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Robust semi-automated quantification of cardiac MR perfusion using level set: Application to hypertrophic
Yoon-Chul Kim1, Sung Mok Kim2, Yeon Hyeon Choe2
1Clinical Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Insights
This study introduces a semi-automated method for analyzing cardiac MRI perfusion in hypertrophic cardiomyopathy (HCM) patients, improving accuracy and efficiency in quantifying myocardial ischemia caused by coronary microvascular dysfunction.
Area of Science:
- Cardiovascular Imaging
- Medical Physics
- Biomedical Engineering
Background:
- Hypertrophic cardiomyopathy (HCM) patients may experience myocardial ischemia due to coronary microvascular dysfunction.
- Existing clinical MR perfusion studies in HCM rely on manual border tracing for data analysis.
- There is a need for a robust, semi-automated method for myocardial perfusion quantification in HCM.
Purpose of the Study:
- To develop and validate a semi-automated analysis method for myocardial perfusion quantification in clinical HCM data.
- To overcome limitations of manual analysis in HCM patients with irregular myocardial shapes and low contrast.
Main Methods:
- Dynamic multi-slice stress perfusion MRI data from 18 HCM patients were analyzed.
- A semi-automated method using landmark selections (LV center, RV insertion point) was employed.
- Automated segmentation of endocardial and epicardial borders utilized distance regularized level set evolution.
Main Results:
- The automated epicardial border detection showed average radial distance errors of 7.5% (basal), 9.5% (mid), and 11.6% (apical) compared to manual tracing.
- Myocardial upslope measurements demonstrated high correlation between manual and proposed methods (r=0.964 in anterolateral, r=0.866 in inferoseptal).
Conclusions:
- The proposed semi-automated method is feasible for myocardial MR perfusion quantification in HCM patients.
- The method effectively handles irregular myocardial shapes and low image contrast common in HCM.
- This approach offers a more robust and potentially efficient alternative to manual analysis.
Background:
Recently there have been several clinical MR perfusion studies in patients with hypertrophic cardiomyopathy (HCM) who may suffer from myocardial ischemia due to coronary microvascular dysfunction. In these studies, data analysis relied on a manual procedure of tracing epicardial and endocardial borders. The goal of this work is to develop and validate a robust semi-automated analysis method for myocardial perfusion quantification in clinical HCM data.
Method:
Dynamic multi-slice stress perfusion MRI data were acquired from 18 HCM patients. The proposed semi-automated method required user input of two landmark selections: LV center point and RV insertion point. Automated segmentations of the endocardial and epicardial borders were performed in three short-axis slices using distance regularized level set evolution on RV, LV, and myocardial enhancement frames.
Results:
The proposed automated epicardial border detection method resulted in average radial distance errors of 7.5%, 9.5%, and 11.6% in basal, mid, and apical slices, respectively, when compared to manual tracing of the borders as a reference. In linear regression analysis, the highest correlation of myocardial upslope measurements was observed between the manual method and the proposed method in the anterolateral section (r=0.964), and the lowest correlation was observed in the inferoseptal section (r=0.866).
Conclusion:
The proposed semi-automated method for myocardial MR perfusion quantification is feasible in HCM patients who typically show (1) irregular myocardial shape and (2) low image contrast between the myocardium and its surrounding regions due to coronary microvascular disease.
