Abstract

Accurate anatomical localization of functional information is the main goal of hybridizing optoacoustic and ultrasound imaging, with the promise of early stage diagnosis and disease pathophysiology. Optoacoustic integration to ultrasound is a relatively mature technique for clinical two-dimensional imaging, however the complexity of biological samples places particular demands for volumetric measurement and reconstruction. This integration is a multi-fold challenge that is mainly associated with the system geometry, the sampling and beam quality. In this study, we evaluated the design geometry for the sparse ultrasonic hand-held probe that is popularly associated with three-dimensional imaging of anatomical deformation, to incorporate the three-dimensional optoacoustic physiological information. We explored the imaging performance of three unconventional annular geometries; namely, segmented, spiral, and circular geometries. To avoid bias evaluation, two classes of analytical and model-based algorithms were used. The superior performance of the segmented annular array for recovery of the true object is demonstrated. Along with the model-based approach, this geometry offers spatial invariant resolution for the optoacoustic mode for the given field of view.The analytical approach, on the other hand, is computationally less expensive and is the method of choice for ultrasound imaging. Our design can potentially evolve into a valuable diagnostic tool, particularly for vascular-related disease.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (7)

J. Sauvage, M. Flesch, G. Ferin, A. Nguyen-Dinh, J. Poree, M. Tanter, M. Pernot, and T. Deffieux, “A large aperture row column addressed probe for in vivo 4d ultrafast doppler ultrasound imaging,” Phys. Medicine & Biol. 63, 215012 (2018).
[Crossref]

E. Merčep, X. L. Deán-Ben, and D. Razansky, “Imaging of blood flow and oxygen state with a multi-segment optoacoustic ultrasound array,” Photoacoustics 10, 48–53 (2018).
[Crossref]

A. Oraevsky, B. Clingman, J. Zalev, A. Stavros, W. Yang, and J. Parikh, “Clinical optoacoustic imaging combined with ultrasound for coregistered functional and anatomical mapping of breast tumors,” Photoacoustics 12, 30–45 (2018).
[Crossref] [PubMed]

M. W. Schellenberg and H. K. Hunt, “Hand-held optoacoustic imaging: A review,” Photoacoustics,  7, 1 (2018).
[Crossref] [PubMed]

B. Berthon, P. Morichau-Beauchant, J. Porée, A. Garofalakis, B. Tavitian, M. Tanter, and J. Provost, “Spatiotemporal matrix image formation for programmable ultrasound scanners,” Phys. Medicine & Biol. 63, 03NT03 (2018).
[Crossref]

M. A. Kalkhoran and D. Vray, “Theoretical characterization of annular array as a volumetric optoacoustic ultrasound handheld probe,” J. Biomed. Opt. 23, 025004 (2018).
[Crossref]

M. V. Zibetti, C. Lin, and G. T. Herman, “Total variation superiorized conjugate gradient method for image reconstruction,” Inverse Probl. 34, 034001 (2018).
[Crossref]

2017 (6)

J. Shin and L. Huang, “Spatial prediction filtering of acoustic clutter and random noise in medical ultrasound imaging,” IEEE Transactions on Med. Imaging 36, 396–406 (2017).
[Crossref]

B. Adcock, A. C. Hansen, C. Poon, and B. Roman, “Breaking the coherence barrier: A new theory for compressed sensing,” Forum Math. Sigma 5, 32 (2017).
[Crossref]

J. A. Guggenheim, J. Li, T. J. Allen, R. J. Colchester, S. Noimark, O. Ogunlade, I. P. Parkin, I. Papakonstantinou, A. E. Desjardins, E. Z. Zhang, and P. C. Beard, “Ultrasensitive plano-concave optical microresonators for ultrasound sensing,” Nat. Photonics 11, 714 (2017).
[Crossref]

M. Gesnik, K. Blaize, T. Deffieux, J.-L. Gennisson, J.-A. Sahel, M. Fink, S. Picaud, and M. Tanter, “3d functional ultrasound imaging of the cerebral visual system in rodents,” NeuroImage 149, 267–274 (2017).
[Crossref] [PubMed]

