Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks.

By Feiyu Chen, V. Taviani, Itzik Malkiel, Joseph Y. Cheng, Jonathan I. Tamir, Jamil Shaikh, Stephanie T. Chang, C. Hardy, John M. Pauly, Shreyas S. Vasanawala. 2018

129 citations recorded by the source corpus.

Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.25 times acceleration in 157 patients referred for abdominal imaging (mean age, 11 years; range, 1-34 years; 72 males [mean age, 10 years; range, 1-26 years] and 85 females [mean age, 12 years; range, 1-34 years]) between March 2016 and April 2017. A VN was trained based on the parallel imaging and compressed sensing (PICS) reconstruction of 130 patients. The remaining 27 patients were used for evaluation. Image quality was evaluated in an independent blinded fashion by three radiologists in terms of overall image quality, perceived signal-to-noise ratio, image contrast, sharpness, and residual artifacts with scores ranging from 1 (nondiagnostic) to 5 (excellent). Wilcoxon tests were performed to test the hypothesis that there was no significant difference between VN and PICS. Results VN achieved improved perceived signal-to-noise ratio (P = .01) and improved sharpness (P < .001), with no difference in image contrast (P = .24) and residual artifacts (P = .07). In terms of overall image quality, VN performed better than did PICS (P = .02). Average reconstruction time ± standard deviation was 5.60 seconds ± 1.30 per section for PICS and 0.19 second ± 0.04 per section for VN. Conclusion Compared with the conventional parallel imaging and compressed sensing reconstruction (PICS), the variational network (VN) approach accelerates the reconstruction of variable-density single-shot fast spin-echo sequences and achieves improved overall image quality with higher perceived signal-to-noise ratio and sharpness. © RSNA, 2018 Online supplemental material is available for this article.

Subfields: Atomic and Molecular Physics, and Optics, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “This is illustrated by the tremendous amounts of high-impact publications in top-journals dealing with deep learning in medical imaging (for example [16,20,29,31,39,89,136,139,161,272,284], all published in 2018).”
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, background): “While recent approaches from the literature [43, 44, 62, 63, 65] have either been trained with hundreds of examples rather than millions of examples as it is common in deep learning for computer vision [4, 66], or trained on synthetic non-medical data [64] that is publicly available from existing da”