Background. Recent studies have shown that the right ventricular (RV) analysis in myocardial perfusion imaging (MPI) SPECT can reveal some critical issues of heart disease. This study proposes a new algorithm for right ventricular 3D segmentation and quantification.
Methods. The proposed Quantitative Cardiac analysis in Nuclear Medicine imaging (QCard-NM) algorithm provides the RV myocardial surface estimation and create its contour using an iterative 3D model fitting method. The founded contour is then used for the RV quantitative analysis. The proposed method was assessed using various patient datasets and digital phantoms. First, the physician’s handmade contours were compared to the QCard-NM results using the Dice Similarity Coefficient (DSC). Second, the QCard-NM's repeatability was evaluated using repeated MPI scans in a single day. Third, the ability of QCard-NM analysis to classify the RCA stenosis was assessed. Fourth, the bias of Calculated RV cavity volume with the algorithms mentioned above was analyzed using 31 digital phantoms.
Results. The average DSC value was 𝟎𝟎. 𝟖𝟖𝟖𝟖 ± 𝟎𝟎. 𝟎𝟎𝟎𝟎 in the first dataset. In the second dataset, results imply that the Pearson correlation coefficient of the RV calculated cavity volume among two repeated scans was 0.87. The RV quantitative analysis using QCard-NM revealed an accuracy of 71.4% [p-value<0.05] detecting the RCA stenosis. In the phantom study, the mean absolute errors for calculated cavity volume were 22% and 38% for the QCard-NM and QPS, respectively.
Conclusion. We believe this preliminary study could lead to the development of a framework for improving the diagnosis of RCA abnormalities using RV quantitative analysis.