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Volumetric breast density evaluation from ultrasound tomography images

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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 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 and , 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 ], 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 ( 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.
Title: Volumetric breast density evaluation from ultrasound tomography images
Description:
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 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 and , 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 ], 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 ( 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.

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