Abstract

Ultrasound (US)-guided diffuse optical tomography (DOT) is a promising non-invasive functional imaging technique for diagnosing breast cancer and monitoring breast cancer treatment response. However, because larger lesions are highly absorbing, reconstructions of these lesions using reflection geometry may exhibit light shadowing, which leads to inaccurate quantification of their deeper portions. Here we propose a depth-regularized reconstruction algorithm combined with a semi-automated interactive neural network (CNN) for depth-dependent reconstruction of absorption distribution. CNN segments co-registered US to extract both spatial and depth priors, and the depth-regularized algorithm incorporates these parameters into the reconstruction. Through simulation and phantom data, the proposed algorithm is shown to significantly improve the depth distribution of reconstructed absorption maps of large targets. Evaluated with 26 patients with larger breast lesions, the algorithm shows 2.4 to 3 times improvement in the top-to-bottom reconstructed homogeneity of the absorption maps for these lesions.

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

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    [Crossref]

2019 (1)

2018 (4)

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
[Crossref]

W. Lu, D. Lighter, and I. B. Styles, “L1-norm based nonlinear reconstruction improves quantitative accuracy of spectral diffuse optical tomography,” Biomed. Opt. Express 9(4), 1423–1444 (2018).
[Crossref]

M. H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, A. K. Davison, and R. Martí, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEE J. Biomed. Health Inform 22(4), 1218–1226 (2018).
[Crossref]

L. Zhang, S. Jiang, Y. Zhao, J. Feng, B. W. Pogue, and K. D. Paulsen, “Direct Regularization From Co-Registered Contrast MRI Improves Image Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast Lesions,” IEEE Trans. Med. Imaging 37(5), 1247–1252 (2018).
[Crossref]

2017 (5)

K. S. Uddin, A. Mostafa, M. Anastasio, and Q. Zhu, “Two-step imaging reconstruction using truncated pseudoinverse as a preliminary estimate in ultrasound guided diffuse optical tomography,” Biomed. Opt. Express 8(12), 5437–5449 (2017).
[Crossref]

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

M. Althobaiti, H. Vavadi, and Q. Zhu, “Diffuse optical tomography reconstruction method using ultrasound images as prior for regularization matrix,” J. Biomed. Opt. 22(2), 026002 (2017).
[Crossref]

Q. Huang, Y. Luo, and Q. Zhang, “Breast ultrasound image segmentation: a survey,” Int. J. CARS 12(3), 493–507 (2017).
[Crossref]

2016 (2)

Q. Zhu, A. Ricci Jr, P. Hegde, M. Kane, E. Cronin, A. Merkulov, Y. Xu, B. Tavakoli, and S. Tannenbaum, “Assessment of functional differences in malignant and benign breast lesions and improvement of diagnostic accuracy by using us-guided diffuse optical tomography in conjunction with conventional us,” Radiology 280(2), 387–397 (2016).
[Crossref]

D. Grosenick, H. Rinneberg, R. Cubeddu, and P. Taroni, “Review of optical breast imaging and spectroscopy,” J. Biomed. Opt. 21(9), 091311 (2016).
[Crossref]

2012 (1)

2011 (1)

Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and X-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
[Crossref]

2010 (2)

C. Xu and Q. Zhu, “Light shadowing effect of large breast lesions imaged by optical tomography in reflection geometry,” J. Biomed. Opt. 15(3), 036003 (2010).
[Crossref]

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

2009 (2)

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci. 2(1), 183–202 (2009).
[Crossref]

A. Zacharopoulos, M. Schweiger, V. Kolehmainen, and S. Arridge, “3d shape based reconstruction of experimental data in diffuse optical tomography,” Opt. Express 17(21), 18940–18956 (2009).
[Crossref]

2008 (1)

B. J. Tromberg, B. W. Pogue, K. D. Paulsen, A. G. Yodh, D. A. Boas, and A. E. Cerussi, “Assessing the future of diffuse optical imaging technologies for breast cancer management,” Med. Phys. 35(6), 2443–2451 (2008).
[Crossref]

2007 (2)

2006 (1)

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

2005 (2)

Q. Zhu, E. B. Cronin, A. A. Currier, H. S. Vine, M. Huang, N. Chen, and C. Xu, “Benign versus malignant breast masses: optical differentiation with us-guided optical imaging reconstruction,” Radiology 237(1), 57–66 (2005).
[Crossref]

B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. B. Weaver, C. Kogel, and S. P. Poplack, “Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure,” J. Biomed. Opt. 10(5), 051504 (2005).
[Crossref]

2003 (1)

2001 (1)

D. A. Boas, D. H. Brooks, E. L. Miller, C. A. DiMarzio, M. Kilmer, R. J. Gaudette, and Q. Zhang, “Imaging the body with diffuse optical tomography,” IEEE Signal Process. Mag. 18(6), 57–75 (2001).
[Crossref]

Ademuyiwa, F.