X. Deán-Ben, E. Merčep, and D. Razansky, “Hybrid-array-based optoacoustic and ultrasound (opus) imaging of biological tissues,” Appl. Phys. Lett. 110, 203703 (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, 3449–3457 (2017).
[Crossref] [PubMed]

2016 (8)

J. Kim, S. Park, Y. Jung, S. Chang, J. Park, Y. Zhang, J. F. Lovell, and C. Kim, “Programmable real-time clinical photoacoustic and ultrasound imaging system,” Sci. Reports 6, 35137 (2016).
[Crossref]

L. R. McNally, M. Mezera, D. E. Morgan, P. J. Frederick, E. S. Yang, I.-E. Eltoum, and W. E. Grizzle, “Current and emerging clinical applications of multispectral optoacoustic tomography (msot) in oncology,” Clin. Cancer Res. 22, 3432–3439 (2016).
[Crossref] [PubMed]

A. Taruttis, A. C. Timmermans, P. C. Wouters, M. Kacprowicz, G. M. van Dam, and V. Ntziachristos, “Optoacoustic imaging of human vasculature: feasibility by using a handheld probe,” Radiology 281, 256–263 (2016).
[Crossref] [PubMed]

A. Dima and V. Ntziachristos, “In-vivo handheld optoacoustic tomography of the human thyroid,” Photoacoustics 4, 65–69 (2016).
[Crossref] [PubMed]

P. J. van den Berg, R. Bansal, K. Daoudi, W. Steenbergen, and J. Prakash, “Preclinical detection of liver fibrosis using dual-modality photoacoustic/ultrasound system,” Biomed. Opt. Express 7, 5081–5091 (2016).
[Crossref] [PubMed]

M. U. Arabul, M. Heres, M. C. Rutten, M. R. van Sambeek, F. N. van de Vosse, and R. G. Lopata, “Toward the detection of intraplaque hemorrhage in carotid artery lesions using photoacoustic imaging,” J. Biomed. Opt. 22, 041010 (2016).
[Crossref]

G. Xu, Z.-x. Meng, J.-d. Lin, C. X. Deng, P. L. Carson, J. B. Fowlkes, C. Tao, X. Liu, and X. Wang, “High resolution physio-chemical tissue analysis: towards non-invasive in vivo biopsy,” Sci. Reports 6, 16937 (2016).
[Crossref]

E. Roux, A. Ramalli, P. Tortoli, C. Cachard, M. C. Robini, and H. Liebgott, “2-D ultrasound sparse arrays multidepth radiation optimization using simulated annealing and spiral-array inspired energy functions,” IEEE Transactions on Ultrason. Ferroelectr. Freq. Control. 63, 2138–2149 (2016).
[Crossref]

2015 (5)

P. Wong, I. Kosik, A. Raess, and J. J. Carson, “Objective assessment and design improvement of a staring, sparse transducer array by the spatial crosstalk matrix for 3d photoacoustic tomography,” PloS One 10, e0124759 (2015).
[Crossref] [PubMed]

C. Poon, “On the role of total variation in compressed sensing,” SIAM J. on Imaging Sci. 8, 682–720 (2015).
[Crossref]

A. Ramalli, E. Boni, A. S. Savoia, and P. Tortoli, “Density-tapered spiral arrays for ultrasound 3-d imaging,” IEEE Transactions on ultrasonics, ferroelectrics, and frequency control 62, 1580 (2015)

M. Schwarz, M. Omar, A. Buehler, J. Aguirre, and V. Ntziachristos, “Implications of ultrasound frequency in optoacoustic mesoscopy of the skin,” IEEE Transactions on Med. Imaging 34, 672–677 (2015).
[Crossref]

K. A. Mohan, S. Venkatakrishnan, J. W. Gibbs, E. B. Gulsoy, X. Xiao, M. De Graef, P. W. Voorhees, and C. A. Bouman, “Timbir: A method for time-space reconstruction from interlaced views,” IEEE Trans. Computat. Imaging 1, 96–111 (2015).
[Crossref]

2014 (3)

