Abstract

Skull bone represents a highly acoustical impedance mismatch and a dispersive barrier for the propagation of acoustic waves. Skull distorts the amplitude and phase information of the received waves at different frequencies in a transcranial brain imaging. We study a novel algorithm based on vector space similarity model for the compensation of the skull-induced distortions in transcranial photoacoustic microscopy. The results of the algorithm tested on a simplified numerical skull phantom, demonstrate a fully recovered vasculature with the recovery rate of 91.9%.

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

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2019 (2)

L. Mohammadi, H. Behnam, J. Tavakkoli, and M. Avanaki, “Skull’s photoacoustic attenuation and dispersion modeling with deterministic ray-tracing: Towards real-time aberration correction,” Sensors 19(2), 345 (2019).
[Crossref]

S. Antholzer, M. Haltmeier, and J. Schwab, “Deep learning for photoacoustic tomography from sparse data,” Inverse Probl. Sci. Eng. 27(7), 987–1005 (2019).
[Crossref]

2018 (9)

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, S. Adabi, and M. Nasiriavanaki, “Double-stage delay multiply and sum beamforming algorithm: Application to linear-array photoacoustic imaging,” IEEE Trans. Biomed. Eng. 65(1), 31–42 (2018).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, K. Kratkiewicz, S. Adabi, and M. Nasiriavanaki, “Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm,” J. Biomed. Opt. 23(02), 1 (2018).
[Crossref]

P. Omidi, M. Zafar, M. Mozaffarzadeh, A. Hariri, X. Haung, M. Orooji, and M. Nasiriavanaki, “A novel dictionary-based image reconstruction for photoacoustic computed tomography,” Appl. Sci. 8(9), 1570 (2018).
[Crossref]

H. Estrada, X. Huang, J. Rebling, M. Zwack, S. Gottschalk, and D. Razansky, “Virtual craniotomy for high-resolution optoacoustic brain microscopy,” Sci. Rep. 8(1), 1459 (2018).
[Crossref]

K. Yoon, W. Lee, P. Croce, A. Cammalleri, and S.-S. Yoo, “Multi-resolution simulation of focused ultrasound propagation through ovine skull from a single-element transducer,” Phys. Med. Biol. 63(10), 105001 (2018).
[Crossref]

D. Allman, A. Reiter, and M. A. L. Bell, “Photoacoustic source detection and reflection artifact removal enabled by deep learning,” IEEE Trans. Med. Imaging 37(6), 1464–1477 (2018).
[Crossref]

A.-R. Mohammadi-Nejad, M. Mahmoudzadeh, M. S. Hassanpour, F. Wallois, O. Muzik, C. Papadelis, A. Hansen, H. Soltanian-Zadeh, J. Gelovani, and M. Nasiriavanaki, “Neonatal brain resting-state functional connectivity imaging modalities,” Photoacoustics 10, 1 (2018).
[Crossref]

J. Kang, E. M. Boctor, S. Adams, E. Kulikowicz, H. K. Zhang, R. C. Koehler, and E. M. Graham, “Validation of noninvasive photoacoustic measurements of sagittal sinus oxyhemoglobin saturation in hypoxic neonatal piglets,” J. Appl. Physiol. 125(4), 983–989 (2018).
[Crossref]

S. Mahmoodkalayeh, H. Z. Jooya, A. Hariri, Y. Zhou, Q. Xu, M. A. Ansari, and M. R. Avanaki, “Low temperature-mediated enhancement of photoacoustic imaging depth,” Sci. Rep. 8(1), 4873 (2018).
[Crossref]

2016 (5)

L. Li, J. Xia, G. Li, A. Garcia-Uribe, Q. Sheng, M. A. Anastasio, and L. V. Wang, “Label-free photoacoustic tomography of whole mouse brain structures ex vivo,” Neurophotonics 3(3), 1 (2016).
[Crossref]

M. Kneipp, J. Turner, H. Estrada, J. Rebling, S. Shoham, and D. Razansky, “Effects of the murine skull in optoacoustic brain microscopy,” J. Biophotonics 9(1-2), 117–123 (2016).
[Crossref]

