A novel analysis algorithm for potential quantitative assessment of myocardial computed tomography perfusion

Abstract

Rationale and objectives: Cardiac computed tomography (CT) has emerged as a robust modality for imaging coronary stenosis and has recently been used to evaluate myocardial abnormalities such as ischemic perfusion defects and infarction. We developed a new image analysis algorithm for the semiautomatic and quantitative assessment of myocardial perfusion by CT.

Materials and methods: The algorithm semiautomatically segments two-dimensional short-axis reformatted DICOM images of the left ventricle into regions of interest (ROIs) in accordance with American Heart Association (AHA) standards and is capable of creating nine further ROI subsegments. This includes separate endocardial, mid-ventricle, and epicardial layers. Image intensity values (Hounsfield unit) and relative myocardial thickness are quantitatively reported for each ROI and segment.

Results: The algorithm allows comparison of the HU values at the same ROI locations between rest and stress. The reproducibility is very good; ICC 0.89 for rest images, 0.83 for stress images. The mean time for generating ROIs for the entire heart was 11 minutes versus 22 minutes for manual tracing.

Conclusion: The algorithm reports parameters relevant for evaluation of stress perfusion CT studies and will allow more accurate and reproducible analysis in cardiac CT research.

Keywords: Computed tomography; algorithm; cardiac imaging; image analysis; myocardial perfusion.