Assessment of Deep-Learning Based Motion Compensation on Detection of Perfusion Defects In Cardiac-Gated Spect Images

Motion compensation is an effective approach for noise suppression and motion blur reduction in cardiac-gated SPECT imaging. In this work, we investigate the potential benefit of applying motion compensation with a deep learning (DL) network for assessment of perfusion defects in gated images. In addition to evaluating motion-compensation accuracy on clinical acquisitions, we also conduct a receiver-operating characteristic (ROC) study to assess the detection performance of perfusion detects when DL motion compensation is used to generate the perfusion images. For this task we use a clinical model observer on a set of hybrid studies generated from clinical acquisitions in which the perfusion defects are introduced as ground truth. The results in the experiments demonstrate that DL motion compensation can yield higher detection accuracy, with the area under the ROC curve being 0.848, compared to 0.823 for motion-compensation using optical flow equation, and 0.800 for traditional ungated studies.