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Self‐supervised learning for accelerated 3D high‐resolution ultrasound imaging
Author(s) -
Dai Xianjin,
Lei Yang,
Wang Tonghe,
Axente Marian,
Xu Dong,
Patel Pretesh,
Jani Ashesh B.,
Curran Walter J.,
Liu Tian,
Yang Xiaofeng
Publication year - 2021
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14946
Subject(s) - artificial intelligence , computer science , image resolution , pattern recognition (psychology) , breast ultrasound , deep learning , computer vision , breast imaging , noise (video) , image (mathematics) , breast cancer , mammography , cancer , medicine
Purpose Ultrasound (US) imaging has been widely used in diagnosis, image‐guided intervention, and therapy, where high‐quality three‐dimensional (3D) images are highly desired from sparsely acquired two‐dimensional (2D) images. This study aims to develop a deep learning‐based algorithm to reconstruct high‐resolution (HR) 3D US images only reliant on the acquired sparsely distributed 2D images. Methods We propose a self‐supervised learning framework using cycle‐consistent generative adversarial network (cycleGAN), where two independent cycleGAN models are trained with paired original US images and two sets of low‐resolution (LR) US images, respectively. The two sets of LR US images are obtained through down‐sampling the original US images along the two axes, respectively. In US imaging, in‐plane spatial resolution is generally much higher than through‐plane resolution. By learning the mapping from down‐sampled in‐plane LR images to original HR US images, cycleGAN can generate through‐plane HR images from original sparely distributed 2D images. Finally, HR 3D US images are reconstructed by combining the generated 2D images from the two cycleGAN models. Results The proposed method was assessed on two different datasets. One is automatic breast ultrasound (ABUS) images from 70 breast cancer patients, the other is collected from 45 prostate cancer patients. By applying a spatial resolution enhancement factor of 3 to the breast cases, our proposed method achieved the mean absolute error (MAE) value of 0.90 ± 0.15, the peak signal‐to‐noise ratio (PSNR) value of 37.88 ± 0.88 dB, and the visual information fidelity (VIF) value of 0.69 ± 0.01, which significantly outperforms bicubic interpolation. Similar performances have been achieved using the enhancement factor of 5 in these breast cases and using the enhancement factors of 5 and 10 in the prostate cases. Conclusions We have proposed and investigated a new deep learning‐based algorithm for reconstructing HR 3D US images from sparely acquired 2D images. Significant improvement on through‐plane resolution has been achieved by only using the acquired 2D images without any external atlas images. Its self‐supervision capability could accelerate HR US imaging.