Abstract

Imaging-guided near infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response of breast cancers. However, diffused light measurements are sensitive to artifacts caused by outliers and errors in measurements due to probe-tissue coupling, patient and probe motions, and tissue heterogeneity. In general, pre-processing of the measurements is needed by experienced users to manually remove these outliers and therefore reduce imaging artifacts. An automated method of outlier removal, data selection, and filtering for diffuse optical tomography is introduced in this manuscript. This method consists of multiple steps to first combine several data sets collected from the same patient at contralateral normal breast and form a single robust reference data set using statistical tests and linear fitting of the measurements. The second step improves the perturbation measurements by filtering out outliers from the lesion site measurements using model based analysis. The results of 20 malignant and benign cases show similar performance between manual data processing and automated processing and improvement in tissue characterization of malignant to benign ratio by about 27%.

© 2016 Optical Society of America

Full Article  |  PDF Article
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References

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B. Zheng, A. B. Hollingsworth, M. Y. Tan, R. G. Stough, and H. Liu, “Abstract P4-02-06: Improving efficacy of applying breast MRI to detect mammography-occult breast cancer,” Cancer Res. 76(4Supplement), 4–20 (2016).
[PubMed]

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[Crossref] [PubMed]

C. Xu, H. Vavadi, A. Merkulov, H. Li, M. Erfanzadeh, A. Mostafa, Y. Gong, H. Salehi, S. Tannenbaum, and Q. Zhu, “Ultrasound-Guided Diffuse Optical Tomography for Predicting and Monitoring Neoadjuvant Chemotherapy of Breast Cancers Recent Progress,” Ultrason. Imaging 38(1), 5–18 (2016).
[PubMed]

2015 (1)

2014 (2)

2013 (1)

2012 (2)

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] [PubMed]

2010 (3)

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref] [PubMed]

Q. Zhu, P. U. Hegde, A. Ricci, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, P. J. Deckers, and S. H. Tannenbaum, “Early-Stage Invasive Breast Cancers: Potential Role of Optical Tomography with US Localization in Assisting Diagnosis 1,” Radiology 256(2), 367–378 (2010).
[Crossref] [PubMed]

Z. M. Zhang, S. Chen, and Y. Z. Liang, “Baseline correction using adaptive iteratively reweighted penalized least squares,” Analyst (Lond.) 135(5), 1138–1146 (2010).
[Crossref] [PubMed]

2009 (4)

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25(12), 123010 (2009).
[Crossref]

H. Dehghani, S. Srinivasan, B. W. Pogue, and A. Gibson, “Numerical modelling and image reconstruction in diffuse optical tomography,” Philos. Trans. Roy. Soc. A 367(1900), 3073–3093 (2009).

D. L. Kepshire, H. Dehghani, F. Leblond, and B. W. Pogue, “Automatic exposure control and estimation of effective system noise in diffuse fluorescence tomography,” Opt. Express 17(25), 23272–23283 (2009).
[Crossref] [PubMed]

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

2007 (1)

B. C. Sutradhar, D. P. Chu, and W. Bari, “Estimation effects on powers of two simple test statistics in identifying an outlier in linear models,” J. Stat. Comput. Simul. 77(4), 305–328 (2007).
[Crossref]

2006 (2)

S. Srinivasan, B. W. Pogue, H. Dehghani, F. Leblond, and X. Intes, “Data subset algorithm for computationally efficient reconstruction of 3-D spectral imaging in diffuse optical tomography,” Opt. Express 14(12), 5394–5410 (2006).
[Crossref] [PubMed]

A. Cerussi, N. Shah, D. Hsiang, A. Durkin, J. Butler, and B. J. Tromberg, “In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy,” J. Biomed. Opt. 11(4), 044005 (2006).
[Crossref] [PubMed]

2005 (1)

B. Chance, S. Nioka, J. Zhang, E. F. Conant, E. Hwang, S. Briest, S. G. Orel, M. D. Schnall, and B. J. Czerniecki, “Breast cancer detection based on incremental biochemical and physiological properties of breast cancers: a six-year, two-site study,” Acad. Radiol. 12(8), 925–933 (2005).
[Crossref] [PubMed]

2004 (1)

2002 (1)

V. Ntziachristos, A. G. Yodh, M. D. Schnall, and B. Chance, “MRI-guided diffuse optical spectroscopy of malignant and benign breast lesions,” Neoplasia 4(4), 347–354 (2002).
[Crossref] [PubMed]

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]

1998 (1)

R. M. Danen, Y. Wang, X. D. Li, W. S. Thayer, and A. G. Yodh, “Regional imager for low-resolution functional imaging of the brain with diffusing near-infrared light,” Photochem. Photobiol. 67(1), 33–40 (1998).
[Crossref] [PubMed]

1993 (1)

1971 (1)

W. Stefansky, “Rejecting outliers by maximum normed residual,” Ann. Math. Stat. 42(1), 35–45 (1971).
[Crossref]

1969 (1)

F. E. Grubbs, “Procedures for detecting outlying observations in samples,” Technometrics 11(1), 1–21 (1969).
[Crossref]

1950 (1)

F. E. Grubbs, “Sample criteria for testing outlying observations,” Ann. Math. Stat. 21(1), 27–58 (1950).
[Crossref]

Abbate, F.

