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We evaluated training deep neural network (DNN) beamformers for the task of high contrast imaging in the presence of reverberation clutter. Training data was generated using simulated hypoechoic cysts and a pseudo nonlinear method for generating reverberation clutter. Performance was compared to standard delay-and-sum (DAS) beamforming on simulated hypoechoic cysts having a different size. For a hypoechoic cyst in the presence of reverberation clutter, when the intrinsic contrast ratio (CR) was -10 dB and -20 dB, the measured CR for DAS beamforming was -9.2±0.8 dB and -14.3±0.5 dB, respectively, and the measured CR for DNNs was -10.7±1.4 dB and -20.0±1.0 dB, respectively. For a hypoechoic cyst with -20 dB intrinsic CR, the contrast-to-noise ratio (CNR) was 3.4±0.3 dB and 4.3±0.3 dB for DAS and DNN beamforming, respectively. These results show that DNN beamforming was able to extend contrast ratio dynamic range (CRDR) by about 10 dB while also improving CNR.
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Domain Adaptation for Ultrasound Beamforming
Ultrasound B-Mode images are created from data obtained from each element in the transducer array in a process called beamforming. The beamforming goal is to enhance signals from specified spatial locations, while reducing signal from all other locations. On clinical systems, beamforming is accomplished with the delay-and-sum (DAS) algorithm. DAS is efficient but fails in patients with high noise levels, so various adaptive beamformers have been proposed. Recently, deep learning methods have been developed for this task. With deep learning methods, beamforming is typically framed as a regression problem, where clean, ground-truth data is known, and usually simulated. For in vivo data, however, it is extremely difficult to collect ground truth information, and deep networks trained on simulated data underperform when applied to in vivo data, due to domain shift between simulated and in vivo data. In this work, we show how to correct for domain shift by learning deep network beamformers that leverage both simulated data, and unlabeled in vivo data, via a novel domain adaption scheme. A challenge in our scenario is that domain shift exists both for noisy input, and clean output. We address this challenge by extending cycle-consistent generative adversarial networks, where we leverage maps between synthetic simulation and real in vivo domains to ensure that the learned beamformers capture the distribution of both noisy and clean in vivo data. We obtain consistent in vivo image quality improvements compared to existing beamforming techniques, when applying our approach to simulated anechoic cysts and in vivo liver data.
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- Award ID(s):
- 1750994
- PAR ID:
- 10216938
- Date Published:
- Journal Name:
- MICCAI 2020: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
- Volume:
- 12262
- Page Range / eLocation ID:
- 410-420
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-59713-9_40
