Dynamic functional and mechanical response of breast tissue to compression

S. A. Carp; J. Selb; Q. Fang; R. Moore; D. B. Kopans; E. Rafferty; D. A. Boas

doi:10.1364/OE.16.016064

Dynamic functional and mechanical response of breast tissue to compression

S. A. Carp, J. Selb, Q. Fang, R. Moore, D. B. Kopans, E. Rafferty, and D. A. Boas

Optics Express
Vol. 16,
Issue 20,
pp. 16064-16078
(2008)
•https://doi.org/10.1364/OE.16.016064

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S. A. Carp, J. Selb, Q. Fang, R. Moore, D. B. Kopans, E. Rafferty, and D. A. Boas, "Dynamic functional and mechanical response of breast tissue to compression," Opt. Express 16, 16064-16078 (2008)

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Related Topics
Table of Contents Category
- Medical Optics and Biotechnology
Optics & Photonics Topics
?

The topics in this list come from the OSA Optics and Photonics Topics applied to this article.
Previously assigned OCIS codes
- Medical and biological imaging (170.3880)
- Spectroscopy, fluorescence and luminescence (170.6280)
- Functional monitoring and imaging (170.2655)
- Tissue characterization (170.6935)

About this Article
History
- Original Manuscript: June 6, 2008
- Revised Manuscript: July 23, 2008
- Manuscript Accepted: August 8, 2008
- Published: September 25, 2008
Virtual Issues
Virtual Journal for Biomedical Optics Vol. 3, Iss. 11

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Open Access

Abstract

Physiological tissue dynamics following breast compression offer new contrast mechanisms for evaluating breast health and disease with near infrared spectroscopy. We monitored the total hemoglobin concentration and hemoglobin oxygen saturation in 28 healthy female volunteers subject to repeated fractional mammographic compression. The compression induces a reduction in blood flow, in turn causing a reduction in hemoglobin oxygen saturation. At the same time, a two phase tissue viscoelastic relaxation results in a reduction and redistribution of pressure within the tissue and correspondingly modulates the tissue total hemoglobin concentration and oxygen saturation. We observed a strong correlation between the relaxing pressure and changes in the total hemoglobin concentration bearing evidence of the involvement of different vascular compartments. Consequently, we have developed a model that enables us to disentangle these effects and obtain robust estimates of the tissue oxygen consumption and blood flow. We obtain estimates of 1.9±1.3 µmol/100mL/min for OC and 2.8±1.7 mL/100mL/min for blood flow, consistent with other published values.

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2008 (1)

H. Rinneberg, D. Grosenick, K. T. Moesta, H. Wabnitz, J. Mucke, G. Wubbeler, R. Macdonald, and P. Schlag, “Detection and characterization of breast tumours by time-domain scanning optical mammography,” Opto-Electron. Rev. 16, 147–162 (2008).
[Crossref]

2007 (3)

A. Cerussi, D. Hsiang, N. Shah, R. Mehta, A. Durkin, J. Butler, and B. J. Tromberg, “Predicting response to breast cancer neoadjuvant chemotherapy using diffuse optical spectroscopy,” PNAS 104, 4014–4019 (2007).
[Crossref] [PubMed]

M. Sridhar and M. F. Insana, “Ultrasonic measurements of breast viscoelasticity,” Med. Phys. 34, 4757–4767 (2007).
[Crossref]

T. J. Huppert, M. S. Allen, H. Benav, P. B. Jones, and D. A. Boas, “A multicompartment vascular model for inferring baseline and functional changes in cerebral oxygen metabolism and arterial dilation,” J. Cereb. Blood Flow Metab. 27, 1262–1279 (2007).
[Crossref] [PubMed]

2006 (4)

A. Thomas, T. Fischer, H. Frey, R. Ohlinger, S. Grunwald, J. Blohmer, K. Winzer, S. Weber, G. Kristiansen, B. Ebert, and S. Kümmel, “Real-time elastography - an advanced method of ultrasound: first results in 108 patients with breast lesions,” Ultrasound Obstet. Gynecol. 28, 335–340 (2006).
[Crossref] [PubMed]

R. Leiderman, P. Barbone, A. Oberai, and J. Bamber, “Coupling between elastic strain and interstitial fluid flow: ramification for poroelastic imaging,” Phys. Med. Biol. 51, 6291–6313 (2006).
[Crossref] [PubMed]

A. P. Gibson, T. Austin, N. L. Everdell, M. Schweiger, S. R. Arridge, J. H. Meek, J. S. Wyatt, D. T. Delpy, and J. C. Hebden, “Three-dimensional whole-head optical passive motor evoked responses in the tomography of neonate,” Neuroimage 30, 521–528 (2006).
[Crossref]