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
[Crossref]

Adibi, A.

Aguirre, A.

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

Althobaiti, M.

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
[Crossref]

M. Althobaiti, H. Vavadi, and Q. Zhu, “Diffuse optical tomography reconstruction method using ultrasound images as prior for regularization matrix,” J. Biomed. Opt. 22(2), 026002 (2017).
[Crossref]

Anastasio, M.

Ardeshirpour, Y.

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

Arridge, S.

Bai, J.

Bansal, R.

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
[Crossref]

Beck, A.

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci. 2(1), 183–202 (2009).
[Crossref]

Boas, D. A.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and X-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
[Crossref]

B. J. Tromberg, B. W. Pogue, K. D. Paulsen, A. G. Yodh, D. A. Boas, and A. E. Cerussi, “Assessing the future of diffuse optical imaging technologies for breast cancer management,” Med. Phys. 35(6), 2443–2451 (2008).
[Crossref]

D. A. Boas, D. H. Brooks, E. L. Miller, C. A. DiMarzio, M. Kilmer, R. J. Gaudette, and Q. Zhang, “Imaging the body with diffuse optical tomography,” IEEE Signal Process. Mag. 18(6), 57–75 (2001).
[Crossref]

Boverman, G.

Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and X-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
[Crossref]

Brooks, D. H.

Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and X-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
[Crossref]

D. A. Boas, D. H. Brooks, E. L. Miller, C. A. DiMarzio, M. Kilmer, R. J. Gaudette, and Q. Zhang, “Imaging the body with diffuse optical tomography,” IEEE Signal Process. Mag. 18(6), 57–75 (2001).
[Crossref]

Brooksby, B.

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. B. Weaver, C. Kogel, and S. P. Poplack, “Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure,” J. Biomed. Opt. 10(5), 051504 (2005).
[Crossref]

Caelles, S.

K.-K. Maninis, S. Caelles, J. Pont-Tuset, and L. Van Gool, “Deep extreme cut: From extreme points to object segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018), pp. 616–625.

Carp, S. A.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and X-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
[Crossref]

Carpenter, C. M.

Cerussi, A. E.

B. J. Tromberg, B. W. Pogue, K. D. Paulsen, A. G. Yodh, D. A. Boas, and A. E. Cerussi, “Assessing the future of diffuse optical imaging technologies for breast cancer management,” Med. Phys. 35(6), 2443–2451 (2008).
[Crossref]

Chen, N.

Q. Zhu, E. B. Cronin, A. A. Currier, H. S. Vine, M. Huang, N. Chen, and C. Xu, “Benign versus malignant breast masses: optical differentiation with us-guided optical imaging reconstruction,” Radiology 237(1), 57–66 (2005).
[Crossref]

Q. Zhu, N. Chen, and S. H. Kurtzman, “Imaging tumor angiogenesis by use of combined near-infrared diffusive light and ultrasound,” Opt. Lett. 28(5), 337–339 (2003).
[Crossref]

Cormier, J.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Cronin, E.

Q. Zhu, A. Ricci Jr, P. Hegde, M. Kane, E. Cronin, A. Merkulov, Y. Xu, B. Tavakoli, and S. Tannenbaum, “Assessment of functional differences in malignant and benign breast lesions and improvement of diagnostic accuracy by using us-guided diffuse optical tomography in conjunction with conventional us,” Radiology 280(2), 387–397 (2016).
[Crossref]

Cronin, E. B.

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

Q. Zhu, E. B. Cronin, A. A. Currier, H. S. Vine, M. Huang, N. Chen, and C. Xu, “Benign versus malignant breast masses: optical differentiation with us-guided optical imaging reconstruction,” Radiology 237(1), 57–66 (2005).
[Crossref]

Cubeddu, R.

D. Grosenick, H. Rinneberg, R. Cubeddu, and P. Taroni, “Review of optical breast imaging and spectroscopy,” J. Biomed. Opt. 21(9), 091311 (2016).
[Crossref]

Currier, A. A.

Q. Zhu, E. B. Cronin, A. A. Currier, H. S. Vine, M. Huang, N. Chen, and C. Xu, “Benign versus malignant breast masses: optical differentiation with us-guided optical imaging reconstruction,” Radiology 237(1), 57–66 (2005).
[Crossref]

Davison, A. K.

M. H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, A. K. Davison, and R. Martí, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEE J. Biomed. Health Inform 22(4), 1218–1226 (2018).
[Crossref]

Deckers, P. J.

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

Dehghani, H.

P. K. Yalavarthy, B. W. Pogue, H. Dehghani, C. M. Carpenter, S. Jiang, and K. D. Paulsen, “Structural information within regularization matrices improves near infrared diffuse optical tomography,” Opt. Express 15(13), 8043–8058 (2007).
[Crossref]

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. B. Weaver, C. Kogel, and S. P. Poplack, “Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure,” J. Biomed. Opt. 10(5), 051504 (2005).
[Crossref]

Deng, B.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

DiMarzio, C. A.

