Multiparametric Tissue Characterization for Breast Cancer Screening Using Transmission and Reflection Ultrasound Tomography

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PROJECT SUMMARY/ABSTRACT Breast cancer is the most diagnosed cancer in women in the United States and the most commonly occurring cancer worldwide according to the World Health Organization. The primary screening method for non-palpable breast cancers is X-ray mammography, which uses ionizing radiation and often has low sensitivity and specificity in dense breasts. Ultrasound tomography offers a low-cost alternative to X-ray mammography that can image the breast based on its acoustic properties and enable early cancer detection and diagnosis, while avoiding the potentially harmful effects of ionizing radiation. Currently, ultrasound tomography of the breast generally relies primarily on the transmission of ultrasound through the tissue to reconstruct underlying tissue properties such as sound speed and attenuation. However, a significant gap in our knowledge is the simultaneous modeling of signals transmitted through the tissue and signals reflected from the tissue to estimate these tissue properties. Combining transmission and reflection information from an ultrasound tomography system will enable accurate estimation of difficult-to-measure tissue properties such as the mass density. Furthermore, the addition of reflection information may enable tissue characterization in acoustically challenging cases where transmission information is less reliable (e.g., transmission through bone, partial angle tomography). I propose to improve the robustness of ultrasound transmission tomography by incorporating reflection information and enable the estimation of mass density. In order to expand the capabilities of the ultrasound tomography system I aim to: extend transmission tomography to three dimension in order account for out-of-plane scattering (Aim 1); develop algorithms to simultaneously reconstruct the sound speed, acoustic attenuation, and mass density of tissue using the complete transmission and reflection information acquired by an ultrasound tomography system (Aim 2); and develop sound speed reconstruction algorithms solely based on the reflected ultrasound signal, which will also benefit the applications of handheld pulse-echo ultrasound (Aim 3). These aims will improve our understanding of acoustic models applicable to ultrasound signals in both transmission and reflection as well as how these models may be inverted to reconstruct accurate and spatially resolved images of tissue properties. Expanding the tissue characterization capabilities of ultrasound tomography will enable a multi-parametric approach for the detection and characterization of breast cancer using a non-ionizing radiation imaging modality. Improving the robustness of ultrasound tomography in acoustically challenging cases will also enable cancer detection outside of the breast and may further enable detection of breast cancer metastasis. The proposed work will broadly benefit the ultrasound imaging field by improving our tissue modeling and characterization capabilities and enabling the early detection of disease based on acoustic tissue properties.