Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images
<|reference_start|>Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images: 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.<|reference_end|>
@article{thies2024differentiable,
    title={Differentiable Score-Based Likelihoods: Learning CT Motion Compensation
  From Clean Images},
    author={Mareike Thies, Noah Maul, Siyuan Mei, Laura Pfaff, Nastassia
  Vysotskaya, Mingxuan Gu, Jonas Utz, Dennis Possart, Lukas Folle, Fabian
  Wagner, Andreas Maier},
    journal={arXiv preprint arXiv:2404.14747},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14747},
    primaryClass={cs.CV}
}