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Updated: Apr 21, 2026

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Pre-Imaging Clinical Factors Associated With Cardiac MR Image Quality Using Large Language Model-Enabled Data

Hong Yu1,2, Masha Bondarenko2, Ali Nowroozi2

  • 1Department of Radiology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.

Journal of Magnetic Resonance Imaging : JMRI
|April 20, 2026
PubMed
Summary
This summary is machine-generated.

Poor cardiac MR image quality is linked to cognitive/communication impairment and respiratory issues. Identifying these factors pre-imaging may help improve cardiac MRI diagnostics and reduce repeat scans.

Keywords:
artificial intelligencecardiac MRimage qualitylarge language model

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Area of Science:

  • Medical imaging
  • Artificial Intelligence
  • Cardiology

Background:

  • Poor cardiac magnetic resonance (MR) image quality necessitates repeat examinations, impacting patient care and clinical decisions.
  • Improving cardiac MR image quality is crucial for accurate diagnosis and efficient healthcare delivery.

Purpose of the Study:

  • To determine if pre-imaging clinical information, extracted by a large language model (LLM), is independently associated with cardiac MR image quality.
  • To investigate the predictive value of patient clinical data for cardiac MR image quality outcomes.

Main Methods:

  • Retrospective analysis of 1006 adult cardiac MR examinations across 1.5T and 3T scanners.
  • Utilized a HIPAA-compliant LLM to extract clinical information and assign image quality labels from radiology reports.
  • Employed multivariable logistic regression and chi-square tests to assess associations between clinical variables and image quality.

Main Results:

  • LLM-derived image quality labels demonstrated substantial agreement with expert assessments (κ=0.689).
  • Cognitive/communication impairment (OR 1.81) and respiratory issues (OR 1.57) were significantly associated with poor cardiac MR image quality.
  • These associations remained significant after adjusting for repeat imaging (p<0.001 for cognitive impairment, p=0.027 for respiratory compromise).

Conclusions:

  • Cognitive/communication impairment and respiratory compromise are independent predictors of poor cardiac MR image quality.
  • Pre-imaging clinical data, extracted via LLM, can identify patients at higher risk for suboptimal cardiac MR imaging.
  • This finding may inform strategies to optimize cardiac MR imaging protocols and reduce image quality-related failures.