D. A. Boas, D. H. Brooks, E. L. Miller, C. A. DiMarzio, M. Kilmer, R. J. Gaudette, and Q. Zhang, “Imaging the body with diffuse optical tomography,” IEEE Signal Process. Mag. 18(6), 57–75 (2001).
[Crossref]

Eftekhar, A. A.

Fang, Q.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and X-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
[Crossref]

Feng, J.

L. Zhang, S. Jiang, Y. Zhao, J. Feng, B. W. Pogue, and K. D. Paulsen, “Direct Regularization From Co-Registered Contrast MRI Improves Image Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast Lesions,” IEEE Trans. Med. Imaging 37(5), 1247–1252 (2018).
[Crossref]

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

Ganau, S.

M. H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, A. K. Davison, and R. Martí, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEE J. Biomed. Health Inform 22(4), 1218–1226 (2018).
[Crossref]

Gaudette, R. J.

D. A. Boas, D. H. Brooks, E. L. Miller, C. A. DiMarzio, M. Kilmer, R. J. Gaudette, and Q. Zhang, “Imaging the body with diffuse optical tomography,” IEEE Signal Process. Mag. 18(6), 57–75 (2001).
[Crossref]

Grosenick, D.

D. Grosenick, H. Rinneberg, R. Cubeddu, and P. Taroni, “Review of optical breast imaging and spectroscopy,” J. Biomed. Opt. 21(9), 091311 (2016).
[Crossref]

Gui, J.

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

Hegde, P.

Q. Zhu, A. Ricci Jr, P. Hegde, M. Kane, E. Cronin, A. Merkulov, Y. Xu, B. Tavakoli, and S. Tannenbaum, “Assessment of functional differences in malignant and benign breast lesions and improvement of diagnostic accuracy by using us-guided diffuse optical tomography in conjunction with conventional us,” Radiology 280(2), 387–397 (2016).
[Crossref]

Hegde, P. U.

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

Huang, J.

Huang, M.

Q. Zhu, E. B. Cronin, A. A. Currier, H. S. Vine, M. Huang, N. Chen, and C. Xu, “Benign versus malignant breast masses: optical differentiation with us-guided optical imaging reconstruction,” Radiology 237(1), 57–66 (2005).
[Crossref]

Huang, Q.

Q. Huang, Y. Luo, and Q. Zhang, “Breast ultrasound image segmentation: a survey,” Int. J. CARS 12(3), 493–507 (2017).
[Crossref]

Jiang, S.

L. Zhang, S. Jiang, Y. Zhao, J. Feng, B. W. Pogue, and K. D. Paulsen, “Direct Regularization From Co-Registered Contrast MRI Improves Image Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast Lesions,” IEEE Trans. Med. Imaging 37(5), 1247–1252 (2018).
[Crossref]

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

P. K. Yalavarthy, B. W. Pogue, H. Dehghani, C. M. Carpenter, S. Jiang, and K. D. Paulsen, “Structural information within regularization matrices improves near infrared diffuse optical tomography,” Opt. Express 15(13), 8043–8058 (2007).
[Crossref]

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. B. Weaver, C. Kogel, and S. P. Poplack, “Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure,” J. Biomed. Opt. 10(5), 051504 (2005).
[Crossref]

Kane, M.

Q. Zhu, A. Ricci Jr, P. Hegde, M. Kane, E. Cronin, A. Merkulov, Y. Xu, B. Tavakoli, and S. Tannenbaum, “Assessment of functional differences in malignant and benign breast lesions and improvement of diagnostic accuracy by using us-guided diffuse optical tomography in conjunction with conventional us,” Radiology 280(2), 387–397 (2016).
[Crossref]

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

Kilmer, M.

D. A. Boas, D. H. Brooks, E. L. Miller, C. A. DiMarzio, M. Kilmer, R. J. Gaudette, and Q. Zhang, “Imaging the body with diffuse optical tomography,” IEEE Signal Process. Mag. 18(6), 57–75 (2001).
[Crossref]

Kogel, C.

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. B. Weaver, C. Kogel, and S. P. Poplack, “Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure,” J. Biomed. Opt. 10(5), 051504 (2005).
[Crossref]

Kolehmainen, V.

Kopans, D. B.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and X-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
[Crossref]

Kurtzman, S. H.

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

Q. Zhu, N. Chen, and S. H. Kurtzman, “Imaging tumor angiogenesis by use of combined near-infrared diffusive light and ultrasound,” Opt. Lett. 28(5), 337–339 (2003).
[Crossref]

Li, M.

Lighter, D.

Liu, F.

Lu, W.

Luo, J.

Luo, Y.

Q. Huang, Y. Luo, and Q. Zhang, “Breast ultrasound image segmentation: a survey,” Int. J. CARS 12(3), 493–507 (2017).
[Crossref]

Lv, X.

