Keywords: Multiple Sclerosis, Neuro
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords