indication of hepatic resection: Decision tree incorporating indocyanine green test,” J. Hepatobiliary. Pancreat. Surg. 12(1), 16–22 (2005).
[Crossref]

M. Xu and L. V. Wang, “Universal back-projection algorithm for photoacoustic computed tomography,” Phys. Rev. E: Stat., Nonlinear, Soft Matter Phys. 71(1), 016706 (2005).
[Crossref]

2003 (1)

T. Aoyagi, “Pulse oximetry: its invention, theory, and future,” J. Anesth. 17(4), 259–266 (2003).
[Crossref]

1998 (2)

P. Ott, “Hepatic elimination of indocyanine green with special reference to distribution kinetics and the influence of plasma protein binding,” Pharmacol. Toxicol. (Oxford, U. K.) 83(Suppl 2), 1–48 (1998).
[Crossref]

T. Imai, K. Takahashi, F. Goto, and Y. Morishita, “Measurement of blood concentration of indocyanine green by pulse dye densitometry - Comparison with the conventional spectrophotometric method,” J. Clin. Monit. Comput. 14(7/8), 477–484 (1998).
[Crossref]

1986 (1)

R. Heintz, C. K. Svensson, K. Stoeckel, G. J. Powers, and D. Lalka, “Indocyanine green: Pharmacokinetics in the rabbit and relevant studies of its stability and purity,” J. Pharm. Sci. 75(4), 398–402 (1986).
[Crossref]

1984 (1)

J. J. Thiessen, P. L. Rappaport, and J. G. Eppel, “Indocyanine green pharmacokinetics in the rabbit,” Can. J. Physiol. Pharmacol. 62(9), 1078–1085 (1984).
[Crossref]

1982 (1)

C. K. Svensson, D. J. Edwards, D. Lalka, P. M. Mauriello, and E. Middleton, “Comparison of chromatographic and spectrophotometric analysis of indocyanine green in plasma following administration of multiple doses to humans,” J. Pharm. Sci. 71(11), 1305–1306 (1982).
[Crossref]

1980 (1)

K. Stoeckel, P. J. McNamara, A. J. McLean, P. duSouich, D. Lalka, and M. Gibaldi, “Nonlinear pharmacokinetics of indocyanine green in the rabbit and rat,” J. Pharmacokinet. Biopharm. 8(5), 483–496 (1980).
[Crossref]

1976 (1)

M. L. Landsman, G. Kwant, G. A. Mook, and W. G. Zijlstra, “Light absorbing properties, stability, and spectral stabilization of indocyanine green,” J. Appl. Physiol. 40(4), 575–583 (1976).
[Crossref]

Abecassis, M. M.

J. K. Heimbach, L. M. Kulik, R. S. Finn, C. B. Sirlin, M. M. Abecassis, L. R. Roberts, A. X. Zhu, M. H. Murad, and J. A. Marrero, “AASLD guidelines for the treatment of hepatocellular carcinoma,” Hepatology 67(1), 358–380 (2018).
[Crossref]

Adam, R.

N. N. Rahbari, O. J. Garden, R. Padbury, M. Brooke-Smith, M. Crawford, R. Adam, M. Koch, M. Makuuchi, R. P. Dematteo, C. Christophi, S. Banting, V. Usatoff, M. Nagino, G. Maddern, T. J. Hugh, J.-N. Vauthey, P. Greig, M. Rees, Y. Yokoyama, S. T. Fan, Y. Nimura, J. Figueras, L. Capussotti, M. W. Büchler, and J. Weitz, “Posthepatectomy liver failure: A definition and grading by the International Study Group of Liver Surgery (ISGLS),” Surgery 149(5), 713–724 (2011).
[Crossref]

Agano, T.