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

Maninis, K.-K.

K.-K. Maninis, S. Caelles, J. Pont-Tuset, and L. Van Gool, “Deep extreme cut: From extreme points to object segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018), pp. 616–625.

Martí, J.

M. H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, A. K. Davison, and R. Martí, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEE J. Biomed. Health Inform 22(4), 1218–1226 (2018).
[Crossref]

Martí, R.

M. H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, A. K. Davison, and R. Martí, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEE J. Biomed. Health Inform 22(4), 1218–1226 (2018).
[Crossref]

Martino, M.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Merkulov, A.

Q. Zhu, A. Ricci Jr, P. Hegde, M. Kane, E. Cronin, A. Merkulov, Y. Xu, B. Tavakoli, and S. Tannenbaum, “Assessment of functional differences in malignant and benign breast lesions and improvement of diagnostic accuracy by using us-guided diffuse optical tomography in conjunction with conventional us,” Radiology 280(2), 387–397 (2016).
[Crossref]

Miller, E. L.

Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and X-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
[Crossref]

D. A. Boas, D. H. Brooks, E. L. Miller, C. A. DiMarzio, M. Kilmer, R. J. Gaudette, and Q. Zhang, “Imaging the body with diffuse optical tomography,” IEEE Signal Process. Mag. 18(6), 57–75 (2001).
[Crossref]

Mohajerani, P.

Moore, R. H.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and X-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
[Crossref]

Mostafa, A.

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
[Crossref]

K. S. Uddin, A. Mostafa, M. Anastasio, and Q. Zhu, “Two-step imaging reconstruction using truncated pseudoinverse as a preliminary estimate in ultrasound guided diffuse optical tomography,” Biomed. Opt. Express 8(12), 5437–5449 (2017).
[Crossref]

Paulsen, K. D.

L. Zhang, S. Jiang, Y. Zhao, J. Feng, B. W. Pogue, and K. D. Paulsen, “Direct Regularization From Co-Registered Contrast MRI Improves Image Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast Lesions,” IEEE Trans. Med. Imaging 37(5), 1247–1252 (2018).
[Crossref]

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

B. J. Tromberg, B. W. Pogue, K. D. Paulsen, A. G. Yodh, D. A. Boas, and A. E. Cerussi, “Assessing the future of diffuse optical imaging technologies for breast cancer management,” Med. Phys. 35(6), 2443–2451 (2008).
[Crossref]

P. K. Yalavarthy, B. W. Pogue, H. Dehghani, C. M. Carpenter, S. Jiang, and K. D. Paulsen, “Structural information within regularization matrices improves near infrared diffuse optical tomography,” Opt. Express 15(13), 8043–8058 (2007).
[Crossref]

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. B. Weaver, C. Kogel, and S. P. Poplack, “Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure,” J. Biomed. Opt. 10(5), 051504 (2005).
[Crossref]

Pogue, B. W.

L. Zhang, S. Jiang, Y. Zhao, J. Feng, B. W. Pogue, and K. D. Paulsen, “Direct Regularization From Co-Registered Contrast MRI Improves Image Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast Lesions,” IEEE Trans. Med. Imaging 37(5), 1247–1252 (2018).
[Crossref]

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

B. J. Tromberg, B. W. Pogue, K. D. Paulsen, A. G. Yodh, D. A. Boas, and A. E. Cerussi, “Assessing the future of diffuse optical imaging technologies for breast cancer management,” Med. Phys. 35(6), 2443–2451 (2008).
[Crossref]

P. K. Yalavarthy, B. W. Pogue, H. Dehghani, C. M. Carpenter, S. Jiang, and K. D. Paulsen, “Structural information within regularization matrices improves near infrared diffuse optical tomography,” Opt. Express 15(13), 8043–8058 (2007).
[Crossref]

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. B. Weaver, C. Kogel, and S. P. Poplack, “Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure,” J. Biomed. Opt. 10(5), 051504 (2005).
[Crossref]

Pons, G.

M. H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, A. K. Davison, and R. Martí, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEE J. Biomed. Health Inform 22(4), 1218–1226 (2018).
[Crossref]

Pont-Tuset, J.

K.-K. Maninis, S. Caelles, J. Pont-Tuset, and L. Van Gool, “Deep extreme cut: From extreme points to object segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018), pp. 616–625.

Poplack, S.

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
[Crossref]

Poplack, S. P.

B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. B. Weaver, C. Kogel, and S. P. Poplack, “Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure,” J. Biomed. Opt. 10(5), 051504 (2005).
[Crossref]

Poplack SP, S. P.

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

Ren, F.

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

Ricci Jr, A.

Q. Zhu, A. Ricci Jr, P. Hegde, M. Kane, E. Cronin, A. Merkulov, Y. Xu, B. Tavakoli, and S. Tannenbaum, “Assessment of functional differences in malignant and benign breast lesions and improvement of diagnostic accuracy by using us-guided diffuse optical tomography in conjunction with conventional us,” Radiology 280(2), 387–397 (2016).
[Crossref]

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

Rinneberg, H.

