Backscatter ultrasound physics for image segmentation and biological tissue characterization

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BACKGROUND: Clinical ultrasound (US) systems include B-mode for structure imaging, Doppler modes for flow characterization, and elastography modes for tissue deformation and elasticity assessments. Other methods using radiofrequency (RF) backscatter echoes have also been developed for tissue characterization of numerous organs.*******FRAMEWORK and OBJECTIVE: Modern quantitative US (QUS) RF-based methods for tissue characterization mainly rely on two main strategies. One approach consists in modeling the frequency-dependent backscatter coefficient (spectroscopy analysis) to describe microstructural properties of tissues; whereas a second approach is to use 1st and 2nd order statistics of the echo envelope to define speckle properties. None of these approaches has been implemented yet on clinical scanners. The framework of this proposal consists in unifying these different concepts to propose diagnostic parameters with a physical interpretation. This will be done by considering a frequency dependent structure factor and mixtures of homodyned K-distributions of echo statistics. By unifying QUS concepts, we aim identifying complementary parameters with specific signatures of tissue microstructures for the purpose of pathological tissue segmentation and characterization. Targeted applications are imaging of atherosclerotic plaques, blood clots, breast tumours and diseased tendons.*******NOVELTY and IMPACT: US is arguably the hardest medical imaging modality upon which to perform segmentation (and tissue characterization) as image contrast and structure definition are much worse than competing magnetic resonance and computed tomography imaging technologies. Accordingly, most computer-based methods for US segmentation require anatomical, geometrical, temporal and/or image physics priors. In this grant, we propose novel image physics concepts in the framework of a Bayesian segmentation and tissue characterization model that can include anatomical, geometrical and temporal priors to dynamically track organs within an image sequence. This project is technically challenging and based on fundamental acoustic physics, and should directly impact human health as targeted applications are broad and cover active clinical areas of research of my laboratory (clinical data acquisitions are supported by other active grants).*******CONCLUSION: US imaging represents today the largest grow in term of number of units in the radiology and medical physics fields because it is non-invasive, hand-held, relatively inexpensive compared to other imaging technologies, and also because it can be used on the bed size. Depending on the application and modality used, US is also sensitive and specific. The development of QUS-based methods, as proposed in this grant program, should impact this momentum and provide clinicians with new imaging modalities for better diagnostic and therapy follow-up.***