A Machine Learning Alternative to Beamforming to Improve Ultrasound Image Quality for Interventional Access to the Kidney

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Project Summary Despite the widespread prevalence of ultrasound imaging in hospitals today, the clinical utility of ultrasound guidance is severely hampered by clutter and reverberation artifacts that obscure structures of interest and com- plicate anatomical measurements. Clutter is particularly problematic in overweight and obese individuals, who account for 78.6 million adults and 12.8 million children in North America. Similarly, interventional procedures of- ten require insertion of one or more metal tools, which generate reverberation artifacts that obfuscate instrument location, orientation, and geometry, while obscuring nearby tissues, thus additionally hampering ultrasound im- age quality. Although artifacts are problematic, ultrasound continues to persist primarily because of its greatest strengths (i.e., mobility, cost, non-ionizing radiation, real-time visualization, and multiplanar views) in comparison to existing image-guidance options, but it would be significantly more useful without problematic artifacts. Our long-term project goal is to use state-of-the-art machine learning techniques to provide interventional radiologists with artifact-free ultrasound-based images. We will initially develop a new framework alternative to the ultrasound beamforming process that removes needle tip reverberations and acoustic clutter caused by multipath scattering in near-field tissues when guiding needles to the kidney to enable removal of painful kidney stones. Our first aim will test convolutional neural networks (CNNs) that input raw channel data and output human readable images with no artifacts caused by multipath scattering and reverberations. A secondary goal of the CNNs is to learn the minimum number of parameters required to create these new CNN-based images. Our second aim will validate the trained algorithms with ultrasound data from experimental phantom and ex vivo tissue. Our third aim will extend our evaluation to ultrasound images of in vivo porcine kidneys. This work is the first to propose bypassing the entire beamforming process and replacing it with machine learning and computer vision techniques to remove traditionally problematic noise artifacts and create a fundamentally new type of artifact-free, high-contrast, high-resolution, ultrasound-based image for guiding interventional procedures. This work combines the expertise of an imaging scientist, a computer scientist, and an interventional ra- diologist to explore an untapped, understudied area that is only recently made feasible through improvements in computing power, advances in computer vision capabilities, and new knowledge about dominant sources of image degradation. Translation to in vivo cases is enabled by our clinical collaboration with the Department of Radiology at the Johns Hopkins Hospital. With support from the NIH Trailblazer Award, our team will be the first to develop these tools and capabilities to eliminate noise artifacts in interventional ultrasound, opening the door to a new paradigm in ultrasound image formation, which will directly benefit millions of patients with clearer, easier-to-interpret ultrasound images. Subsequent R01 funding will customize our innovation to addi- tional application-specific ultrasound procedures (e.g., breast biopsies, cancer detection, autonomous surgery).