ArticlemedRxiv : the preprint server for health sciences2026
AI-powered Gradient Echo Plural Contrast Imaging (AI-GEPCI) - a Comprehensive Multiparametric Neurological Protocol from a Single MRI Scan.
Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Updated by
Authors and funding
9 authors.
Funding
Abstract
Background: MRI plays an essential role in diagnosing and monitoring neurological diseases. Conventional protocols rely on 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: Train attention-based convolutional neural networks (ACNNs) to generate clinical-quality FLAIR, MPRAGE, R2*, and derived contrasts from a single Gradient Echo Plural Contrast Imaging (GEPCI) acquisition, enabling multi-contrast imaging from one scan. Study Type: Retrospective. Population: 43 MRI scans from individuals with multiple sclerosis (25/18 F/M, 49±11 years old). Field Strength/Sequence: 3T MRI was used to obtain 3D GEPCI, MPRAGE, and FLAIR sequences. Assessment: Technical quality of the AI-generated contrasts was evaluated against directly acquired MRI images using structural similarity index (SSIM). Quantitative accuracy for R2* maps was evaluated using normalized root-mean-square error (NRMSE). Clinical image quality was assessed by expert physicians. Lesion volumes and counts were obtained using automated segmentation. Results: AI-generated FLAIR and MPRAGE images achieved mean SSIM values of 0.923±0.028 and 0.935±0.022, respectively. The generated R2* maps achieved a mean SSIM of 0.996±0.006, with quantitative accuracy reflected by an NRMSE of 0.031±0.020. Physicians rated GEPCI-FLAIR images at 4.2 and GEPCI-MPRAGE images at 4.5 (on a 1-to-5 scale), both exceeding the clinically routine standard of 4.0. Lesion volume and count comparisons from automated segmentation showed strong agreement between AI-generated and ground-truth measurements (R Conclusion: AI-GEPCI generated multiple clinically relevant MRI contrasts from a single GEPCI acquisition with high similarity to corresponding acquired images. Radiological reviews and quantitative analyses supported the feasibility of producing high-quality, intrinsically co-registered multi-contrasts for comprehensive brain evaluation.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.