Personalized Cine Cardiac Mri Protocol Optimization Using a Retrieval-Augmented Large Language Model: A Prospective Comparison With Technologist-Optimized Protocol Publisher Pubmed

Summary: Can AI tailor cardiac MRI scans? A nonrandomized study found LLM-guided protocols matched expert image quality with fewer repeats; randomized tests are needed. #CardiacMRI #MedicalAI

Dehghani S ; Farashahi A ; Hammood I R
Source: Journal of Magnetic Resonance Imaging Published:2026

Abstract

Background: Cine cardiac magnetic resonance imaging (MRI) protocols are typically based on static templates, which may not optimally address individual patient limitations such as arrhythmia or limited breath-hold capacity. Purpose: To test whether an artificial intelligence (AI) framework using a large language model with retrieval-augmented generation (LLM-RAG) can provide cine cardiac MRI protocols that are equivalent or noninferior to those of technologist-optimized protocols. Study Type: Prospective, intra-individual comparative, nonrandomized. Population/Subjects: Sixty-eight patients (mean age 46 ± 18 years; 39 women) referred for clinical cardiac MRI, including patients with normal function as well as those with arrhythmia, implants, or limited breath-hold capacity. Field Strength/Sequence: 1.5 T, cine cardiac MRI. Assessment: Each participant underwent cine cardiac MRI twice in one session. First with a technologist-optimized protocol, then with an AI-optimized protocol (fixed order). Image quality and artifact suppression were assessed using four-point Likert scales by two blinded readers. Left ventricular (LV) functional parameters and repeat acquisitions were recorded. Statistical Tests: Wilcoxon signed-rank test, McNemar's test, paired t-tests, and Bland–Altman analysis. Significance set at p < 0.05. Results: Compared to technologist-, AI-optimized cine protocols demonstrated significantly higher image quality (3.69 ± 0.47 vs. 3.00 ± 0.49, p < 0.001) and artifact suppression (3.19 ± 0.35 vs. 2.17 ± 0.45, p < 0.001). The AI-optimized protocol reduced repeat acquisitions by 68% (6 vs. 19 repeats, p = 0.028). No significant differences were observed in LV functional parameters between both protocols (all p > 0.05). Bland–Altman analysis showed minimal bias for all LV functional parameters. Data Conclusion: In this nonrandomized study, a LLM-RAG framework generated cine cardiac MRI protocols that were noninferior to technologist-optimized protocols for image quality and demonstrated high agreement for LV functional measurements with fewer repeat acquisitions. These findings demonstrated the feasibility of AI-guided patient-adaptive protocoling. Randomized trials are warranted. Evidence Level: 2. Technical Efficacy: Stage 2. © 2026 International Society for Magnetic Resonance in Medicine.