D. Grosenick, H. Rinneberg, R. Cubeddu, and P. Taroni, “Review of optical breast imaging and spectroscopy,” J. Biomed. Opt. 21(9), 091311 (2016).
[Crossref]

Sajjadi, A. Y.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Saksena, M. A.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Schweiger, M.

Selb, J.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and X-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
[Crossref]

Sentís, M.

M. H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, A. K. Davison, and R. Martí, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEE J. Biomed. Health Inform 22(4), 1218–1226 (2018).
[Crossref]

Singh, B.

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Srinivasan, S.

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

Styles, I. B.

Tannenbaum, S.

Q. Zhu, A. Ricci Jr, P. Hegde, M. Kane, E. Cronin, A. Merkulov, Y. Xu, B. Tavakoli, and S. Tannenbaum, “Assessment of functional differences in malignant and benign breast lesions and improvement of diagnostic accuracy by using us-guided diffuse optical tomography in conjunction with conventional us,” Radiology 280(2), 387–397 (2016).
[Crossref]

Taroni, P.

D. Grosenick, H. Rinneberg, R. Cubeddu, and P. Taroni, “Review of optical breast imaging and spectroscopy,” J. Biomed. Opt. 21(9), 091311 (2016).
[Crossref]

Tavakoli, B.

Q. Zhu, A. Ricci Jr, P. Hegde, M. Kane, E. Cronin, A. Merkulov, Y. Xu, B. Tavakoli, and S. Tannenbaum, “Assessment of functional differences in malignant and benign breast lesions and improvement of diagnostic accuracy by using us-guided diffuse optical tomography in conjunction with conventional us,” Radiology 280(2), 387–397 (2016).
[Crossref]

Teboulle, M.

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci. 2(1), 183–202 (2009).
[Crossref]

Tosteson, T. D.

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

Tromberg, B. J.

B. J. Tromberg, B. W. Pogue, K. D. Paulsen, A. G. Yodh, D. A. Boas, and A. E. Cerussi, “Assessing the future of diffuse optical imaging technologies for breast cancer management,” Med. Phys. 35(6), 2443–2451 (2008).
[Crossref]

Uddin, K. M. S.

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
[Crossref]

Uddin, K. S.

Van Gool, L.

K.-K. Maninis, S. Caelles, J. Pont-Tuset, and L. Van Gool, “Deep extreme cut: From extreme points to object segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018), pp. 616–625.

Vavadi, H.

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
[Crossref]

M. Althobaiti, H. Vavadi, and Q. Zhu, “Diffuse optical tomography reconstruction method using ultrasound images as prior for regularization matrix,” J. Biomed. Opt. 22(2), 026002 (2017).
[Crossref]

Vine, H. S.

Q. Zhu, E. B. Cronin, A. A. Currier, H. S. Vine, M. Huang, N. Chen, and C. Xu, “Benign versus malignant breast masses: optical differentiation with us-guided optical imaging reconstruction,” Radiology 237(1), 57–66 (2005).
[Crossref]

Wang, K.

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

Weaver, J.

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

Weaver, J. B.

B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. B. Weaver, C. Kogel, and S. P. Poplack, “Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure,” J. Biomed. Opt. 10(5), 051504 (2005).
[Crossref]

Xu, C.

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
[Crossref]

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

C. Xu and Q. Zhu, “Light shadowing effect of large breast lesions imaged by optical tomography in reflection geometry,” J. Biomed. Opt. 15(3), 036003 (2010).
[Crossref]

Q. Zhu, E. B. Cronin, A. A. Currier, H. S. Vine, M. Huang, N. Chen, and C. Xu, “Benign versus malignant breast masses: optical differentiation with us-guided optical imaging reconstruction,” Radiology 237(1), 57–66 (2005).
[Crossref]

Xu, J.

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

Xu, S.

Xu, Y.

Q. Zhu, A. Ricci Jr, P. Hegde, M. Kane, E. Cronin, A. Merkulov, Y. Xu, B. Tavakoli, and S. Tannenbaum, “Assessment of functional differences in malignant and benign breast lesions and improvement of diagnostic accuracy by using us-guided diffuse optical tomography in conjunction with conventional us,” Radiology 280(2), 387–397 (2016).
[Crossref]

Yalavarthy, P. K.

Yap, M. H.

M. H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, A. K. Davison, and R. Martí, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEE J. Biomed. Health Inform 22(4), 1218–1226 (2018).
[Crossref]

Yin, H.

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

Yodh, A. G.

B. J. Tromberg, B. W. Pogue, K. D. Paulsen, A. G. Yodh, D. A. Boas, and A. E. Cerussi, “Assessing the future of diffuse optical imaging technologies for breast cancer management,” Med. Phys. 35(6), 2443–2451 (2008).
[Crossref]

Zacharopoulos, A.

