Numerical observer for cardiac motion assessment using machine learning

In medical imaging, image quality is commonly assessed by measuring the performance of a human observer performing a specific diagnostic task. However, in practice studies involving human observers are time consuming and difficult to implement. Therefore, numerical observers have been developed, aiming to predict human diagnostic performance to facilitate image quality assessment. In this paper, we present a numerical observer for assessment of cardiac motion in cardiac-gated SPECT images. Cardiac-gated SPECT is a nuclear medicine modality used routinely in the evaluation of coronary artery disease. Numerical observers have been developed for image quality assessment via analysis of detectability of myocardial perfusion defects (e.g., the channelized Hotelling observer), but no numerical observer for cardiac motion assessment has been reported. In this work, we present a method to design a numerical observer aiming to predict human performance in detection of cardiac motion defects. Cardiac motion is estimated from reconstructed gated images using a deformable mesh model. Motion features are then extracted from the estimated motion field and used to train a support vector machine regression model predicting human scores (human observers' confidence in the presence of the defect). Results show that the proposed method could accurately predict human detection performance and achieve good generalization properties when tested on data with different levels of post-reconstruction filtering.