Myocardial Regional Shortening from 4D Cardiac CT Angiography for the Detection of Left Ventricular Segmental Wall Motion Abnormality

Purpose: To investigate whether endocardial regional shortening computed from four-dimensional (4D) CT angiography (RSCT) can be used as a decision classifier to detect the presence of left ventricular (LV) wall motion abnormalities (WMAs). Materials and Methods: One hundred electrocardiographically gated cardiac 4D CT studies (mean age, 59 years +/- 14 [SD]; 61 male patients) conducted between April 2018 and December 2020 were retrospectively evaluated. Three experts labeled LV wall motion in each of the 16 American Heart Association (AHA) segments as normal or abnormal; they also measured peak RSCT across one heart-beat in each segment. The data set was split evenly into training and validation groups. During training, interchangeability of RSCT thresholding with experts to detect WMA was assessed using the individual equivalence index (gamma), and an optimal threshold of the peak RSCT (RSCT*) that achieved maximum agreement was identified. RSCT* was then validated using the validation group, and the effect of AHA segment-specific thresholds was evaluated. Agreement was assessed using kappa statistics. Results: The optimal threshold, RSCT* of-0.19, when applied to all AHA segments, led to high agreement (agreement rate = 92.17%, kappa = 0.82) and interchangeability with experts (gamma =-2.58%). The same RSCT* also achieved high agreement in the validation group (agreement rate = 90.29%, kappa = 0.76, gamma =-0.38%). The use of AHA segment-specific thresholds (range: 0.16 to-0.23 across AHA segments) slightly improved agreement (1.79% increase). Conclusion: RSCT thresholding was interchangeable with expert visual analysis in detecting segmental WMA from 4D CT and may be used as an objective decision classifier.