Zhang, B.

Zhang, L.

L. Zhang, S. Jiang, Y. Zhao, J. Feng, B. W. Pogue, and K. D. Paulsen, “Direct Regularization From Co-Registered Contrast MRI Improves Image Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast Lesions,” IEEE Trans. Med. Imaging 37(5), 1247–1252 (2018).
[Crossref]

Zhang, Q.

Q. Huang, Y. Luo, and Q. Zhang, “Breast ultrasound image segmentation: a survey,” Int. J. CARS 12(3), 493–507 (2017).
[Crossref]

D. A. Boas, D. H. Brooks, E. L. Miller, C. A. DiMarzio, M. Kilmer, R. J. Gaudette, and Q. Zhang, “Imaging the body with diffuse optical tomography,” IEEE Signal Process. Mag. 18(6), 57–75 (2001).
[Crossref]

Zhao, Y.

L. Zhang, S. Jiang, Y. Zhao, J. Feng, B. W. Pogue, and K. D. Paulsen, “Direct Regularization From Co-Registered Contrast MRI Improves Image Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast Lesions,” IEEE Trans. Med. Imaging 37(5), 1247–1252 (2018).
[Crossref]

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

Zhou, F.

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
[Crossref]

Zhu, Q.

S. Xu, K. S. Uddin, and Q. Zhu, “Improving DOT reconstruction with a Born iterative method and US-guided sparse regularization,” Biomed. Opt. Express 10(5), 2528–2541 (2019).
[Crossref]

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
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K. S. Uddin, A. Mostafa, M. Anastasio, and Q. Zhu, “Two-step imaging reconstruction using truncated pseudoinverse as a preliminary estimate in ultrasound guided diffuse optical tomography,” Biomed. Opt. Express 8(12), 5437–5449 (2017).
[Crossref]

M. Althobaiti, H. Vavadi, and Q. Zhu, “Diffuse optical tomography reconstruction method using ultrasound images as prior for regularization matrix,” J. Biomed. Opt. 22(2), 026002 (2017).
[Crossref]

Q. Zhu, A. Ricci Jr, P. Hegde, M. Kane, E. Cronin, A. Merkulov, Y. Xu, B. Tavakoli, and S. Tannenbaum, “Assessment of functional differences in malignant and benign breast lesions and improvement of diagnostic accuracy by using us-guided diffuse optical tomography in conjunction with conventional us,” Radiology 280(2), 387–397 (2016).
[Crossref]

C. Xu and Q. Zhu, “Light shadowing effect of large breast lesions imaged by optical tomography in reflection geometry,” J. Biomed. Opt. 15(3), 036003 (2010).
[Crossref]

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

Q. Zhu, E. B. Cronin, A. A. Currier, H. S. Vine, M. Huang, N. Chen, and C. Xu, “Benign versus malignant breast masses: optical differentiation with us-guided optical imaging reconstruction,” Radiology 237(1), 57–66 (2005).
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B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

Zwiggelaar, R.

M. H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, A. K. Davison, and R. Martí, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEE J. Biomed. Health Inform 22(4), 1218–1226 (2018).
[Crossref]

Appl. Opt. (2)

Biomed. Opt. Express (3)

Breast Cancer Res. (1)

J. Feng, J. Xu, S. Jiang, H. Yin, Y. Zhao, J. Gui, K. Wang, X. Lv, F. Ren, B. W. Pogue, and K. D. Paulsen, “Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis,” Breast Cancer Res. 19(1), 117 (2017).
[Crossref]

IEEE J. Biomed. Health Inform (1)

M. H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, A. K. Davison, and R. Martí, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEE J. Biomed. Health Inform 22(4), 1218–1226 (2018).
[Crossref]

IEEE Signal Process. Mag. (1)

D. A. Boas, D. H. Brooks, E. L. Miller, C. A. DiMarzio, M. Kilmer, R. J. Gaudette, and Q. Zhang, “Imaging the body with diffuse optical tomography,” IEEE Signal Process. Mag. 18(6), 57–75 (2001).
[Crossref]

IEEE Trans. Med. Imaging (1)

L. Zhang, S. Jiang, Y. Zhao, J. Feng, B. W. Pogue, and K. D. Paulsen, “Direct Regularization From Co-Registered Contrast MRI Improves Image Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast Lesions,” IEEE Trans. Med. Imaging 37(5), 1247–1252 (2018).
[Crossref]

Int. J. CARS (1)

Q. Huang, Y. Luo, and Q. Zhang, “Breast ultrasound image segmentation: a survey,” Int. J. CARS 12(3), 493–507 (2017).
[Crossref]

J. Biomed. Opt. (6)

C. Xu and Q. Zhu, “Light shadowing effect of large breast lesions imaged by optical tomography in reflection geometry,” J. Biomed. Opt. 15(3), 036003 (2010).
[Crossref]

