AI-Powered Gradient Echo Plural Contrast Imaging (AI-GEPCI)-A Comprehensive Neurological Protocol From a Single MRI...

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.

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.

Retrospective.

43 MRI scans from individuals with multiple sclerosis (25/18 F/M, 49 ± 11 years-of-age).

3 T MRI, 3D GEPCI, MPRAGE, and FLAIR.

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.

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).

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.

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.

2.

Stage 1.