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Previous ultrasound tomography work conducted by our group showed a direct correlation between measured sound speed and physical density in vitro, and increased in vivo sound speed with increasing mammographic density, a known risk factor for breast cancer. Building on these empirical results, the purpose of this work was to explore a metric to quantify breast density using our ultrasound tomography sound speed images in a manner analogous to computer-assisted mammogram segmentation for breast density analysis. Therefore, volumetric ultrasound percent density (USPD) is determined by segmenting high sound speed areas from each tomogram using a k-means clustering routine, integrating these results over the entire volume of the breast, and dividing by whole-breast volume. First, a breast phantom comprised of fat inclusions embedded in fibroglandular tissue was scanned four times with both our ultrasound tomography clinical prototype (with 4 mm spatial resolution) and CT. The coronal transmission tomograms and CT images were analyzed using semiautomatic segmentation routines, and the integrated areas of the phantom's fat inclusions were compared between the four repeated scans. The average variability for inclusion segmentation was approximately 7% and approximately2%, respectively, and a close correlation was observed in the integrated areas between the two modalities. Next, a cohort of 93 patients was imaged, yielding volumetric coverage of the breast (45-75 sound speed tomograms/patient). The association of USPD with mammographic percent density (MPD) was evaluated using two measures: (1) qualitative, as determined by a radiologist's visual assessment using BI-RADS Criteria and (2) quantitative, via digitization and semiautomatic segmentation of craniocaudal and mediolateral oblique mammograms. A strong positive association between BI-RADS category and USPD was demonstrated [Spearman rho = 0.69 (p < 0.001)], with significant differences between all BI-RADS categories as assessed by one-way ANOVA and Scheffe posthoc analysis. Furthermore, comparing USPD to calculated mammographic density yielded moderate to strong positive associations for CC and MLO views (r2 = 0.75 and 0.59, respectively). These results support the hypothesis that utilizing USPD as an analogue to mammographic breast density is feasible, providing a nonionizing, whole-breast analysis.

Women with mammographic percent density >50% have a approximately three-fold increased risk of developing breast cancer, potentially making them screening candidates for breast MRI scanning. The purpose of this work is to introduce a new method to quantify mammographic percent density (MPD), and to compare the results with the current standard of care for breast density assessment. Craniocaudal (CC) and mediolateral oblique (MLO) mammograms for 104 patients were digitized and analyzed using an interactive computer-assisted segmentation routine implemented for two purposes: (1) to segment the breast area from background and radiographic markers, and (2) to segment dense from fatty portions of the breast. Our technique was evaluated by comparing the results to qualitative estimates determined by a certified breast radiologist using the BI-RADS Categorical Assessment (1 (fatty) to 4 (dense) scale). Statistically significant correlations (two-tailed, p < 0.01) were observed between calculated MPD and BI-RADS for both CC (Spearman rho = 0.67) and MLO views (Spearman rho = 0.71). For the CC view, statistically significant differences were revealed between the mean MPD for each BI-RADS category except between fatty (BI-RADS 1) and scattered (BI-RADS 2). Finally, for the MLO views, statistically significant differences in the mean MPD between all BI-RADS categories were observed. Comparing the CC and MLO views revealed a strong positive correlation (Pearson r = 0.8) in calculated MPD. In addition, an evaluation of the reproducibility of our segmentation demonstrated the average standard deviation of MPD for a subsample of eight patients, measured five times, was 1.9% (range: 0.03%-9.9%). Eliminating one misassignment reduced the average standard deviation to 0.75% (range: 0.03%-3.16%). Further analysis of approximately 10% of the patient sample revealed strong agreement (ICC = 0.80-0.85) in the reliability of MPD estimates for both mammographic views. Overall, these results demonstrate the feasibility of utilizing our approach for quantitative breast density segmentation, which may be useful for detecting small changes in MPD introduced through chemoprevention, diet, or other interventions.