M. Althobaiti, H. Vavadi, and Q. Zhu, “Diffuse optical tomography reconstruction method using ultrasound images as prior for regularization matrix,” J. Biomed. Opt. 22(2), 026002 (2017).
[Crossref]

B. Brooksby, S. Jiang, H. Dehghani, B. W. Pogue, K. D. Paulsen, J. B. Weaver, C. Kogel, and S. P. Poplack, “Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure,” J. Biomed. Opt. 10(5), 051504 (2005).
[Crossref]

B. B. Zimmermann, B. Deng, B. Singh, M. Martino, J. Selb, Q. Fang, A. Y. Sajjadi, J. Cormier, R. H. Moore, D. B. Kopans, D. A. Boas, M. A. Saksena, and S. A. Carp, “Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis,” J. Biomed. Opt. 22(4), 046008 (2017).
[Crossref]

D. Grosenick, H. Rinneberg, R. Cubeddu, and P. Taroni, “Review of optical breast imaging and spectroscopy,” J. Biomed. Opt. 21(9), 091311 (2016).
[Crossref]

H. Vavadi, A. Mostafa, F. Zhou, K. M. S. Uddin, M. Althobaiti, C. Xu, R. Bansal, F. Ademuyiwa, S. Poplack, and Q. Zhu, “Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging,” J. Biomed. Opt. 24(2), 1–9 (2018).
[Crossref]

Med. Phys. (1)

B. J. Tromberg, B. W. Pogue, K. D. Paulsen, A. G. Yodh, D. A. Boas, and A. E. Cerussi, “Assessing the future of diffuse optical imaging technologies for breast cancer management,” Med. Phys. 35(6), 2443–2451 (2008).
[Crossref]

Opt. Express (2)

Opt. Lett. (1)

Proc. Natl. Acad. Sci. U. S. A. (1)

B. Brooksby, B. W. Pogue, S. Jiang, H. Dehghani, S. Srinivasan, C. Kogel, T. D. Tosteson, J. Weaver, S. P. Poplack SP, and K. D. Paulsen, “Imaging breast adipose and fibroglandular tissue molecular signatures by using hybrid MRI-guided near-infrared spectral tomography,” Proc. Natl. Acad. Sci. U. S. A. 103(23), 8828–8833 (2006).
[Crossref]

Radiology (4)

Q. Zhu, E. B. Cronin, A. A. Currier, H. S. Vine, M. Huang, N. Chen, and C. Xu, “Benign versus malignant breast masses: optical differentiation with us-guided optical imaging reconstruction,” Radiology 237(1), 57–66 (2005).
[Crossref]

Q. Zhu, P. U. Hegde, A. Ricci Jr, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, and P. J. Deckers, “Early-stage invasive breast cancers: potential role of optical tomography with us localization in assisting diagnosis,” Radiology 256(2), 367–378 (2010).
[Crossref]

Q. Zhu, A. Ricci Jr, P. Hegde, M. Kane, E. Cronin, A. Merkulov, Y. Xu, B. Tavakoli, and S. Tannenbaum, “Assessment of functional differences in malignant and benign breast lesions and improvement of diagnostic accuracy by using us-guided diffuse optical tomography in conjunction with conventional us,” Radiology 280(2), 387–397 (2016).
[Crossref]

Q. Fang, J. Selb, S. A. Carp, G. Boverman, E. L. Miller, D. H. Brooks, R. H. Moore, D. B. Kopans, and D. A. Boas, “Combined optical and X-ray tomosynthesis breast imaging,” Radiology 258(1), 89–97 (2011).
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SIAM J. Imaging Sci. (1)

A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci. 2(1), 183–202 (2009).
[Crossref]

Other (1)

K.-K. Maninis, S. Caelles, J. Pont-Tuset, and L. Van Gool, “Deep extreme cut: From extreme points to object segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018), pp. 616–625.

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Figures (10)