X. L. Deán-Ben and D. Razansky, “Adding fifth dimension to optoacoustic imaging: volumetric time-resolved spectrally enriched tomography,” Light. Sci. & Appl. 3, e137 (2014).
[Crossref]

F. Krahmer and R. Ward, “Stable and robust sampling strategies for compressive imaging,” IEEE Transactions on Image Process. 23, 612–622 (2014).
[Crossref]

J. Prakash, A. S. Raju, C. B. Shaw, M. Pramanik, and P. K. Yalavarthy, “Basis pursuit deconvolution for improving model-based reconstructed images in photoacoustic tomography,” Biomed. Opt. Express 5, 1363–1377 (2014).
[Crossref] [PubMed]

2013 (5)

E. J. Candes, C. A. Sing-Long, and J. D. Trzasko, “Unbiased risk estimates for singular value thresholding and spectral estimators,” IEEE Transactions on Signal Process. 61, 4643–4657 (2013).
[Crossref]

A. Rosenthal, V. Ntziachristos, and D. Razansky, “Acoustic inversion in optoacoustic tomography: A review,” Curr. Med. imaging reviews 9, 318–336 (2013).
[Crossref]

X. L. Deán-Ben and D. Razansky, “Portable spherical array probe for volumetric real-time optoacoustic imaging at centimeter-scale depths,” Opt. Express 21, 28062–28071 (2013).
[Crossref]

W. Xia, D. Piras, J. C. van Hespen, S. Van Veldhoven, C. Prins, T. G. van Leeuwen, W. Steenbergen, and S. Manohar, “An optimized ultrasound detector for photoacoustic breast tomography,” Med. Phys. 40, 032901 (2013).
[Crossref]

R. Leary, Z. Saghi, P. A. Midgley, and D. J. Holland, “Compressed sensing electron tomography,” Ultramicroscopy 131, 70–91 (2013).
[Crossref] [PubMed]

2012 (3)

F. Zhao, D. C. Noll, J.-F. Nielsen, and J. A. Fessler, “Separate magnitude and phase regularization via compressed sensing,” IEEE Transactions on Med. Imaging 31, 1713–1723 (2012).
[Crossref]

G. T. Herman, E. Garduño, R. Davidi, and Y. Censor, “Superiorization: An optimization heuristic for Med. Phys,” Med. Phys. 39, 5532–5546 (2012).
[Crossref] [PubMed]

D. Brunet, E. R. Vrscay, and Z. Wang, “On the mathematical properties of the structural similarity index,” IEEE Transactions on Image Process. 21, 1488–1499 (2012).
[Crossref]

2011 (2)

W. Lin and C.-C. J. Kuo, “Perceptual visual quality metrics: A survey,” J. Vis. Commun. Image Represent. 22, 297–312 (2011).
[Crossref]

E. Garduño, G. T. Herman, and R. Davidi, “Reconstruction from a few projections by l1-minimization of the haar transform,” Inverse Probl. 27, 055006 (2011).
[Crossref]

2010 (1)

Y. Censor, R. Davidi, and G. T. Herman, “Perturbation resilience and superiorization of iterative algorithms,” Inverse Probl. 26, 065008 (2010).
[Crossref]

2009 (3)

M. Pramanik, G. Ku, and L. V. Wang, “Tangential resolution improvement in thermoacoustic and photoacoustic tomography using a negative acoustic lens,” J. Biomed. Opt. 14, 024028 (2009).
[Crossref] [PubMed]

T. Goldstein and S. Osher, “The split bregman method for l1-regularized problems,” SIAM J. on Imaging Sci. 2, 323–343 (2009).
[Crossref]

P. Rodríguez and B. Wohlberg, “Efficient minimization method for a generalized total variation functional,” IEEE Transactions on Image Process. 18, 322–332 (2009).
[Crossref]

2006 (1)

E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. on Pure Appl. Math. A J. Issued by Courant Inst. Math. Sci. 59, 1207–1223 (2006).
[Crossref]

2004 (1)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

2003 (2)

M. Xu and L. V. Wang, “Analytic explanation of spatial resolution related to bandwidth and detector aperture size in thermoacoustic or photoacoustic reconstruction,” Phys. Rev. E 67, 056605 (2003).
[Crossref]