H. Estrada, J. Rebling, J. Turner, and D. Razansky, “Broadband acoustic properties of a murine skull,” Phys. Med. Biol. 61(5), 1932–1946 (2016).
[Crossref]

S. Almquist, D. L. Parker, and D. A. Christensen, “Rapid full-wave phase aberration correction method for transcranial high-intensity focused ultrasound therapies,” Journal of therapeutic ultrasound 4(1), 30 (2016).
[Crossref]

B. Petrovic and S. Parolai, “Joint deconvolution of building and downhole strong-motion recordings: Evidence for the seismic wavefield being radiated back into the shallow geological layers,” Bull. Seismol. Soc. Am. 106(4), 1720–1732 (2016).
[Crossref]

2015 (3)

A. Kyriakou, E. Neufeld, B. Werner, G. Székely, and N. Kuster, “Full-wave acoustic and thermal modeling of transcranial ultrasound propagation and investigation of skull-induced aberration correction techniques: a feasibility study,” J. therapeutic ultrasound 3(1), 11 (2015).
[Crossref]

J. Yao, L. Wang, J.-M. Yang, K. I. Maslov, T. T. Wong, L. Li, C.-H. Huang, J. Zou, and L. V. Wang, “High-speed label-free functional photoacoustic microscopy of mouse brain in action,” Nat. Methods 12(5), 407–410 (2015).
[Crossref]

L. Lin, J. Xia, T. T. Wong, L. Li, and L. V. Wang, “In vivo deep brain imaging of rats using oral-cavity illuminated photoacoustic computed tomography,” J. Biomed. Opt. 20(1), 016019 (2015).
[Crossref]

2014 (2)

M. Nasiriavanaki, J. Xia, H. Wan, A. Q. Bauer, J. P. Culver, and L. V. Wang, “High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain,” Proc. Natl. Acad. Sci. 111(1), 21–26 (2014).
[Crossref]

A. Kyriakou, E. Neufeld, B. Werner, M. M. Paulides, G. Szekely, and N. Kuster, “A review of numerical and experimental compensation techniques for skull-induced phase aberrations in transcranial focused ultrasound,” Int. J. Hyperthermia 30(1), 36–46 (2014).
[Crossref]

2013 (3)

B. E. Treeby, “Acoustic attenuation compensation in photoacoustic tomography using time-variant filtering,” J. Biomed. Opt. 18(3), 036008 (2013).
[Crossref]

J. Yao, J. Xia, K. I. Maslov, M. Nasiriavanaki, V. Tsytsarev, A. V. Demchenko, and L. V. Wang, “Noninvasive photoacoustic computed tomography of mouse brain metabolism in vivo,” NeuroImage 64, 257–266 (2013).
[Crossref]

M. Kwak, G. Leroy, J. D. Martinez, and J. Harwell, “Development and evaluation of a biomedical search engine using a predicate-based vector space model,” J. Biomed. Inf. 46(5), 929–939 (2013).
[Crossref]

2012 (4)

J.-M. Yang, C. Favazza, R. Chen, J. Yao, X. Cai, K. Maslov, Q. Zhou, K. K. Shung, and L. V. Wang, “Simultaneous functional photoacoustic and ultrasonic endoscopy of internal organs in vivo,” Nat. Med. 18(8), 1297–1302 (2012).
[Crossref]

G. Pinton, J.-F. Aubry, E. Bossy, M. Muller, M. Pernot, and M. Tanter, “Attenuation, scattering, and absorption of ultrasound in the skull bone,” Med. Phys. 39(1), 299–307 (2012).
[Crossref]

C. Huang, L. Nie, R. W. Schoonover, Z. Guo, C. O. Schirra, M. A. Anastasio, and L. V. Wang, “Aberration correction for transcranial photoacoustic tomography of primates employing adjunct image data,” J. Biomed. Opt. 17(6), 066016 (2012).
[Crossref]

R. W. Schoonover, L. V. Wang, and M. A. Anastasio, “Numerical investigation of the effects of shear waves in transcranial photoacoustic tomography with a planar geometry,” J. Biomed. Opt. 17(6), 061215 (2012).
[Crossref]

