Clinical evaluation of deep learning–accelerated 3D FLAIR imaging at 1.5T and 3T for visualization of cerebral white matter lesions

Background Fluid-attenuated inversion recovery (FLAIR) imaging is a standard sequence used for the detection of white matter lesions; however, 1-mm isotropic FLAIR can take 5–7 min at 1.5T and 3T using routine methods, limiting routine use of high-resolution 3D FLAIR imaging. Purpose To evaluate a deep learning (DL)–based unrolled optimization 3D FLAIR technique, Sonic DL™ 3D (SDL), in comparison to a standard 3D FLAIR protocol, both combined with DL-based post-processing (AIR™ Recon DL) at 1.5T and 3T. Material and Methods A total of 67 patients (15 scanned at 1.5T and 52 at 3T) with suspected white matter lesions were included. Two neuroradiologists performed blinded, head-to-head image quality evaluation (motion, noise, and overall diagnostic quality). Quantitative lesion volume and lesion counts were derived from automated segmentation using SPM 12. Cohen's kappa, Wilcoxon signed-rank tests, and paired t -tests were applied. Results At 1.5T, SDL FLAIR (4 min 53 s) reduced scan times by 15% compared to standard 3D FLAIR (5 min 43 s) and performed similarly or better in lesion visualization and image quality, with significantly ( P <0.05) improved noise quality. At 3T, SDL FLAIR (2 min 45 s) achieved a reduction in scan time of approximately 35% compared to standard 3D FLAIR (4 min 15 s) and showed improved lesion conspicuity (periventricular, juxtacortical, and other regions) and image quality metrics. However, except for noise quality, these differences did not reach statistical significance despite moderate to almost perfect interrater agreement (Cohen's kappa 0.55–0.85; P <0.001). Conclusion Accelerated SDL FLAIR demonstrated improved diagnostic quality, comparable lesion conspicuity, and volumetric measurements to standard 3D FLAIR sequence at 1.5T and 3T.