Fig. 1.
Fig. 1. (a) US image of a breast tumor with manually marked lesion boundaries. (b) US image segmentation using the semi-automated interactive CNN model.
Fig. 2.
Fig. 2. (a), (b), and (c) are cross section illustrations of three differently shaped simulated targets. In each target, two cylinders with the same optical properties are concentrically stacked. (d), (f), and (h) are reconstructed images from the ${\sigma _1}$-regularized reconstruction. Only the top layer of the target is resolved for all three shapes. (e), (g), and (i) are reconstructed images from the proposed depth-regularized algorithm. Under conditions (a) and (b), both targets are resolved at the deeper layer. The ratio R = 1.32 in (e) and 1.65 in (g). For target shape (c), the top portion absorbed most of the light and the deeper target portion could not be resolved. Each image slice is 9 cm by 9 cm in spatial dimensions and the depth between slices is 0.5 cm. The absorption coefficient is cm−1 with range 0 to 0.2 cm−1.
Fig. 3.
Fig. 3. (a), (b), and (c) are cross sections of three differently shaped simulated targets. For all three shapes, the ellipsoid fitting of the target is marked in the red. (d), (f), and (h) are reconstructed images from the depth-regularized algorithm with ellipsoidal fitting, with the ratio R = 2.45 in (d) and R = 1.59 in (f). (e), (g), and (i) are reconstructed images from the proposed algorithm with the actual target size, with the ratio R = 1.32 in (e) and R = 1.65 in (g). For shape (c), both methods failed to resolve the bottom portion.
Fig. 4.
Fig. 4. (a) Co-registered US image, a solid ball phantom 2 cm below the surface. (b) Reconstructed images from the ${\sigma _1}$-regularized algorithm, with a maximum reconstructed $\; {\mu _a} = 0.214\; c{m^{ - 1}}$. Only the target top-layer was resolved in depth, which leads to an infinite top-to-bottom ratio. (c) Reconstructed images from the proposed depth-regularized algorithm, with a maximum reconstructed $\; {\mu _a} = 0.204c{m^{ - 1}}$. The proposed algorithm resolves both target layers, which is closer to the phantom shape and has a R = 1.071 top-to-bottom ratio. Note that each image slice is 9 cm by 9 cm. The depth spacing between layers is 0.5 cm, and the absorption coefficient is measured in cm −1. Note that the target is marked by a circle, and some sound reverberations from the hard resin ball are shown outside the circle.
Fig. 5.
Fig. 5. Segmented US image and reconstructed absorption maps of a stage 2 cancer at 780 nm. (a) US image from a co-registered commercial US system. (b) Segmented US image from CNN. (c) Reconstructed absorption map from the ${\sigma _1}$ -regularized algorithm with a maximum $\; {\mu _a} = 0.307\; c{m^{ - 1}}$and infinite R. (d) Reconstructed absorption map from the depth-regularized algorithm with a maximum $\; {\mu _a} = 0.299\; c{m^{ - 1}}$ and R = 1.57. (c) and (d) consist of 7 sub-images, each sub-image showing one 9 cm by 9 cm cross section of the reconstructed absorption map from 0.5 cm to 3.5 cm in 0.5 cm spacing below tissue surface. The absorption coefficient in cm−1 with range 0 to 0.2 cm−1.
Fig. 6.
Fig. 6. Segmented US images and reconstructed absorption maps of a benign fibroadenoma imaged at 780 nm. (a) US image from a co-registered commercial US system. (b) Segmented US image from the segmentation CNN. (c) Reconstructed absorption map from the ${\sigma _1}$ -regularized algorithm, with a maximum ${\mu _a} = 0.084\; c{m^{ - 1}}$. (d) Reconstructed absorption map from the depth-regularized algorithm, with a maximum ${\mu _a} = 0.079\; c{m^{ - 1}}$. The units of absorption coefficients are cm−1, and the range is 0 to 0.1 cm−1.
Fig. 7.
Fig. 7. Boxplots of reconstructed maximum total hemoglobin values among 14 benign and 12 malignant cases, using either the ${\sigma _1}$-regularized algorithm (${\sigma _1}$-regularized) or the proposed depth-regularized algorithm (depth). HbT is reported in $\mu M.$
Fig. 8.
Fig. 8. Boxplot of R for 12 benign and 11 malignant cases.
Fig. 9.
Fig. 9. Images of a small malignant lesion imaged at 780 nm, reconstructed with both the ${\sigma _1}$ -regularized algorithm and depth-regularized algorithm. (a) Co-registered US image (b) Segmented US image (c) Reconstructed absorption distribution from the ${\sigma _1}$-regularized algorithm, with a maximum ${\mu _a} = 0.227\; c{m^{ - 1}}$. (d) Reconstructed absorption distribution from the depth-regularized algorithm, with a maximum ${\mu _a} = 0.226\; c{m^{ - 1}}$. The absorption coefficient is in cm−1, and the range is 0 to 0.2 cm−1.
Fig. 10.
Fig. 10. Reconstructed absorption coefficients versus segmentation errors. Positive errors mean segmented regions are larger than the true size of the lesion, and negative errors mean the opposite. First and second target layer widths were changed to evaluate the errors. Segmentation errors have minimal influence on reconstructed absorption coefficients.

Equations (5)

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[ U S C ] M × 1 = [ W ] M × N [ δ μ a ] N × 1 ,
δ μ a = arg min δ μ a | | U S C W δ μ a | | 2 + R ( δ μ a ) .
| | U S C W δ μ a | | 2 δ μ a = W H ( W δ μ a U S C ) .
p r o x R ( z , R ( δ μ a ) ) = argmin z { R ( δ μ a ) + 1 2 | | z δ μ a | | 2 2 } .
S γ ( z ) = s i g n ( z ) max ( 0 , | z | γ ) ,

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