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: AI-generated FLAIR and MPRAGE achieved mean SSIM values of 0.923 ± 0.028 and 0.935 ± 0.022, respectively. Generated R2* maps achieved a mean SSIM of 0.996 ± 0.006 and NRMSE of 0.031 ± 0.020. Physicians-assigned mean clinical quality ratings of 4.2 for GEPCI-FLAIR and 4.5 for GEPCI-MPRAGE exceeded the 4.0 clinical standard on a 1-to-5 scale. Lesion volume and count comparisons from automated segmentation showed strong agreement between AI-generated and ground-truth measurements: R2 = 0.988 and R2 = 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.
Keywords: attention convolutional neural networks (ACNN); central vein sign (CVS); gradient Echo plural contrast imaging (GEPCI); magnetic resonance imaging (MRI); multiple sclerosis (MS); paramagnetic rim lesion (PRL).
This study developed a new MRI method called AI‐GEPCI that can create many important brain images from a single scan. Normally, people with neurological diseases need several different MRI scans, which take a long time, are costly, and can be hard to tolerate. We trained artificial intelligence (AI) to turn one GEPCI scan into several clinical images (called FLAIR, MPRAGE, and R2*) that doctors routinely use. The AI‐generated images looked very similar to standard MRI images and were rated as high quality by physicians. This approach may allow faster, more comfortable, and more consistent MRI exams for brain diseases.
© 2026 The Author(s). Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.