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[Crossref] [PubMed]

2005 (7)

X. Intes, S. Djeziri, Z. Ichalalene, N. Mincu, Y. Wang, P. St-Jean, F. Lesage, D. Hall, D. Boas, M. Polyzos, D. Fleiszer, and B. Mesurolle, “Time-domain optical mammography SoftScan: Initial results,” Acad. Radiol. 12, 934–947 (2005).
[Crossref] [PubMed]

B. Chance, S. Nioka, J. Zhang, E. Conant, E. Hwang, S. Briest, S. Orel, M. D. Schnall, and B. Czerniecki, “Breast Cancer detection based on incremental Biochemical and Physiological properties of breast cancers: A six-year, two-site study 1,” Acad. Radiol. 12, 925–933 (2005).
[Crossref] [PubMed]

S. Srinivasan, B. W. Pogue, B. Brooksby, S. D. Jiang, H. Dehghani, C. Kogel, W. A. Wells, S. P. Poplack, and K. D. Paulsen, “Near-infrared characterization of breast tumors in vivo using spectrally-constrained reconstruction,” Technol. Cancer Res. Treat. 4, 513–526 (2005).
[PubMed]

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: A case study with comparison to MRI,” Med. Phys. 32, 1128–1139 (2005).
[Crossref] [PubMed]

T. Xydeas, K. Siegmann, R. Sinkus, U. Krainick-Strobel, S. Miller, and C. Claussen, “Magnetic resonance elastography of the breast - Correlation of signal intensity data with viscoelastic properties,” Invest. Radiol. 40, 412–420 (2005).
[Crossref] [PubMed]

C. Schmitz, D. Klemer, R. Hardin, M. Katz, P. Yaling, H. Graber, M. Levin, R. Levina, N. Franco, W. Solomon, and R. Barbour, “Design and implementation of dynamic near-infrared optical tomographic imaging instrumentation for simultaneous dual-breast measurements,” Appl. Opt. 44, 2140–2153 (2005).
[Crossref] [PubMed]

T. Durduran, R. Choe, G. Q. Yu, C. Zhou, J. C. Tchou, B. J. Czerniecki, and A. G. Yodh, “Diffuse optical measurement of blood flow in breast tumors,” Opt. Lett. 30, 2915–2917 (2005).
[Crossref] [PubMed]

2004 (2)

M. Insana, C. Pellot-Barakat, M. Sridhar, and K. Lindfors, “Viscoelastic Imaging of Breast Tumor Microenvironment with Ultrasound,” J. Mammary Gland Biology and Neoplasia 9, 393–404 (2004).
[Crossref]

L. Spinelli, A. Torricelli, A. Pifferi, P. Taroni, G. M. Danesini, and R. Cubeddu, “Bulk optical properties and tissue components in the female breast from multiwavelength time-resolved optical mammography,” J. Biomed. Opt. 9, 1137–1142 (2004).
[Crossref] [PubMed]

2003 (3)

S. D. Jiang, B. W. Pogue, K. D. Paulsen, C. Kogel, and S. P. Poplack, “In vivo near-infrared spectral detection of pressure-induced changes in breast tissue,” Opt. Lett. 28, 1212–1214 (2003).
[Crossref]

N. Shah, A. E. Cerussi, D. Jakubowski, D. Hsiang, J. Butler, and B. J. Tromberg, “The role of diffuse optical spectroscopy in the clinical management of breast cancer,” Dis. Markers 19, 95–105 (2003).

A. Li, E. L. Miller, M. E. Kilmer, T. J. Brukilacchio, T. Chaves, J. Stott, Q. Zhang, T. Wu, M. Chorlton, R. H. Moore, D. B. Kopans, and D. A. Boas, “Tomographic optical breast imaging guided by three-dimensional mammography,” Appl. Opt. 42, 5181–5190 (2003).
[Crossref] [PubMed]

2002 (6)

C. H. Schmitz, M. Locker, J. M. Lasker, A. H. Hielscher, and R. L. Barbour, “Instrumentation for fast functional optical tomography,” Rev. Sci. Instrum. 73, 429–439 (2002). Part 1.
[Crossref]

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

D. A. Mankoff, L. K. Dunnwald, J. R. Gralow, G. K. Ellis, A. Charlop, T. J. Lawton, E. K. Schubert, J. Tseng, and R. B. Livingston, “Blood Flow and Metabolism in Locally Advanced Breast Cancer: Relationship to Response to Therapy,” J. Nucl. Med. 43, 500–509 (2002).
[PubMed]