Y. Zhu, G. Xu, J. Yuan, J. Jo, G. Gandikota, H. Demirci, T. Agano, N. Sato, Y. Shigeta, and X. Wang, “Light Emitting Diodes based Photoacoustic Imaging and Potential Clinical Applications,” Sci. Rep. 8(1), 9885 (2018).
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G. Xu, Z.-X. Meng, J. D. Lin, J. Yuan, P. L. Carson, B. Joshi, and X. Wang, “The functional pitch of an organ: quantification of tissue texture with photoacoustic spectrum analysis,” Radiology 271(1), 248–254 (2014).
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J. K. Heimbach, L. M. Kulik, R. S. Finn, C. B. Sirlin, M. M. Abecassis, L. R. Roberts, A. X. Zhu, M. H. Murad, and J. A. Marrero, “AASLD guidelines for the treatment of hepatocellular carcinoma,” Hepatology 67(1), 358–380 (2018).
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Figures (6)

Fig. 1.
Fig. 1. A schematic of the experiment system. The hardware components employed in the PAT system. DAS: Data Acquisition System (preamplifiers, multiplexers and analog-to-digital converters). OPO: Q-switched Nd:YAG-pumped optical parameter oscillator system. PC: personal computer. Imaging Probe (red dotted box): a 128-element transducer array, a fiber bundle and a resin shell enclosed with a membrane.
Fig. 2.
Fig. 2. In vivo imaging of rabbit ear. (a). Photograph of the photoacoustic imaging setup. (b) Photograph of rabbit ear under imaging showing the clearly visible central auricular artery (CAA) morphology and location. Transverse photoacoustic (c) and US (d) images of the rabbit ear with the CAA marked by yellow dashed circle and arrow, respectively. Region of interest (ROI) (yellow dashed circle) was selected to determine the photoacoustic signal intensity.
Fig. 3.
Fig. 3. An exponential decay model was used to determine the PSIre change after it reached the peak after ICG injection (red line).
Fig. 4.
Fig. 4. Measurement of ICG in vitro phantom by spectrophotometer and photoacoustic tomography. (a). Tissue mimicking phantom setup. (b). Normalized absorbance of ICG by spectrophotometer (Sp, blue line) compared to the normalized PSI of ICG obtained by photoacoustic tomography (PA, green line). (c). Representative PA images showing a map of optical absorption from ICG with the indicated concentrations of ICG (0, 0.1, 0.2, 0.3, 0.5, 1.0 mg/dl). a.u.=arbitrary unit, ICG = indocyanine green, Sp = spectrophotometer, PA = photoacoustic PSI = photoacoustic signal intensity
Fig. 5.
Fig. 5. (a) PSI measured in vivo (purple line) and in vitro (red circle) by PAT, and absorbance estimated in vitro by spectrophotometer (green hollow box). The in vivo PAT data was collected using normal rabbits (n=7, male, weight: 2.5-2.7 kg), while both the in vitro PAT data and absorbance measurement were performed using blood sample. (b) Close-up view of the data shown in (a) in the first 300 s (red dashed box). ICG = indocyanine green, Sp = spectrophotometer, PA = photoacoustic, PSI = photoacoustic signal intensity, PAT = photoacoustic tomography, PSIre=relative photoacoustic signal intensity.
Fig. 6.
Fig. 6. ICG clearance studies by real-time PAT using PH and control groups of rabbits (n=4, PH group, n=6, Control group). (a) Temporal PA signal trace, (b) elimination rate constant k, (c) elimination time t0, and (d) significant differences in PSIre for the PH (red) and control (blue) groups. PSIre=relative photoacoustic signal intensity, ICG = indocyanine green, PA = photoacoustic PH = partial hepatectomy, PAT = photoacoustic tomography, a.u.=arbitrary unit.

Tables (1)

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Table 1. Conventional liver function tests in both PH and Control group

Equations (3)

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P S I r e ( t ) = P S I ( t ) P S I b a s e P S I b a s e
P S I r e ( t ) = P S I r e ( 0 ) e k t
ρ ( A , B ) = 1 N 1 i = 1 N ( A i μ A ¯ σ A ) ( B i μ B ¯ σ B )