bibtext: @InProceedings{ Thi_Differentiable_MICCAI2024,
                   author = { Thies, Mareike and Maul, Noah and Mei, Siyuan and Pfaff, Laura and Vysotskaya, Nastassia and Gu, Mingxuan and Utz, Jonas and Possart, Dennis and Folle, Lukas and Wagner, Fabian and Maier, Andreas },
                   title = { { Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
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 metric for motion artifact severity in the CT image facilitating the application of an iterative, gradient-based motion compensation algorithm. By optimizing the underlying motion parameters to maximize likelihood, our method effectively reduces motion artifacts, bringing the image closer to the distribution of motion-free scans. Our approach achieves comparable performance to state-of-the-art methods while eliminating the need for a representative data set of motion-affected samples. This is particularly advantageous in real-world applications, where patient motion patterns may exhibit unforeseen variability, ensuring robustness without implicit assumptions about recoverable motion types.
title: Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images
authors: Thies, Mareike Maul, Noah Mei, Siyuan Pfaff, Laura Vysotskaya, Nastassia Gu, Mingxuan Utz, Jonas Possart, Dennis Folle, Lukas Wagner, Fabian Maier, Andreas