Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images

arXiv:2404.14747v1 Announce Type: new
Abstract: Motion artifacts can compromise the diagnostic value of computed tomography (CT) images. Motion correction approaches require a per-scan estimation of patient-specific motion patterns. In this work, we train a score-based model to act as a probability density estimator for clean head CT images. Given the trained model, we quantify the deviation of a given motion-affected CT image from the ideal distribution through likelihood computation. We demonstrate that the likelihood can be utilized as a surrogate …

abstract act arxiv compensation cs.cv diagnostic differentiable estimator head images patient patterns per probability train type value work