J. P. Delille, P. J. Slanetz, E. D. Yeh, D. B. Kopans, and L. Garrido, “Breast Cancer: Regional Blood Flow and Blood Volume Measured with Magnetic Susceptibility-based MR Imaging-Initial Results,” Radiology 223,558–565 (2002).
[Crossref] [PubMed]

H. Y. An and W. L. Lin, “Cerebral venous and arterial blood volumes can be estimated separately in humans using magnetic resonance imaging,” Magn. Reson. Med. 48, 583–588 (2002).
[Crossref] [PubMed]

F. Azar, D. Metaxas, and M. Schnall, “Methods for modeling and predicting mechanical deformaions of the breast under external perturbations,” Med. Image Anal. 6, 1–27 (2002).
[Crossref] [PubMed]

2001 (3)

A. Y. Bluestone, G. Abdoulaev, C. H. Schmitz, R. L. Barbour, and A. H. Hielscher, “Three-dimensional optical tomography of hemodynamics in the human head,” Opt. Express 9, 272–286 (2001).
[Crossref] [PubMed]

R. Barbour, H. Graber, Y. Pei, S. Zhong, and C. Schmitz, “Optical tomographic imaging of dynamic features of dense-scattering media,” J. Opt. Soc. Am. A 18, 3018–3036 (2001).
[Crossref]

F. S. Azar, D. N. Metaxas, and M. D. Schnall, “A deformable finite element model of the breast for predicting mechanical deformations under external perturbations,” Acad. Radiol. 8, 965–975 (2001).
[Crossref] [PubMed]

2000 (3)

T. Varghese, J. Ophir, and T. A. Krouskop, “Nonlinear stress-strain relationships in tissue and their effect on the contrast-to-noise ratio in elastograms,” Ultrasound Med. Biol. 26, 839–851 (2000).
[Crossref] [PubMed]

T. Q. Duong and S. G. Kim, “In vivo MR measurements of regional arterial and venous blood volume fractions in intact rat brain,” Magn. Reson. Med. 43, 393–402 (2000).
[Crossref] [PubMed]

V. Ntziachristos, A. G. Yodh, M. Schnall, and B. Chance, “Concurrent MRI and diffuse optical tomography of breast after indocyanine green enhancement,” PNAS 97, 2767–2772 (2000).
[Crossref] [PubMed]

1997 (1)

J. W. Chang, H. L. Graber, P. C. Koo, R. Aronson, S. L. S. Barbour, and R. L. Barbour, “Optical imaging of anatomical maps derived from magnetic resonance images using time-independent optical sources,” IEEE Trans. Med. Imaging 16, 68–77 (1997).
[Crossref] [PubMed]

1994 (2)

S. Fantini, M. Franceschini, and E. Gratton, “Semi-infinite-geometry boundary problem for light migration in highly scattering media: a frequency-domain study in the diffusion approximation,” J. Opt. Soc. Am. B 11, 2128–2138 (1994).
[Crossref]

S. Fantini, M. Franceschini, J. Fishkin, B. Barbieri, and E. Gratton, “Quantitative determination of the absorption spectra of chromophores in scattering media: a light-emitting-diode based technique,” Appl. Opt. 33, 5204–5213 (1994).
[Crossref] [PubMed]

1992 (1)

C. Wilson, A. A. Lammertsma, C. G. McKenzie, K. Sikora, and T. Jones, “Measurements of blood-flow and exchanging water space in breast-tumors using positron emission tomography - a rapid and noninvasive dynamic method,” Cancer Research 52, 1592–1597 (1992).
[PubMed]

1987 (2)

D. White, H. Woodard, and S. Hammond, “Average soft-tissue and bone models for use in radiation dosimetry,” Br. J. Radiol. 60, 907–913 (1987).
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Acad. Radiol. (3)

Am. J. Physiol. (Heart and Circulatory Physiology) (1)

C. Desjardins and B. Duling, “Microvessel hematocrit: measurement and implications for capillary oxygen transport,” Am. J. Physiol. (Heart and Circulatory Physiology) 252, H494–H503 (1987).

