[Clincial Research Progress in Using Magnetic Resonance Imaging to Assess Myocardial Fibrosis in Hypertrophic

Ke Shi1, Shiqin Yu1, Dong Xia1,2

  • 1( 610041) Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.

Insights

Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death. Cardiac MRI techniques like late gadolinium enhancement and T1 mapping are advancing the noninvasive assessment of myocardial fibrosis in HCM patients.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Biomedical Engineering

Background:

  • Hypertrophic cardiomyopathy (HCM) is the most common primary cardiomyopathy and a major cause of sudden cardiac death in young individuals.
  • Myocardial fibrosis is a key pathological feature of HCM, contributing to adverse cardiac remodeling, arrhythmias, and heart failure.
  • China has the world's largest population of HCM patients, with increasing prevalence.

Purpose of the Study:

  • To review recent advancements in cardiac magnetic resonance imaging (MRI) for assessing myocardial fibrosis in HCM.
  • To highlight the utility of various MRI techniques in characterizing HCM tissue and predicting patient prognosis.

Main Methods:

  • Review of current cardiac MRI techniques for myocardial fibrosis evaluation in HCM.
  • Focus on late gadolinium enhancement (LGE) and T1 mapping.
  • Exploration of emerging techniques including T1ρ mapping and MRI-based radiomics and machine learning.

Main Results:

  • Late gadolinium enhancement (LGE) and T1 mapping are established MRI methods for detecting myocardial fibrosis in HCM.
  • These techniques aid in understanding HCM pathophysiology and patient risk stratification.
  • Novel MRI approaches show promise for more comprehensive fibrosis assessment.

Conclusions:

  • Cardiac MRI is the gold standard for noninvasive evaluation of myocardial fibrosis in HCM.
  • Advanced MRI techniques offer improved tissue characterization and prognostic value for HCM patients.
  • Future research directions include integrating radiomics and machine learning with MRI for enhanced HCM management.