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- W4310584080 abstract "The distortion of ultrasound (US) wavefronts as they traverse tissues with varying material properties (i.e., aberration) is a primary cause of image degradation. Aberration correction is of particular importance among subjects with obesity for abdominal imaging-for which US is a first-choice diagnostic tool-since the images are degraded due to aberrating layers of subcutaneous fat. Receive beamforming for diagnostic US typically computes time-of-flight delays based on a single speed of sound (SoS) for the entire medium; thus, correcting the aberration requires inferring the correct delays resulting from heterogeneity of the tissue. Here we use a convolutional neural network (CNN) to predict the SoS distribution in the medium directly from the RF data of the backscattered echoes, and subsequently use this field to correct the receive delays to reduce aberration. Following training with full wave simulations and experimental acquisitions, the CNN predicted had median error in the SoS of 5 m/s for experimental validation data. The resulting experimental images had improved contrast (9.7±2.9 dB) and resolution (44% lower) than uncorrected delay-and-sum beamforming with constant SoS. This pipeline, termed SoundAI, thus shows potential to improve abdominal US image and enhance diagnostic decision-making." @default.
- W4310584080 created "2022-12-12" @default.
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- W4310584080 date "2022-10-10" @default.
- W4310584080 modified "2023-10-16" @default.
- W4310584080 title "SoundAI: Improved Imaging with Learned Sound Speed Maps" @default.
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- W4310584080 doi "https://doi.org/10.1109/ius54386.2022.9958284" @default.
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