Ultrafast Deep Learning vs. Wave-CAIPI 3D FLAIR for Clinical Evaluation and Quantitative Assessment of White Matter Lesions

Motivation: Deep learning (DL) reconstructions show promise in accelerating MRI yet have not been extensively validated clinically, particularly for 3D sequences. Goal(s): To evaluate the diagnostic quality of DL-based 3D FLAIR compared to Wave-CAIPI-accelerated FLAIR in a clinical setting. Approach: This prospective study included 26 patients undergoing evaluation for demyelinating disease with Wave-CAIPI-FLAIR and a resolution-matched 6-fold-under-sampled Cartesian FLAIR acquisition with DL reconstruction. Results: DL-FLAIR reduced scan time (1:53 vs. 2:50) and showed better image quality with higher SNR/CNR, greater lesion conspicuity, and reduced noise compared to Wave-CAIPI-FLAIR, with high agreement in lesional and regional brain volumes between both methods. Impact: Deep learning reconstruction of 3D-FLAIR provides 30% less acquisition time and improved subjective image quality compared to a state-of-the-art accelerated technique. The excellent agreement in quantitative lesion and regional brain volumes suggests robustness for use in clinical and research studies.