Ultrasound is commonly used as an adjunct to mammography for diagnostic evaluation of suspicions arising from breast cancer screening. As an alternative to conventional sonography that uses hand-held transducers, toroidal array probes that encircle the breast immersed in a water bath have been investigated for ultrasound tomography. In this paper, two sets of experiments performed with a prototype ultrasound scanner on a phantom and a human breast in vivo are used to investigate the effects of diffraction and coherence in ultrasound tomography. Reconstructions obtained with transmission diffraction tomography (TDT) are compared with conventional reflection imaging and computerized ultrasound tomography showing a substantial improvement. The in vivo tests demonstrate that TDT can image the complex boundary of a cancer mass and suggest that it can reveal the anatomy of milk ducts and Cooper's ligaments.

Previous ultrasound tomography work conducted by our group showed a direct correlation between measured sound speed and physical density in vitro, and increased in vivo sound speed with increasing mammographic density, a known risk factor for breast cancer. Building on these empirical results, the purpose of this work was to explore a metric to quantify breast density using our ultrasound tomography sound speed images in a manner analogous to computer-assisted mammogram segmentation for breast density analysis. Therefore, volumetric ultrasound percent density (USPD) is determined by segmenting high sound speed areas from each tomogram using a k-means clustering routine, integrating these results over the entire volume of the breast, and dividing by whole-breast volume. First, a breast phantom comprised of fat inclusions embedded in fibroglandular tissue was scanned four times with both our ultrasound tomography clinical prototype (with 4 mm spatial resolution) and CT. The coronal transmission tomograms and CT images were analyzed using semiautomatic segmentation routines, and the integrated areas of the phantom's fat inclusions were compared between the four repeated scans. The average variability for inclusion segmentation was approximately 7% and approximately2%, respectively, and a close correlation was observed in the integrated areas between the two modalities. Next, a cohort of 93 patients was imaged, yielding volumetric coverage of the breast (45-75 sound speed tomograms/patient). The association of USPD with mammographic percent density (MPD) was evaluated using two measures: (1) qualitative, as determined by a radiologist's visual assessment using BI-RADS Criteria and (2) quantitative, via digitization and semiautomatic segmentation of craniocaudal and mediolateral oblique mammograms. A strong positive association between BI-RADS category and USPD was demonstrated [Spearman rho = 0.69 (p0.001)], with significant differences between all BI-RADS categories as assessed by one-way ANOVA and Scheffé posthoc analysis. Furthermore, comparing USPD to calculated mammographic density yielded moderate to strong positive associations for CC and MLO views (r2 = 0.75 and 0.59, respectively). These results support the hypothesis that utilizing USPD as an analogue to mammographic breast density is feasible, providing a nonionizing, whole-breast analysis.

Women with mammographic percent density50% have a approximately three-fold increased risk of developing breast cancer, potentially making them screening candidates for breast MRI scanning. The purpose of this work is to introduce a new method to quantify mammographic percent density (MPD), and to compare the results with the current standard of care for breast density assessment. Craniocaudal (CC) and mediolateral oblique (MLO) mammograms for 104 patients were digitized and analyzed using an interactive computer-assisted segmentation routine implemented for two purposes: (1) to segment the breast area from background and radiographic markers, and (2) to segment dense from fatty portions of the breast. Our technique was evaluated by comparing the results to qualitative estimates determined by a certified breast radiologist using the BI-RADS Categorical Assessment (1 (fatty) to 4 (dense) scale). Statistically significant correlations (two-tailed, p0.01) were observed between calculated MPD and BI-RADS for both CC (Spearman rho = 0.67) and MLO views (Spearman rho = 0.71). For the CC view, statistically significant differences were revealed between the mean MPD for each BI-RADS category except between fatty (BI-RADS 1) and scattered (BI-RADS 2). Finally, for the MLO views, statistically significant differences in the mean MPD between all BI-RADS categories were observed. Comparing the CC and MLO views revealed a strong positive correlation (Pearson r = 0.8) in calculated MPD. In addition, an evaluation of the reproducibility of our segmentation demonstrated the average standard deviation of MPD for a subsample of eight patients, measured five times, was 1.9% (range: 0.03%-9.9%). Eliminating one misassignment reduced the average standard deviation to 0.75% (range: 0.03%-3.16%). Further analysis of approximately 10% of the patient sample revealed strong agreement (ICC = 0.80-0.85) in the reliability of MPD estimates for both mammographic views. Overall, these results demonstrate the feasibility of utilizing our approach for quantitative breast density segmentation, which may be useful for detecting small changes in MPD introduced through chemoprevention, diet, or other interventions.

