3D inverse scattering ultrasound tomography (3D UT) is quantitative and not subject to artifacts from 2D algorithms and data and does not require contrast agents or ionizing radiation. However, it is time consuming, so it is important to have timing results for 3D inverse scattering reconstructions of the whole breast with 3D algorithms and full 3D data and in the clinically relevant context of a diverse population of dense, heterogeneously dense and fatty breasts. The adaptive algorithm uses different reconstruction frequencies and iteration counts for different breasts. We compare a computational complexity count with the observed fit of reconstruction times vs breast size that and show performance comparable to published TFLOP performance for nVidia cards. We show a reconstruction time of 24 minutes for an average size breast and show substantial speed up with more efficient nVidia cards. These numbers indicate clinical viability for 3D transmission ultrasound even in the clinical setting with diverse demographics in low income areas. The cohort of 23 cases of different types of breasts were reconstructed on two P6000's and compared with the same data reconstructed on two RTX6000's with 24GB on-board memory and some optimization of the CUDA code. The resulting speed up is better than linear with increasing computation time, indicating increasing efficiency with computational complexity, larger breasts. Image quality is also affected, since increasing iteration and frequency counts give generally better images as long as overconvergence is avoided. These results further validate the 3D Quantitative UT as clinically viable, especially for underserved populations.
Mammographic quantitative breast density (QBD), the ratio of fibroglandular tissue to whole breast volume, is known to be important for risk assessment for breast cancer. Most methods are based on 2D projections, though some use MRI. We show two methods for determining QBD from 3D ultrasound tomographic (UT) images, their equivalence and superiority over other methods of estimation. False assignments to breast density can occur if projection methods are used. A sigmoidal function is fit using a log likelihood maximization and the QBD from MRI images is compared with QBD as calculated from 3D UT showing strong correlation.
3D Transmission with 360 degree compounded reflection ultrasound has been shown effective as a basis for quantitative assessment of breast density on a continuous scale that is compatible with existing FDA approved methods. Breast density is an important risk factor in several breast cancer risk models. Unfortunately, methods utilizing projections (e.g. mammography) or even tomosynthesis do not fully represent the true topological diversity and complexity of the human breast. Presently, the use of the reflection image is important in delineation of the breast volume from the water bath. However, the reflection data and/or image may not be available in some scenarios due to scanner design or equipment malfunction. Furthermore, other data (such as levels of data) may be missing or not collected for specific, perhaps economic, reasons. The Spearman Rank coefficient for correlation of the 3D transmission and reflection ultrasound based quantitative breast density (QBD) was 93% which decreased to 91.5% when reflection image/data were removed. The Spearman r increased again to 95% when smoothing was applied to the speed and attenuation images. The results indicate that even without the reflection data information, the 3D transmission ultrasound characterization of the tissue yields QBD values commensurate with FDA approved methods. This may make the construction of certain quantitative breast estimator devices more economical and useful.
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