Purpose: Quantifying stenosis in cardiac computed tomography angiography (CTA) images remains a difficult task, as image noise and cardiac motion can degrade image quality and distort underlying anatomic information. The purpose of this study was to develop a computational framework to objectively assess the precision of quantifying coronary stenosis in cardiac CTA.
Approach: The framework used models of coronary vessels and plaques, asymmetric motion point spread functions, CT image blur (task-based modulation transfer functions) and noise (noise-power spectrums), and an automated maximum-likelihood estimator implemented as a matched template squared-difference operator. These factors were integrated into an estimability index (e ′ ) as a task-based measure of image quality in cardiac CTA. The e ′ index was applied to assess how well it can to predict the quality of 132 clinical cases selected from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain trial. The cases were divided into two cohorts, high quality and low quality, based on clinical scores and the concordance of clinical evaluations of cases by experienced cardiac imagers. The framework was also used to ascertain protocol factors for CTA Biomarker initiative of the Quantitative Imaging Biomarker Alliance (QIBA).
Results: The e ′ index categorized the patient datasets with an area under the curve of 0.985, an accuracy of 0.977, and an optimal e ′ threshold of 25.58 corresponding to a stenosis estimation precision (standard deviation) of 3.91%. Data resampling and training–test validation methods demonstrated stable classifier thresholds and receiver operating curve performance. The framework was successfully applicable to the QIBA objective.
Conclusions: A computational framework to objectively quantify stenosis estimation task performance was successfully implemented and was reflective of clinical results in the context of a prominent clinical trial with diverse sites, readers, scanners, acquisition protocols, and patients. It also demonstrated the potential for prospective optimization of imaging protocols toward targeted precision and measurement consistency in cardiac CT images.
Estimating parameters of clinical significance, like coronary stenosis, accurately and precisely from cardiac CT images remains a difficult task as image noise and cardiac motion can degrade image quality and distort underlying anatomic information. The purpose of this study was to develop a computational framework to objectively quantify stenosis estimation task performance of an ideal estimator in cardiac CT. The resulting scalar figure-of-merit, the estimability index (e’), serves as a cardiac CT specific task-based measure of image quality. The developed computational framework consisted of idealized coronary vessel and plaque models, asymmetric motion point spread functions (mPSF), CT image blur (MTF) and noise operators (NPS), and an automated maximum-likelihood estimator (MLE) implemented as a matched template squared-difference operator. Using this framework, e’ values were calculated for 131 clinical case scenarios from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial. The calculated e’ results were then utilized to classify patient cases into two exclusive cohorts, high-quality and low-quality, characterized by clinically meaningful differences in image quality. An e’ based linear classifier categorized the 131 patient datasets with an AUC of 0.96 (6 false-positives and 10 false-negatives), compared to an AUC of 0.89 (4 false-positives and 20 false-negatives) for a linear classifier based on contrast-to-noise ratio (CNR). In summary, a computational framework to objectively quantify stenosis estimation task performance was successfully implemented and was reflective of clinical results in the context of subset of a large clinical trial (PROMISE) with diverse sites, readers, scanners, acquisition protocols, and patient types.