This session focuses on recent developments in breast ultrasound imaging, covering clinical practice as well as clinical and pre-clinical research. Current status of ultrasound screening will be reviewed, followed by discussions of emerging technologies at various stages of regulatory and technical development. The session strives to provide the attendee with a view of a continuum of imaging advances starting with the present and spanning the near and long term future. Learning Objectives: 1. Understand current state of ultrasound screening for breast cancer 2. Learn about current availability of whole breast ultrasound 3. Appreciate the current barriers to stronger adoption of screening ultrasound 4. Learn about attempts to drive adoption of screening ultrasound (e.g. breast density laws) 5. Learn about ultrasound tomography as an emerging technique for breast imaging 6. Learn about the regulatory status of emerging techniques such as ultrasound tomography 7. Learn about the developmental status of combined 3-D ultrasound and Mammography systems 8. Learn about photo-acoustic imaging and its potential future role in breast imaging

Purpose: Breast cancer kills more women in the United States than any other cancer except for lungcancer.Treatment is generally surgery followed by radiation therapy,chemotherapy and/or hormonal therapy. Many of these patients will have side effects to the treatment, including 80% of patients receiving radiation therapy, where 48% will have long term effects. The purpose of this paper is to identify if tracking breast density through treatment is possible, and if that breast density is indicative of side effects.Methods: Patient data were acquired from patients recruited into ongoing studies in accord with a Karmanos Cancer Institute and Wayne State University approved research protocol. Patients were selected if they exhibited a suspicious mass after mammography and/or follow‐up ultrasound. We tracked the patient volume‐averaged breast density throughout the course of treatment, starting on the first day of treatment. We recorded the volume‐averaged breast density of each breast generally once every week. To analyze this data, we plot the left and right volume‐averaged breast density to determine if and how the breast density is changing. We used excel to determine the slope (the change of volume‐averaged breast density in km/s vs. time in s) and the coefficient of determination (r2) for our data. Results: Our results show that the change in breast density over the course of radiation treatment, Tamoxifen treatment, and a combination of the two, is measureable. Conclusions: We can detect a small change in breast density, in‐vivo, on a weekly basis, throughout the course of treatment. The limitation to this study is the small number of patients, in this study only 5. Future studies are needed to determine the significance of our results. Currently, we believe that edema, and possibly fibrosis would be observable, which will be shown in future studies.