Appl. Opt. (3)

Br. J. Radiol. (2)

H. Woodard and D. White, “The composition of body tissues,” Br. J. Radiol. 59, 1209–1219 (1986).
[Crossref] [PubMed]

D. White, H. Woodard, and S. Hammond, “Average soft-tissue and bone models for use in radiation dosimetry,” Br. J. Radiol. 60, 907–913 (1987).
[Crossref] [PubMed]

Cancer Research (1)

Dis. Markers (1)

IEEE Trans. Med. Imaging (1)

Invest. Radiol. (1)

J. Biomed. Opt. (1)

J. Cereb. Blood Flow Metab. (1)

J. Mammary Gland Biology and Neoplasia (1)

J. Nucl. Med. (1)

J. Opt. Soc. Am. A (1)

R. Barbour, H. Graber, Y. Pei, S. Zhong, and C. Schmitz, “Optical tomographic imaging of dynamic features of dense-scattering media,” J. Opt. Soc. Am. A 18, 3018–3036 (2001).
[Crossref]

J. Opt. Soc. Am. B (1)

Lancet (1)

Magn. Reson. Med. (2)

T. Q. Duong and S. G. Kim, “In vivo MR measurements of regional arterial and venous blood volume fractions in intact rat brain,” Magn. Reson. Med. 43, 393–402 (2000).
[Crossref] [PubMed]

Med. Image Anal. (1)

Med. Phys. (3)

M. Sridhar and M. F. Insana, “Ultrasonic measurements of breast viscoelasticity,” Med. Phys. 34, 4757–4767 (2007).
[Crossref]

Neoplasia (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, 347–354 (2002).
[Crossref] [PubMed]

Neuroimage (1)

Opt. Express (1)

Opt. Lett. (2)

Opto-Electron. Rev. (1)

Phys. Med. Biol. (1)

PNAS (2)

Radiology (1)

Rev. Sci. Instrum. (1)

C. H. Schmitz, M. Locker, J. M. Lasker, A. H. Hielscher, and R. L. Barbour, “Instrumentation for fast functional optical tomography,” Rev. Sci. Instrum. 73, 429–439 (2002). Part 1.
[Crossref]

Technol. Cancer Res. Treat. (1)

Ultrasound Med. Biol. (1)

Ultrasound Obstet. Gynecol (1)

Other (10)

M. A. Franceschini, D. K. Joseph, T. J. Huppert, S. G. Diamond, and D. A. Boas, “Diffuse optical imaging of the whole head,” J. Biomed. Opt.11 (2006).
[Crossref] [PubMed]

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Fig. 1. Experimental setup. A computer controlled translation stage applies compression to a subjects’ breasts while diffuse reflection measurements are acquired at several source detector separations. Strain gauges are integrated into the compression frame for force monitoring.

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Fig. 2. Representative hemodynamic measurement. An initial decrease in blood volume and a small decrease in saturation are observed during the initial compression, followed by a slow increase in blood volume and a continued decrease in saturation. Data from three subjects is presented, with the total hemoglobin [HbT] trace on the left, followed by the hemoglobin saturation time course, and finally, the compression force needed to maintain the position of the compression plates on the right.

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Fig. 3. Total hemoglobin concentration vs pressure plots from three subjects, showing lower apparent compliance at high pressure and higher apparent compliance at lower pressures. Data is taken from measurements performed on the same volunteers shown in Figure 2.

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Fig. 4. Metabolic model fitting showing the improvement brought on by subtracting the influence of the change in [HbT] on the SO ₂ trace. a) Raw measurement; b) Corrected SO ₂ trace

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Tables (5)

Table 1. [HbT] decrease during compression and subsequent recovery during tissue relaxation

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Table 2. Pressure relaxation characteristics

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Table 3. Pressure relaxation dynamics

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Table 4. Metabolic analysis criteria summary

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Table 5. Summary of metabolic parameters estimated from measurements satisfying the inclusion criteria listed above, using, respectively, the model in Ref. 24 (Carp2006) and the metabolic model described in §2.4 including the blood volume correction described in the beginning of the current section

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Equations (15)

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μ_{a} = \frac{ω}{2 c_{t}} (\frac{S_{Φ}}{S_{AC}} - \frac{S_{AC}}{S_{Φ}})

{μ'}_{s} = \frac{S_{AC}^{2} - S_{Φ}^{2}}{3 μ_{a}} - μ_{a}

\frac{d [H b O_{2}]}{dt} = - \frac{OC}{4 V_{t}} + \frac{F_{in} {[H b T]}_{b} S_{a}, o_{2}}{V_{t}} - \frac{F_{out} {[H b T]}_{b} S_{ν}, o_{2}}{V_{t}}

F_{out} = F_{in} - \frac{d {[H b T]}_{t}}{dt} \frac{V_{t}}{{[H b T]}_{b}}

S_{ν, O_{2}} = \frac{S O_{2} - f S_{a, o_{2}}}{1 - f}

\frac{d [H b O_{2}]}{dt} = S O_{2} \frac{d {[H b T]}_{t}}{dt} + {[H b T]}_{t} \frac{d S O_{2}}{dt}

