Keywords: Other Neurodegeneration, MR Value, AI/ML Image Reconstruction, Brain, Translational studies, Neurodegeneration
Motivation: Deep-learning-accelerated MPRAGE holds potential to enhance image resolution, reduce acquisition times, and improve diagnostic precision. However, there is currently a lack of clinical validation regarding its performance in neuroimaging.
Goal(s): To optimize and assess the performance of deep-learning-accelerated MPRAGE in comparison to Wave-CAIPI MPRAGE for non-contrast T1-weighted volumetric brain imaging.
Approach: In this prospective clinical study, we systematically optimized and implemented a novel deep-learning-accelerated MPRAGE sequence and compared against Wave-CAIPI MPRAGE, a state-of-the-art acceleration method.
Results: Deep-learning-accelerated MPRAGE enhances resolution and grey-white matter differentiation compared to Wave-CAIPI MPRAGE, with equivalent volumetric estimation in most brain regions.
Impact: Deep-learning-accelerated MPRAGE yields sharper, higher-resolution images while preserving equivalent volumetric estimations. This technique holds significant potential for deploying deep learning across various medical imaging disciplines, potentially enabling faster and more precise disease characterization.
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