F. Lingvall, T. Olofsson, and T. Stepinski, “Synthetic aperture imaging using sources with finite aperture: Deconvolution of the spatial impulse response,” The J. Acoust. Soc. Am. 114, 225–234 (2003).
[Crossref] [PubMed]

2002 (1)

S. J. Norton, “Synthetic aperture imaging with arrays of arbitrary shape. ii. the annular array,” IEEE Transactions on Ultrason. Ferroelectr. Freq. Control. 49, 404–408 (2002).
[Crossref]

1998 (1)

P. C. Hansen, “Rank-deficient prewhitening with quotientsvd andulv decompositions,” BIT Numer. Math. 38, 34–43 (1998).
[Crossref]

1996 (1)

J. A. Fessler and W. L. Rogers, “Spatial resolution properties of penalized-likelihood image reconstruction: space-invariant tomographs,” IEEE Transactions on Image Processing 5, 1346–1358 (1996).
[Crossref] [PubMed]

1991 (1)

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L. R. McNally, M. Mezera, D. E. Morgan, P. J. Frederick, E. S. Yang, I.-E. Eltoum, and W. E. Grizzle, “Current and emerging clinical applications of multispectral optoacoustic tomography (msot) in oncology,” Clin. Cancer Res. 22, 3432–3439 (2016).
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Figures (4)

Fig. 1
Fig. 1 The geometrical distribution of the 128 elements (convex transducers). The recorded signal for the large elements equates to the ensemble signals recorded by λ/2 sub-elements of the same size, which constitutes the sampling pattern.
Fig. 2
Fig. 2 Evaluation of the B-mode performance of the three virtual arrays based on the contrast and resolution. I. The images of the anechoic cysts. II. The CNR value based on the white (cysts) and red (background) frames. III. The VE-WSAFT reconstructed B-mode images of the point reflectors. IV. The lateral profile of the reconstructed images for each of the arrays at the range of 20 mm.
Fig. 3
Fig. 3 Optoacoustic imaging performance of the three virtual arrays for retrieving the point sources (point spread function) situated in the axial plane at a depth of z = 20 mm, in the xy plane of 16 × 16 mm2 size. The reconstructed images used VE-DSBP (upper row) and model-based CGLS (lower row) with only 10 iterations (10 basis). The color bar is in dB.
Fig. 4
Fig. 4 Model-based reconstruction using CGLS (left) and CGLS TV (right) for the segmented annular array. Ten and fifty iterations were used correspondingly for the CGLS and CGLS TV algorithms.

Tables (1)

Tables Icon

Table 1 Quantitative evaluation of sampling patterns.

Equations (15)

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U r e c ( r , t ) = r F o V h A I R ( t ) * h S I R ( r , t ) * S ( r s r n ) ,
h I R = h A I R ( t ) * h S I R ( r s r n , t ) ,
U r e c n = M S k x , y , z ,
S ^ = arg  min z M . S U r e c   2 2
S ^ = M . U r e c
TSVD : W κ = span { v 1 , v 2 , , v κ }
S ( κ ) = W κ z ( κ )
z ( κ ) = arg  min z ( M W κ ) z U r e c   2
Krylov : K κ = s p a n { M T U r e c , ( M T M ) M T U r e c , , ( M T M ) κ 1 M T U r e c } ,
TV ( S ) = | S | ϵ   1 = i ( Δ i x S ) 2 + ( Δ i y S ) 2 + ϵ 2
z ( κ ) = arg  min z ( M W κ ) z U rec   2 + λ T V TV ( z )
M T M S + λ T V T V ( S ) = M T U r e c
CNR = S i S o σ i 2 + σ o 2
PSNR ( f , g ) = 20 log 10 MAX I 1 MN i = 1 M j = 1 N ( f ij g ij ) 2
SSIM ( f , g ) = ( 2 μ f μ g + c 1 ) . ( 2 σ f , g + c 2 ) ( μ f 2 + μ g 2 + c 1 ) . ( σ f 2 + σ g 2 + c 2 )

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