Breast density is now recognized as one of the most important independent risk factors of breast cancer. Current means to assess breast density primarily utilize mammograms which represent a series of projection images, making it difficult to estimate the true volume of the fibroglandular tissue. We present 3D transmission ultrasound as a method to visualize and differentiate fibroglandular tissue within the breast and use an unsupervised learning-based method to quantitatively assess the respective breast density. The method includes initial separation of breast from the surrounding water bath followed by segmentation of the whole breast into fibroglandular tissue and fat using fuzzy C-mean (FCM) classification. We apply these methods to both tissue phantoms (in vitro) and clinical breast images (in vivo). In the case of tissue phantoms, the agreement between the theoretical (geometric density) and experimentally calculated values was better than 90%. For density calculation in a sample size of 50 cases, the results correlate well (Spearman r = 0.93, 95% CI: 0.88-0.96, p<0.0001) with an FDA-cleared breast density assessment software, VolparaDensity. We also discuss the advantage of using FCMbased tissue classification over threshold-based tissue segmentation within the paradigm of iterative image inversion/reconstruction and show that the former method is less sensitive to variation in assessment of breast density as a function of iteration count and thus, less dependent on convergence criteria. These results imply that breast density as assessed by 3D transmission ultra-sound can be of significant clinical utility and play an important role in breast cancer risk assessment.
The challenge of ultrasound tomography in the presence of high impedance contrast is well known. We have successfully used full 3D transmission inverse scattering and refraction corrected reflection tomography to create 3D high-resolution images of the human breast. However, these tissues do not encompass the high contrast that occurs in orthopaedics scenarios, such as the human knee, where cranial and trabecular bone are present. Even though the high contrast of the bone is problematic for model based iterative reconstruction methods, we successfully image the tissue near, and in, the Femur-Tibia (F-T) space using an adapted QT Ultrasound Scanner and adapted inverse scattering algorithm.
We show preliminary reconstructions of a cadaver knee that indicates that we can quantitatively and accurately image proximal soft tissue structures. We give correlations between MR images and QT Ultrasound transmission images that show correlation with known structures: besides the femur, tibia, and fibula, we see the condyle structures (medial and lateral), medial and lateral menisci internal to the F-T space, collateral ligaments, infrapatellar fat pad (Hoffa’s pad), patellar ligament, and various ligaments, tendons and musculature in the leg above and below the knee.
We establish that a substantially different reconstruction protocol (than that of the breast) for 3D inverse scattering is required to obtain these images and we discuss the implications of these changes. These preliminary results show that high resolution of clinically relevant tissue is feasible with ultrasound tomography even within the F-T space.