Personalized Cine Cardiac MRI Protocol Optimization Using a Retrieval‐Augmented Large Language Model: A Prospective Comparison With Technologist‐Optimized Protocol
Year: 2026
Type: article
Study design: Clinical Trial
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.
DOI: 10.1002/jmri.70566
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7219756215