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

Mareike Thies; Noah Maul; Siyuan Mei; Laura Pfaff; Nastassia Vysotskaya; Mingxuan Gu; Jonas Utz; Dennis Possart; Lukas Folle; Fabian Wagner; Andreas Maier

arXiv · 2404.14747

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

Mareike Thies·Noah Maul·Siyuan Mei·Laura Pfaff·Nastassia Vysotskaya·Mingxuan Gu·Jonas Utz·Dennis Possart·Lukas Folle·Fabian Wagner·Andreas Maier

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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.

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Mareike Thies, Noah Maul, Siyuan Mei, Laura Pfaff, Nastassia Vysotskaya, Mingxuan Gu, Jonas Utz, Dennis Possart, Lukas Folle, Fabian Wagner, Andreas Maier. 2024-04-23. Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images. https://arxiv.org/abs/2404.14747

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