Aguirre, A.

Q. Zhu, P. U. Hegde, A. Ricci, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, P. J. Deckers, and S. H. Tannenbaum, “Early-Stage Invasive Breast Cancers: Potential Role of Optical Tomography with US Localization in Assisting Diagnosis 1,” Radiology 256(2), 367–378 (2010).
[Crossref] [PubMed]

Anderson, P. G.

Ardeshirpour, Y.

Q. Zhu, P. U. Hegde, A. Ricci, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, P. J. Deckers, and S. H. Tannenbaum, “Early-Stage Invasive Breast Cancers: Potential Role of Optical Tomography with US Localization in Assisting Diagnosis 1,” Radiology 256(2), 367–378 (2010).
[Crossref] [PubMed]

Arridge, S. R.

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25(12), 123010 (2009).
[Crossref]

Baker, W. B.

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref] [PubMed]

Balestreri, N.

Bandos, A. I.

W. A. Berg, A. I. Bandos, E. B. Mendelson, D. Lehrer, R. A. Jong, and E. D. Pisano, “Ultrasound as the primary screening test for breast cancer: analysis from ACRIN 6666,” J. Natl. Cancer Inst. 108(4), djv367 (2016).
[Crossref] [PubMed]

Bari, W.

B. C. Sutradhar, D. P. Chu, and W. Bari, “Estimation effects on powers of two simple test statistics in identifying an outlier in linear models,” J. Stat. Comput. Simul. 77(4), 305–328 (2007).
[Crossref]

Berg, W. A.

W. A. Berg, A. I. Bandos, E. B. Mendelson, D. Lehrer, R. A. Jong, and E. D. Pisano, “Ultrasound as the primary screening test for breast cancer: analysis from ACRIN 6666,” J. Natl. Cancer Inst. 108(4), djv367 (2016).
[Crossref] [PubMed]

Boas, D. A.

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] [PubMed]

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] [PubMed]

Briest, S.

B. Chance, S. Nioka, J. Zhang, E. F. Conant, E. Hwang, S. Briest, S. G. Orel, M. D. Schnall, and B. J. Czerniecki, “Breast cancer detection based on incremental biochemical and physiological properties of breast cancers: a six-year, two-site study,” Acad. Radiol. 12(8), 925–933 (2005).
[Crossref] [PubMed]

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] [PubMed]

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]

Butler, J.

A. Cerussi, N. Shah, D. Hsiang, A. Durkin, J. Butler, and B. J. Tromberg, “In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy,” J. Biomed. Opt. 11(4), 044005 (2006).
[Crossref] [PubMed]

Carp, S. A.

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] [PubMed]

Carpenter, C. M.

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

Cassano, E.

Cerussi, A.

A. Cerussi, N. Shah, D. Hsiang, A. Durkin, J. Butler, and B. J. Tromberg, “In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy,” J. Biomed. Opt. 11(4), 044005 (2006).
[Crossref] [PubMed]

Chance, B.

B. Chance, S. Nioka, J. Zhang, E. F. Conant, E. Hwang, S. Briest, S. G. Orel, M. D. Schnall, and B. J. Czerniecki, “Breast cancer detection based on incremental biochemical and physiological properties of breast cancers: a six-year, two-site study,” Acad. Radiol. 12(8), 925–933 (2005).
[Crossref] [PubMed]

V. Ntziachristos, A. G. Yodh, M. D. Schnall, and B. Chance, “MRI-guided diffuse optical spectroscopy of malignant and benign breast lesions,” Neoplasia 4(4), 347–354 (2002).
[Crossref] [PubMed]

Chen, S.

Z. M. Zhang, S. Chen, and Y. Z. Liang, “Baseline correction using adaptive iteratively reweighted penalized least squares,” Analyst (Lond.) 135(5), 1138–1146 (2010).
[Crossref] [PubMed]

Choe, R.

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref] [PubMed]

Chu, D. P.

B. C. Sutradhar, D. P. Chu, and W. Bari, “Estimation effects on powers of two simple test statistics in identifying an outlier in linear models,” J. Stat. Comput. Simul. 77(4), 305–328 (2007).
[Crossref]

Conant, E. F.