S O_{2} \frac{d {[H b T]}_{t}}{dt} + {[H b T]}_{t} \frac{d S O_{2}}{dt} = - \frac{OC}{4 V_{t}} + \frac{F_{in} {[H b T]}_{b} S_{a, O_{2}}}{V_{t}}

- (\frac{F_{in} {[H b T]}_{b}}{V_{t}} - \frac{d {[H b T]}_{t}}{dt}) \frac{S O_{2} - f S_{a, O_{2}}}{1 - f}

\frac{d S O_{2}}{dt} = \frac{1}{{[H b T]}_{t}} (- \frac{OC}{4 V_{t}} + \frac{F_{in} {[H b T]}_{b} S_{a, O_{2}}}{V_{t}} + (\frac{F_{in} {[H b T]}_{b}}{V_{t}} - \frac{d {[H b T]}_{t}}{dt}) \frac{f S_{a, O_{2}}}{1 - f}) +

+ \frac{S O_{2}}{{[H b T]}_{t}} (- (\frac{F_{in} {[H b T]}_{b}}{V_{t}} - \frac{d {[H b T]}_{t}}{dt}) \frac{1}{1 - f} - \frac{d {[H B T]}_{t}}{dt})

\frac{d {SO}_{2} (t)}{dt} = a (t) + b (t) {SO}_{2} (t), {SO}_{2 | t = 0} = {SO}_{2, init}

a (t) = \frac{1}{{[H b T]}_{t}} (- \frac{OC}{4 V_{t}} + \frac{F_{in} {[H b T]}_{b} S_{a, O_{2}}}{V_{t}} + (\frac{F_{in} {[H b T]}_{t}}{V_{t}} - \frac{d {[H b T]}_{t}}{dt}) \frac{f S_{a, O_{2}}}{1 - f})

b (t) = - \frac{1}{{[H b T]}_{t}} ((\frac{F_{in} {[H b T]}_{b}}{V_{t}} - \frac{d {[H b T]}_{t}}{dt}) \frac{1}{1 - f} + \frac{d {[H b T]}_{t}}{dt})

{[H b O_{2}]}_{corrected} (t) = [H b O_{2}] (t) - S_{a, O_{2}} ({[H b T]}_{t} (t) - {[H b T]}_{t, begin})

{SO}_{2, corrected} (t) = \frac{{[H b O_{2}]}_{corrected} (t)}{({[H b O_{2}]}_{corrected} (t) + [H b R] (t))}

	Cycle 1	Cycle 2	Cycle 3
[HbT] initial decrease	14.3±7.4%	17.7±3.8 %	19.3±2.5 %
[HbT] recovery	7.4±11.5 %	3.8±4.7 %	2.53±4.2 %

	Cycle 1	Cycle 2	Cycle 3
Fast pressure relaxation	17.9±9.0 %	18.7±7.4 %	18.5±10.0 %
Slow pressure relaxation	43.2±12.1 %	28.9±7.6 %	28.6±10.9 %
Percentage of total relaxation during fast drop	27%	40%	40%

	Cycle 1	Cycle 2	Cycle 3
Duration of quick decrease	3s	3s	3s
Decay time constant of slow decrease	29.5±9.8s	37.5±8.2s	46.2±17.8s

	Cycle 1	Cycle 2	Cycle 3	Overall
Accepted for analysis	8 (18.2%)	9 (20.5%)	11 (25.0%)	28 (21.2%)
Rejected due to SO ₂ SNR	29 (65.9%)	25 (56.8%)	25 (56.8%)	79 (59.9%)
Rejected due to ∂ ² SO _2,corr/dt ²<0	16 (36.4%)	18 (40.9%)	14 (31.8%)	48 (36.4%)
Rejected due to [HbR] increase while compressing	11 (25.0%)	12 (27.3%)	9 (20.5%)	32 (24.2%)
Rejected due to data artifacts	6 (13.6%)	10 (22.7%)	4 (9.1%)	20 (15.2%)

	Cycle 1	Cycle 2	Cycle 3
(Method in Carp2006) Oxygen Consumption (µmol/100mL/min)	5.44±6.39	7.54±7.98	3.30±4.79
(Method in ੲ2.4) Oxygen Consumption (µmol/100mL/min)	1.65±0.73	2.11±1.89	1.89±1.27
(Method in Carp2006) Blood Flow (ml/100mL/min)	9.57±11.15	8.20±9.21	4.65±5.73
(Method in ੲ2.4) Blood Flow (ml/100mL/min)	2.67±1.28	2.97±2.18	2.78±1.70
(Method in Carp2006) R ²	0.61	0.49	0.74
(Method in ੲ2.4) R ²	0.88	0.90	0.93