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15 March 2019 Deep learning image reconstruction method for limited-angle ultrasound tomography in prostate cancer
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Problem: The gold standard for prostate cancer diagnosis is B-mode transrectal ultrasound-guided systematic core needle biopsy. However, cancer is indistinguishable under ultrasound and thus additional costly imaging methods are necessary to perform targeted biopsies. Speed of sound is a potential biomarker for prostate cancer and has the potential to be measured using ultrasound tomography. Given the physical constraints of the prostate’s anatomy, this work explores a simulation study using deep learning for limited-angle ultrasound tomography to reconstruct speed of sound. Methods: A deep learning-based image reconstruction framework is used to address the limited-angle ultrasound tomography problem. The training data is generated using the k-wave acoustic simulation package. The general network structure is composed of a series of dense fully-connected layers followed by an encoder and a decoder network. The basic idea behind this neural network is to encode a time of flight map into a lower dimension representation that can then be decoded into a speed of sound image. Results and Conclusions: We show that limited-angle UST is feasible in simulation using an auto-encoder-like DL framework. There was a mean absolute error of 7.5 ± 8.1 m/s with a maximum absolute error of 139.3 m/s. Future validation on experimental data will further assess their ability in improving limited-angle ultrasound tomography.
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Alexis Cheng, Younsu Kim, Emran M. A. Anas, Arman Rahmim, Emad M. Boctor, Reza Seifabadi, and Bradford J. Wood "Deep learning image reconstruction method for limited-angle ultrasound tomography in prostate cancer", Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 1095516 (15 March 2019);

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