TY - JOUR
T1 - Deep Learning for Ultrasound Beamforming in Flexible Array Transducer
AU - Huang, Xinyue
AU - Lediju Bell, Muyinatu A.
AU - Ding, Kai
N1 - Funding Information:
Manuscript received March 30, 2021; revised May 21, 2021; accepted May 31, 2021. Date of publication June 8, 2021; date of current version October 27, 2021. This work was supported in part by the National Cancer Institute of the National Institutes of Health under Award R37CA229417. The work of Muyinatu A. Lediju Bell was supported in part by the National Institutes of Health (NIH) Trailblazer under Award R21 EB025621. (Corresponding author: Kai Ding.) Xinyue Huang is with the Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218 USA (e-mail: xhuang63@jhu.edu).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - Ultrasound imaging has been developed for image-guided radiotherapy for tumor tracking, and the flexible array transducer is a promising tool for this task. It can reduce the user dependence and anatomical changes caused by the traditional ultrasound transducer. However, due to its flexible geometry, the conventional delay-and-sum (DAS) beamformer may apply incorrect time delay to the radio-frequency (RF) data and produce B-mode images with considerable defocusing and distortion. To address this problem, we propose a novel end-to-end deep learning approach that may alternate the conventional DAS beamformer when the transducer geometry is unknown. Different deep neural networks (DNNs) were designed to learn the proper time delays for each channel, and they were expected to reconstruct the undistorted high-quality B-mode images directly from RF channel data. We compared the DNN results to the standard DAS beamformed results using simulation and flexible array transducer scan data. With the proposed DNN approach, the averaged full-width-at-half-maximum (FWHM) of point scatters is 1.80 mm and 1.31 mm lower in simulation and scan results, respectively; the contrast-to-noise ratio (CNR) of the anechoic cyst in simulation and phantom scan is improved by 0.79 dB and 1.69 dB, respectively; and the aspect ratios of all the cysts are closer to 1. The evaluation results show that the proposed approach can effectively reduce the distortion and improve the lateral resolution and contrast of the reconstructed B-mode images.
AB - Ultrasound imaging has been developed for image-guided radiotherapy for tumor tracking, and the flexible array transducer is a promising tool for this task. It can reduce the user dependence and anatomical changes caused by the traditional ultrasound transducer. However, due to its flexible geometry, the conventional delay-and-sum (DAS) beamformer may apply incorrect time delay to the radio-frequency (RF) data and produce B-mode images with considerable defocusing and distortion. To address this problem, we propose a novel end-to-end deep learning approach that may alternate the conventional DAS beamformer when the transducer geometry is unknown. Different deep neural networks (DNNs) were designed to learn the proper time delays for each channel, and they were expected to reconstruct the undistorted high-quality B-mode images directly from RF channel data. We compared the DNN results to the standard DAS beamformed results using simulation and flexible array transducer scan data. With the proposed DNN approach, the averaged full-width-at-half-maximum (FWHM) of point scatters is 1.80 mm and 1.31 mm lower in simulation and scan results, respectively; the contrast-to-noise ratio (CNR) of the anechoic cyst in simulation and phantom scan is improved by 0.79 dB and 1.69 dB, respectively; and the aspect ratios of all the cysts are closer to 1. The evaluation results show that the proposed approach can effectively reduce the distortion and improve the lateral resolution and contrast of the reconstructed B-mode images.
KW - Ultrasound imaging
KW - beamforming
KW - deep Learning
KW - flexible array transducer
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U2 - 10.1109/TMI.2021.3087450
DO - 10.1109/TMI.2021.3087450
M3 - Article
C2 - 34101588
AN - SCOPUS:85111030545
SN - 0278-0062
VL - 40
SP - 3178
EP - 3189
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 11
ER -