Abstract
PURPOSE: Recently, a novel deep learning (DL)-based technique for reconstructing highly undersampled MR data (DL-Speed, DLS) has been developed, which demonstrated superior performance over compressed sensing. This study aimed to achieve high-resolution double inversion recovery (DIR) imaging using DLS (DLS-DIR) and compare its diagnostic performance in the detection of juxtacortical multiple sclerosis (MS) lesions with that of conventional DIR (C-DIR). METHODS: We retrospectively analyzed MRI data from 25 patients with MS who underwent a comprehensive imaging protocol, including 3D fluid-attenuated inversion recovery (FLAIR), C-DIR, and DLS-DIR. A voxel size of 1.3 × 1.3 × 1.4 mm(3) with a scan duration of 3 mins 55s were used for C-DIR, and isotropic 0.7 mm voxels with a scan time of 4 mins 23s were employed for DLS-DIR. Two neuroradiologists assessed the juxtacortical MS lesions during 2 separate reading sessions (one with C-DIR and the other with DLS-DIR). Lesions were categorized as subcortical white matter lesions, intracortical lesions, or mixed lesions involving both subcortical white and gray matter. The lesion counts per region were compared between the imaging techniques using the Wilcoxon signed-rank test. RESULTS: DLS-DIR detected a significantly higher number of juxtacortical MS lesions compared to C-DIR (Radiologist A: 211 lesions vs. 164 lesions; Radiologist B: 209 lesions vs. 157 lesions, P < 0.05). DLS-DIR also identified more intracortical lesions (Radiologist A: 22 additional lesions, Radiologist B: 34 additional lesions, P < 0.05) and more mixed lesions (Radiologist A: 46 additional lesions, Radiologist B: 42 additional lesions, P < 0.05). CONCLUSION: The DLS technology enables high-resolution, whole-brain DLS-DIR imaging within a 5 mins acquisition time, which can be seamlessly incorporated into routine clinical workflows. This approach substantially enhances the detection and evaluation of juxtacortical MS lesions.