2011 (4)

X. L. Deán-Ben, D. Razansky, and V. Ntziachristos, “The effects of acoustic attenuation in optoacoustic signals,” Phys. Med. Biol. 56(18), 6129–6148 (2011).
[Crossref]

K. Nam, I. M. Rosado-Mendez, N. C. Rubert, E. L. Madsen, J. A. Zagzebski, and T. J. Hall, “Ultrasound attenuation measurements using a reference phantom with sound speed mismatch,” Ultrasonic imaging 33(4), 251–263 (2011).
[Crossref]

M. A. O’Reilly, A. Muller, and K. Hynynen, “Ultrasound insertion loss of rat parietal bone appears to be proportional to animal mass at submegahertz frequencies,” Ultrasound in medicine & biology 37(11), 1930–1937 (2011).
[Crossref]

H.-C. Cho, L. Hadjiiski, B. Sahiner, H.-P. Chan, M. Helvie, C. Paramagul, and A. V. Nees, “Similarity evaluation in a content-based image retrieval (cbir) cadx system for characterization of breast masses on ultrasound images,” Med. Phys. 38(4), 1820–1831 (2011).
[Crossref]

2010 (1)

B. E. Treeby and B. Cox, “Modeling power law absorption and dispersion for acoustic propagation using the fractional laplacian,” J. Acoust. Soc. Am. 127(5), 2741–2748 (2010).
[Crossref]

2009 (2)

F. Marquet, M. Pernot, J. Aubry, G. Montaldo, L. Marsac, M. Tanter, and M. Fink, “Non-invasive transcranial ultrasound therapy based on a 3d ct scan: protocol validation and in vitro results,” Phys. Med. Biol. 54(9), 2597–2613 (2009).
[Crossref]

S. Hu, K. Maslov, V. Tsytsarev, and L. V. Wang, “Functional transcranial brain imaging by optical-resolution photoacoustic microscopy,” J. Biomed. Opt. 14(4), 040503 (2009).
[Crossref]

2008 (3)

X. Jin, C. Li, and L. V. Wang, “Effects of acoustic heterogeneities on transcranial brain imaging with microwave-induced thermoacoustic tomography,” Med. Phys. 35(7Part1), 3205–3214 (2008).
[Crossref]

M.-L. Li, J.-T. Oh, X. Xie, G. Ku, W. Wang, C. Li, G. Lungu, G. Stoica, and L. V. Wang, “Simultaneous molecular and hypoxia imaging of brain tumors in vivo using spectroscopic photoacoustic tomography,” Proc. IEEE 96(3), 481–489 (2008).
[Crossref]

L. V. Wang, “Tutorial on photoacoustic microscopy and computed tomography,” IEEE J. Sel. Top. Quantum Electron. 14(1), 171–179 (2008).
[Crossref]

2004 (1)

G. Clement, P. White, and K. Hynynen, “Enhanced ultrasound transmission through the human skull using shear mode conversion,” J. Acoust. Soc. Am. 115(3), 1356–1364 (2004).
[Crossref]

2003 (2)

X. Wang, Y. Pang, G. Ku, X. Xie, G. Stoica, and L. V. Wang, “Noninvasive laser-induced photoacoustic tomography for structural and functional in vivo imaging of the brain,” Nat. Biotechnol. 21(7), 803–806 (2003).
[Crossref]

J.-F. Aubry, M. Tanter, M. Pernot, J.-L. Thomas, and M. Fink, “Experimental demonstration of noninvasive transskull adaptive focusing based on prior computed tomography scans,” J. Acoust. Soc. Am. 113(1), 84–93 (2003).
[Crossref]

2002 (1)

J. Herlocker, J. A. Konstan, and J. Riedl, “An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms,” Information retrieval 5(4), 287–310 (2002).
[Crossref]

1997 (1)

D. L. Lee, H. Chuang, and K. Seamons, “Document ranking and the vector-space model,” IEEE Softw. 14(2), 67–75 (1997).
[Crossref]

1988 (1)

G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Inf. Process. Manage. 24(5), 513–523 (1988).
[Crossref]

1978 (1)

F. Fry and J. Barger, “Acoustical properties of the human skull,” J. Acoust. Soc. Am. 63(5), 1576–1590 (1978).
[Crossref]

Adabi, S.

