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CNN and back-projection: limited angle ultrasound tomography for speed of sound estimation (Conference Presentation)
Author(s): Emran Mohammad Abu Anas; Alexis Cheng; Reza Seifabadi; Yixuan Wu; Fereshteh Aalamifar; Bradford Wood; Arman Rahmim; Emad M. Boctor
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Paper Abstract

Ultrasound tomography has the potential to quantify the tissue acoustic properties for advanced clinical diagnosis. However, the location of the most of the human anatomies limits the tomography for a few angles that leads the reconstruction as a more challenging problem. In this work, a deep convolutional neural networks-based technique is presented to estimate the speed of sound of tissue from a limited angle projection data. The underlying concept is based on filtered back projection technique, where the convolutional neural network is used to model the high-pass filter before the back projection. Moreover, we use a post convolutional neural networks to suppress the artifacts generated due to the limited angle tomography. We train the network from a set of simulation experiments; on the test set consisting of 1,750 simulation experiments, we achieve an average mean absolute error of 2.1% in predicting the speed of sound map.

Paper Details

Date Published: 15 March 2019
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Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 109550M (15 March 2019); doi: 10.1117/12.2513043
Show Author Affiliations
Emran Mohammad Abu Anas, Johns Hopkins Univ. (United States)
Alexis Cheng, National Institutes of Health (United States)
Reza Seifabadi, National Institutes of Health (United States)
Yixuan Wu, Johns Hopkins Univ. (United States)
Fereshteh Aalamifar, Johns Hopkins Univ. (United States)
Bradford Wood, National Institutes of Health (United States)
Arman Rahmim, The Univ. of British Columbia (Canada)
Emad M. Boctor, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 10955:
Medical Imaging 2019: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

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