B. Chance, S. Nioka, J. Zhang, E. F. Conant, E. Hwang, S. Briest, S. G. Orel, M. D. Schnall, and B. J. Czerniecki, “Breast cancer detection based on incremental biochemical and physiological properties of breast cancers: a six-year, two-site study,” Acad. Radiol. 12(8), 925–933 (2005).
[Crossref] [PubMed]

Cronin, E.

Q. Zhu, A. Ricci, 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] [PubMed]

Cronin, E. B.

Q. Zhu, P. U. Hegde, A. Ricci, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, P. J. Deckers, and S. H. Tannenbaum, “Early-Stage Invasive Breast Cancers: Potential Role of Optical Tomography with US Localization in Assisting Diagnosis 1,” Radiology 256(2), 367–378 (2010).
[Crossref] [PubMed]

Cubeddu, R.

Culver, J. P.

Czerniecki, B. J.

B. Chance, S. Nioka, J. Zhang, E. F. Conant, E. Hwang, S. Briest, S. G. Orel, M. D. Schnall, and B. J. Czerniecki, “Breast cancer detection based on incremental biochemical and physiological properties of breast cancers: a six-year, two-site study,” Acad. Radiol. 12(8), 925–933 (2005).
[Crossref] [PubMed]

Danen, R. M.

R. M. Danen, Y. Wang, X. D. Li, W. S. Thayer, and A. G. Yodh, “Regional imager for low-resolution functional imaging of the brain with diffusing near-infrared light,” Photochem. Photobiol. 67(1), 33–40 (1998).
[Crossref] [PubMed]

Davis, S. C.

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

Deckers, P. J.

Q. Zhu, P. U. Hegde, A. Ricci, M. Kane, E. B. Cronin, Y. Ardeshirpour, C. Xu, A. Aguirre, S. H. Kurtzman, P. J. Deckers, and S. H. Tannenbaum, “Early-Stage Invasive Breast Cancers: Potential Role of Optical Tomography with US Localization in Assisting Diagnosis 1,” Radiology 256(2), 367–378 (2010).
[Crossref] [PubMed]

Dehghani, H.

Diflorio-Alexander, R.

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]

Durduran, T.

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref] [PubMed]

Durkin, A.

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Cancer Res. (1)

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Radiology (3)

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Figures (6)

Fig. 1
Fig. 1 Block diagram of the automated outlier removal and data selection method. In the block diagram MNR stands for maximum normed residual and IRLS stands for iterative reweighted least square.
Fig. 2
Fig. 2 Log scaled amplitude and phase profiles of reference data sets before (first column) and after preprocessing (second column) as well as final compound reference (third column). Saturated source-detector pairs have been marked with red rectangle in the amplitude part of the first column.
Fig. 3
Fig. 3 Reconstructed absorption maps of a benign breast lesion obtained at 740, 780, 808 and 830 nm with no preprocessing (1st row), with manual data selection (2nd row) and automated data selection (3rd row). Each slide is 9 cm by 9 cm reconstructed at the center depth of the lesion. Images from other depths were not shown. Vertical bars are absoption in cm−1 unit.
Fig. 4
Fig. 4 Reconstructed absorption maps of a malignant breast lesion at 740, 780, 808 and 830 nm with no preprocessing (1st row), with manual data selection (2nd row) and automated data selection (3rd row). Each slide is 9 cm by 9 cm reconstructed at the center depth of the lesion. Images from other depths were not shown. Vertical bars are absorption in cm−1 unit.
Fig. 5
Fig. 5 Ultrasound image (1st column) and total hemoglobin map calculated with no data selection (2nd column), with manual data selection (3rd column) and automatic data selection (4th column) of the benign (top row) and the malignant breast lesion (bottom row) reconstructed with automated data selection method. Each slide is 9 cm by 9 cm and vertical bars are hemoglobin concentrations in uM unit.
Fig. 6
Fig. 6 Comparison of maximum total hemoglobin concentration reconstructed with both manual and automated data selection methods for 10 malignant and 10 benign cases. Vertical axis is the total hemoglobin concentration in μmol/Liter.

Tables (3)

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Table 1 Range of parameters used for analytical model and obtained maximum phase delay.

Tables Icon

Table 2 Comparison of maximum reconstructed absorption coefficients of malignant and benign groups using manual and automated data selection method

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Table 3 Comparison of maximum reconstructed absorption coefficients of malignant and benign groups using manual and automated data selection method

Equations (3)

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G Threshold (i)= k1 k t α/ ( 2k ),k2 2 (i) k2+ t α/ ( 2k ),k2 2 (i) .
β n+1 = i=1 m w(i) β n | y(i)f(i,β) | 2 .
U sc = A l (i).exp( φ l (i) ) A r (i).exp( φ r (i) ) A r (i).exp( φ r (i) ) =( A l (i) A r (i) cos( φ l (i) φ r (i) )1 )+j( A l (i) A r (i) sin( φ l (i) φ r (i) ) )

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