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[Crossref]

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Figures (6)

Fig. 1.
Fig. 1. 2-D illustration of a simple model of numerical skull phantom used in our simulations. The initial pressure point source modeled as a sphere with the radius of $r_0$ at a depth of $d$ from the outer-skull surface. A single layer homogenized skull tissue with a thickness variation between 0.5 mm and 2.5 mm is considered. The solid acceptance angle of the transducer is indicated by the dashed lines.
Fig. 2.
Fig. 2. Skull-induced aberration compensation of PA signals produced from a 0.1 mm absorbing sphere passing through a skull tissue with the thickness of 1 mm located at depths (a) 2 mm, (b) 8 mm, and (c) 20 mm. (i) Signal amplitudes and (ii) signal gradients. In this simulation, there is a 5 mm layer of ultrasound gel between the ultrasound transducer and the skull.
Fig. 3.
Fig. 3. Skull-induced aberration compensation of PA signals produced from a 0.1 mm absorbing sphere located at the depth of 5 mm passing through a skull tissue with the thicknesses of (a) 0.5 mm, (b) 1 mm, and (c) 2 mm. (i) Signal amplitudes and (ii) signal gradients. In this simulation, there is a 5 mm layer of ultrasound gel between the ultrasound transducer and the skull.
Fig. 4.
Fig. 4. Skull-induced aberration compensation of PA signals produced from a 0.1 mm absorbing sphere passing through a skull tissue. (a) Skull thickness is 2 mm and the target is located at 5 mm depth (PA signal is contaminated with 10% background noise), (b) skull thickness is 2 mm and the target is located at 5 mm depth (PA signal is contaminated with 20% background noise), (c) skull thickness is 1 mm and the target is located at 20 mm depth (PA signal is contaminated with 10% background noise), and (d) skull thickness is 1 mm and the target is located at 20 mm depth (PA signal is contaminated with 20% background noise). (i) Signal amplitudes and (ii) signal gradients. In this simulation, there is a 5 mm layer of ultrasound gel between the ultrasound transducer and the skull.
Fig. 5.
Fig. 5. Aberration correction of TsPAM images. (a) Synthetic TsPAM image acquired from the experimental setup depicted in Fig. 1, (i) unaberrated image, (ii) aberrated image, (iii) compensated image. (b) A representative depth profile, indicated with green dotted lines in images in (a), of the unaberrated, aberrated and compensated images.
Fig. 6.
Fig. 6. Aberration correction of noisy TsPAM images, reproduced from the TsPAM in Fig. 5, (a) with 10% noise, and (b) with 20% noise. (i) Unaberrated image, (ii) aberrated image, and (iii) compensated image.

Tables (3)

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Table 1. Acoustic properties of skull, brain soft tissue, and the ultrasound gel used in the simulations.a

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Table 2. Recovery percentage calculated for compensated original signal, noisy signal with 10% noise and with 20% noise.

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Table 3. Non-flat skull aberration compensation. Skull thickness variation ( Δ h), transmitted signal percentage, and recovery percentage of the distorted signals generated with non-flat skulls with angles from 5 to 30 versus the transducer axis.

Equations (4)

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c o s ( f i j , f q j ) = f i j . f q j f i j . f q j = Σ k = 1 n f i j , k . f q j , k Σ k = 1 n f i j , k 2 . Σ k = 1 n f q j , k 2
S i m i l a r i t y ( f i j , f q j ) = a r c c o s ( Σ k = 1 n f i j , k . f q j , k Σ k = 1 n f i j , k 2 . Σ k = 1 n f q j , k 2 )
S i m i l a r i t y ( q , d i ) = 1 2 ( S i m i l a r i t y ( T i , T q ) + S i m i l a r i t y ( A i , A q )
r e c o v e r y _   p e r c e n t a g e   ( % ) = ( 1 l 2 ( s i g n a l c o m p s i g n a l w i t h o u t ) l 2 ( s i g n a l w i t h o u t ) ) × 100