Keywords: Tumors (Pre-Treatment), Neuro, MR value, AI & Machine Learning
Motivation: Deep learning (DL)-enabled reconstruction has emerged as a promising approach to accelerate MRI exams; however, the performance of DL-accelerated 3D sequences for the detection of intracranial enhancing lesions has not been clinically investigated.
Goal(s): To evaluate post-contrast DL-accelerated 3D-T1-MPRAGE compared to state-of-the-art Wave-CAIPI accelerated 3D T1-MPRAGE for evaluation of intracranial enhancing lesions.
Approach: Two neuroradiologists performed head-to-head evaluation of 115 cases of post-contrast DL- vs. Wave-CAIPI-MPRAGE for visualization of dural, parenchymal, leptomeningeal, and ependymal enhancement; sharpness; noise; artifacts; and overall diagnostic quality.
Results: Highly accelerated post-contrast DL-T1-MPRAGE achieved noninferior image quality to the standard clinically validated Wave-CAIPI accelerated sequence.
Impact: Deep-learning-accelerated post-contrast 3D T1-MPRAGE demonstrates robust diagnostic quality in visualizing enhancing intracranial pathology in all compartments while maintaining similar perception of noise and artifact. DL offers a powerful approach to accelerating post-contrast 3D T1-MPRAGE for clinical and research studies.
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