Purpose: To investigate the use of the whole-breast sound speed measurement as a marker of breast density (BD), a known risk factor for breast cancer. Methods: As part of an ongoing study of breast cancer detection, 249 patients were scanned with a clinical prototype that operates on the principles of ultrasound tomography. Typically, 40–100 sound speed tomograms were reconstructed from the scan data, corresponding to the entire volume of the breast of each patient. The data were used to estimate the volume averaged sound speed (VASS) of the breast for each patient. The corresponding mammograms were used to calculate mammographic percent density (MPD) using CUMULUS software. Film mammograms were available for 164 patients while 85 digital mammograms were available for the remaining patients. Standard statistical techniques were used to determine associations of breast sound speed with a variety of mammographic measures such as percent density, area of dense tissue, and area of nondense tissue. Furthermore, associations of breast sound speed with continuous variables such as age and weight and dichotomous variables such as parity and menopausal status were also assessed. Results: VASS was found to be significantly associated with MPD. The Spearman correlation coefficient (rs) between VASS and MPD was found to be 0.77 and 0.71 for film and digital mammography, respectively. VASS was positively correlated with dense areas by mammography, both digital (rs = 0.46) and film (rs = 0.56). VASS was negatively associated with nondense area by mammography, both digital (rs = −0.58) and film (rs = −0.63). BD by all methods was less in postmenopausal than in premenopausal women. The MPD was lower in the postmenopausal group (by 6.6%, p < 0.08, for the digital group and 7.73%, p < 0.007, for the film group). The VASS was also lower in the postmenopausal group (by 15 m/s, p < 0.001 for the digital group and 8 m/s, p < 0.08, for the film group). The association of MPD with age was characterized with rs = −0.06 (p < 0.6) for digital mammography and rs = −0.53 (p < 0.002) for film mammography. For weight, the MPD associations were characterized by rs = −0.53 (p < 0.0001) for digital mammography and −0.38 (p < 0.0001) for film mammography. The association of VASS with age was rs = −0.33 (p < 0.002) for the digital group and −0.17 (p < 0.03) for the film group. For weight, the relationship was characterized with rs = −0.45 (p < 0.001) for the digital group and −0.37 (p < 0.0001) for the film group. Conclusions: The association between VASS and MPD is strong for both film and digital mammography, suggesting that VASS is a viable measure of breast density. This result sets the stage for future work that will focus on directly testing the association of VASS with breast cancer risk.

Purpose: Previous ultrasoundtomography work conducted by our group showed a direct correlation between measuredsound speed and physical density in vitro, and increased in vivosound speed with increasing mammographic density, a known risk factor for breast cancer. Building on these empirical results, we investigated the use of volumetric ultrasound percent density (USPD) for breast density estimation. Method and Materials: A breast phantom comprised of fat inclusions embedded in fibroglandular tissue was scanned four times with both ultrasoundtomography and CT. The coronal transmission tomograms and corresponding CTimages were analyzed using a semi‐automatic segmentation routine. Next, a cohort of ∼100 patients was imaged encompassing the entire breast volume (50–75 tomograms/patient). USPD was determined by segmenting high sound speed areas from each slice using a k‐means clustering routine, integrating these results over the entire breast, and dividing by total breast area. USPD was evaluated using two mammographic density measures: (1) qualitative, as determined by a radiologist's visual assessment using BI‐RADS Criteria and (2) quantitative, via digitization and semi‐automatic segmentation of craniocaudal and medio‐lateral oblique mammograms.Results: The integrated areas of the phantom's fat inclusions were compared using both transmission ultrasound and CT for four repeated scans. The average variability for inclusion segmentation was ∼2% and ∼10%, respectively, and a close correlation was observed in the integrated areas between the two modalities. A strong positive association between BI‐RADS category and USPD was demonstrated. Furthermore, comparing USPD to calculated mammographic density yielded moderate to strong positive associations (Pearson r = 0.76–0.84) for MLO and CC views, respectively. Conclusion: These results support the hypothesis that utilizing USPD as an analogue to mammographic breast density is feasible. USPD has the potential to provide a non‐ionizing, whole‐breast analysis of breast density, which may better elucidate the relationship between breast density and breast cancer risk.

