Current cancer diagnosis and treatment requires patient to undergo an invasive biopsy process. This treatment is not only painful and can cause further complications, but is also emotionally and financially draining process for the patient and his/her family.

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In recent years, researchers have developed a non-invasive process, called elastography or elasticity imaging, which maps the mechanical properties (like Young’s modulus) of tissue from ultrasound and/or MRI data and use this map of (mechanical) properties to diagnose the tumor more accurately. Thus, circumventing the need of undergoing an invasive biopsy.

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While valuable and promising, elasticity imaging requires solution of an inverse elasticity problem in modulus reconstruction step. This step is complex (doesn’t admit unique solution), computationally expensive, and is time consuming (this could be critical in medical diagnosis). To circumvent these challenges, in this paper, we propose a novel deep-learning based workflow to non-invasively diagnose breast lesions from ultrasound data, while preserving the underlying mechanics.

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In the process, we also demonstrate how physics-based modeling can felicitate transfer learning/domain randomization in data-scarce applications like medical imaging. By analyzing learned filters in physical and Fourier space, and interpreting them as a discrete differential operator, we also find interesting connection between this learning-based approach to elasticity imaging and traditional strain-imaging based approaches.

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Apart from medical diagnosis, the proposed framework can also find application in many areas of science and engineering like design optimization, non-destructive testing etc., where the goal is to make decision based on quantities of interest inferred from measurements.

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Paper : https://doi.org/10.1016/j.cma.2019.04.045

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Comments and feedback are welcome!