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Principal Investigator: Rehman Ali
Organization: UNIVERSITY OF ROCHESTER
Fiscal Year: 2024
Award: $74,284
Funding agency: National Institute of Biomedical Imaging and Bioengineering
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.
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