<scp>AI</scp> ‐Powered Gradient Echo Plural Contrast Imaging ( <scp>AI</scp> ‐ <scp>GEPCI</scp> )—A Comprehensive Neurological Protocol From a Single <scp>MRI</scp> Scan

BACKGROUND: MRI is essential for diagnosing and monitoring neurological diseases. Conventional protocols require multiple sequences to obtain complementary contrasts, increasing scan time, cost, and tolerability. Generating multiple contrasts from a single acquisition may streamline workflow while maintaining clinical utility. PURPOSE: To train attention-based convolutional neural networks (ACNNs) to generate clinical-quality Fluid-Attenuated-Inversion-Recovery (FLAIR), Magnetization-Prepared-Rapid-Gradient-Echo (MPRAGE), R2* maps, and derived contrasts from a single Gradient Echo Plural Contrast Imaging (GEPCI) acquisition. STUDY TYPE: Retrospective. POPULATION: 43 MRI scans from individuals with multiple sclerosis (25/18 F/M, 49 ± 11 years-of-age). FIELD STRENGTH/SEQUENCE: 3 T MRI, 3D GEPCI, MPRAGE, and FLAIR. ASSESSMENT: Technical quality of AI-generated contrasts was evaluated against directly acquired MRI using structural similarity index (SSIM). Clinical image quality was assessed by physicians. Lesion volumes and counts were obtained using automated segmentation. STATISTICAL TESTS: One-sample one-sided Wilcoxon signed-rank test was used to establish the clinical quality of images. Agreement between native- and AI-derived lesion volume and lesion count measurements was assessed using intraclass correlation coefficients (ICC). Quantitative accuracy for R2* maps was evaluated using normalized root-mean-square error (NRMSE). RESULTS: = 0.933, ICC = 0.988 and ICC = 0.967, respectively. DATA CONCLUSION: AI-GEPCI generated multiple clinically relevant MRI contrasts from a single GEPCI acquisition with high similarity to corresponding acquired images, supporting high-quality, intrinsically co-registered multi-contrast brain evaluation. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 1.