Purpose: Women with high mammographic breast density are at a 4‐ to 6‐fold increased risk of developing breast cancer compared to women with fatty breasts. The purpose of this investigation was to confirm preliminary acoustic velocity measurements for breast density assessment, and to evaluate them with the current standard of care. Method and Materials: A sample of ∼100 patients was imaged with our computed ultrasound tomography clinical prototype with each dataset comprised of 45–75 tomograms ranging from near the chest wall through the nipple region. Whole breast acoustic velocity was determined by creating image stacks and evaluating the sound speed frequency distribution. The acoustic measures of breast density were evaluated by comparing these results to two mammographic density measures: (1) qualitative, as determined by a certified radiologist using the BI‐RADS Categorical Assessment based on a 1 (fatty) to 4 (dense) scale, and (2) quantitative, via digitization and computerized analysis of archival mammograms. The former involved a radiologist's visual assessment of each mammogram, while the latter required digitizing cranio‐caudal (CC) and mediolateral oblique (MLO) mammograms and implementing semi‐automatic segmentation routines. Results: Approximately 140 m/s difference in acoustic velocity was found between the fatty and dense BI‐RADS Categories, with statistically significant differences in mean sound speed demonstrated between each category (ANOVA, α= 0.05). In addition, an increased sound speed was exhibited with increased BI‐RADS Category. Furthermore, moderately strong positive associations were demonstrated between mean acoustic velocity and quantitative measures of mammographic percent breast density for both MLO and CC views (r2 = 0.65–0.76), respectively. Conclusion: These results confirm the hypothesis that utilizing acoustical velocity can be used as an indicator of breast tissue density. Having an operator independent and non‐ionizing means of whole‐breast density evaluation has important clinical implications which include tracking temporal changes, identifying high‐risk populations, and evaluating treatment response.

Purpose: To improve resolution and reduce speckle in ultrasound breast images by accounting for ultrasound scattering from breast heterogeneities during reflectivityimage reconstruction.Method and Materials: X‐ray mammography often fails to detect cancers in dense breasts, while breast ultrasound has the potential to detect them. Breast heterogeneities, particularly in dense breasts, generate significant ultrasound scattering. Properly handling ultrasound scattering is critical for reliable cancer detection and diagnosis in dense breasts. Ultrasoundwave propagation in the breast is governed by the acoustic‐wave equation in heterogeneous media, which can be decomposed into two one‐way wave equations describing wave propagation in opposite directions. A split‐step Fourier solution of a one‐way wave equation is used for backpropagation of reflected ultrasoundwaves. The backpropagation consists of two steps: one phase‐shift step in the frequency‐wavenumber domain, and another phaseshift step in the frequency‐space domain. During the backpropagation of ultrasound wavefields, heterogeneous breast sound‐speed models obtained from transmission ultrasound tomography are used to approximately account for ultrasoundwavescattering. The reflectivityimaging method based on the split‐step Fourier propagator is applied to computer‐generated ultrasound data and in‐vivo ultrasound breast data acquired using a ring transducer array. The ultrasoundimages are compared with those obtained using a uniform sound‐speed model.Results: Comparison of ultrasoundreflectivityimages obtained using heterogeneous breast sound‐speed models with those obtained with a uniform model shows that ultrasound scattering of breast heterogeneities needs to be taken into account to obtain high‐resolution and high‐quality breast images.Conclusion: Using heterogeneous sound‐speed models for ultrasoundwave backpropagation during reflectivityimage reconstruction significantly improves image resolution and reduces speckle. The resolution and quality of ultrasoundreflectivityimages are further enhanced with increasing accuracy and resolution of transmission ultrasound tomography.

Purpose: Travel‐time tomography plays a central role in our currentringtransducer array (Computerized Ultrasound Risk Evaluation — CURE) study of patients with suspicious breast masses. To properly calibrate an algorithm used for bent‐ray tomographic inversion of clinical ultrasound data, the following issues need to be addressed: balancing the data contribution and an a priorimodel, determining the stopping criteria for the iteration of the inversion, and estimating the resolving power of the tomography algorithm, that is, determining the reliability of the inversion. Method and Materials: In this study, quantitative assessments of the above issues are made based on synthetic models. The simulated ultrasonic data are generated using a high‐order finite‐difference time‐domain acoustic‐wave equation to model wave propagation through different numerical breast phantoms. The breast models are obtained by digitizing the reconstructed images of patient data and phantom data scanned by the CURE device. Checkerboard and perturbation tests are also performed to provide a quantitative estimate of the resolving power of the bent‐ray tomography algorithm. Guided by the synthetic study, the tomography algorithm with optimum parameters is applied to in‐vivo patient data acquired using CURE for breast cancer detection and diagnosis. Results: Our series of synthetic simulations show that an optimum trade‐off parameter can be chosen effectively using L‐Curve analysis. In addition, the stopping criteria can be chosen based on convergence rates of the iteration. Checkerboard and perturbation tests set a quantitative upper limit of the resolving power of the bent‐ray tomography for ultrasound data acquired with a ring array. Conclusion:Calibration of the bent‐ray tomography algorithm provides a high degree of confidence in tomographic inversion results, and helps in obtaining optimal tomographic images from in‐vivoultrasound breast data.

