Validation of a Deep Learning-Based Method for Accelerating Susceptibility-Weighted Imaging in Clinical Settings

Abstract

Susceptibility-weighted imaging (SWI) has been widely used in clinical contexts, in which the speed of acquisition is frequently a critical issue. In this study, we aim to test the feasibility of a deep learning (DL)-based reconstruction method for accelerating SWI acquisition in clinical settings. A total of 61 subjects were consecutively enrolled. SWI scans using prospective under-sampling and DL-based reconstruction method (acceleration factor = 5, acquisition time: 1:46) and parallel imaging (PI, acceleration factor = 2, acquisition time: 4:45) were acquired from each subject. The DL-based method utilizes a cascaded convolutional neural network, namely, ReconNet3D, to perform k-space to image reconstruction with the capacity of single-channel input and single-channel output. The DL-SWI and PI-SWI results were compared quantitatively using structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). Two raters independently assessed image quality from five aspects: artifacts, noise level, sharpness, lesion conspicuity, and overall image quality. The numbers of microbleeds were also counted. Finally, both sets of images of the same subject were reviewed side-by-side for noninferiority assessments. The DL-SWI images showed good similarity in terms of SSIM (mean ± SD: 0.89 ± 0.02) and PSNR (mean ± SD: 36.91 ± 2.41) with PI-SWI. In comparison, DL-SWI images had significantly better scores in terms of artifacts, noise, and overall image quality score (4.15 vs. 3.33; 3.85 vs. 3.16; 3.85 vs. 3.44, all with p < 0.001). DL-SWI showed reduced image sharpness (p = 0.031), but no significant difference regarding lesion conspicuity. The number of microbleeds identified from the DL-SWI images completely matched the PI-SWI results. We had not observed any false-negative or false-positive readings on DL-SWI images. The DL-based SWI acceleration method could significantly reduce scan time and maintain image quality, suggesting its great potential in clinical applications.

Keywords: acceleration; deep learning; image quality; image reconstruction; image similarity; magnetic resonance imaging; parallel imaging; susceptibility‐weighted imaging.