Purpose: Women with high mammographic breast density are at a 4‐ to 7‐ fold increased risk of developing breast cancer compared to women with fatty breasts. The purpose of this work is to investigate the potential of assessing breast density via acoustic velocity measurements obtained with ultrasoundcomputed tomography.Method and Materials: A sample of approximately 50 patients was imaged with our computed ultrasoundtomography clinical prototype. Each data set was comprised of 45 tomograms ranging from near the chest wall through the nipple region. Whole breast acoustic velocity was determined by creating image stacks and evaluating the sound speed frequency distribution. The acoustic measures of breast density were evaluated by comparing these results to two mammographic density measures: (1) qualitative, as determined by a certified radiologist using the BIRADS Categorical Assessment based on a 1 (fatty) to 4 (dense) scale, and (2) quantitative, via digitization and computerized analysis of archival mammograms. The former involved a radiologist's visual assessment of each mammogram, while the latter required scanning cranio‐caudal films with a Vidar VXR‐16 DosimetryPro digitizer and implementing semi‐automatic segmentation routines. Results: Approximately 60 m/s difference in acoustic velocity was found between the fatty and dense BIRADS categories. This investigation indicated a positive correlation between BIRADS category and acoustic velocity of the breast. In addition, a strong correlation between the mean acoustic velocity and quantitative measures of percent breast density was demonstrated (Pearson correlation coefficient 0.651, p < 0.001). Conclusion: These results support the hypothesis that utilizing acoustical velocity as an analogue to mammographic breast density is feasible. Our approach to evaluating breast density has the potential to provide a safer, non‐ionizing, and more quantitative means of evaluating breast density, thus better elucidating the relationship that exists between breast density and breast cancer risk.

Purpose: To quantify a preliminary relationship between breast density and acoustic velocity using ultrasoundcomputed tomography. The existence of such a relationship may provide quantitative prognostic information that can be used to determine breast cancer risk. Method and Materials: A sample of female patients was imaged using the CURE, or Computerized Ultrasound Risk Evaluation, device. The mammographic densities of each patient were assigned by a certified radiologist on a 1 (fatty) to 4 (dense) scale. Regions of interest (160 mm2) were selected contralateral to present anomalies in multiple adjacent slices centrally located on the breast, away from the chest wall and nipple regions. The minimum, maximum, mean, and standard deviation of the acoustic velocity were recorded for each slice. Results: This statistical information was then correlated against breast density to investigate the presence of meaningful relationships. Initial assessment of this sample suggests that the maximum sound speed values increase with increasing mammographic density, while the minimum values appear to decrease with increasing density. The mean sound speed shows a weak but steady trend with increasing density. The behavior of these correlations suggests that the intrinsic scatter of sound speeds within the breast tissue is a strong function of the mammographic breast density. However, these relationships are statistically weak due to a small sample size, and further investigation will be required to strengthen this association. Conclusion: This preliminary examination suggests that a quantifiable relationship between breast density and sound speed may exist. If these results are further validated in larger studies, then it may be possible to utilize CURE measurements to assess breast cancer risk with a quantitative score. Results will be available for forty additional patients in July, and a more definitive conclusion regarding these findings can then be provided.