Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction
<|reference_start|>Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction: Low-dose positron emission tomography (PET) image reconstruction methods have potential to significantly improve PET as an imaging modality. Deep learning provides a promising means of incorporating prior information into the image reconstruction problem to produce quantitatively accurate images from compromised signal. Deep learning-based methods for low-dose PET are generally poorly conditioned and perform unreliably on images with features not present in the training distribution. We present a method which explicitly models deep latent space features using a robust kernel representation, providing robust performance on previously unseen dose reduction factors. Additional constraints on the information content of deep latent features allow for tuning in-distribution accuracy and generalisability. Tests with out-of-distribution dose reduction factors ranging from $\times 10$ to $\times 1000$ and with both paired and unpaired MR, demonstrate significantly improved performance relative to conventional deep-learning methods trained using the same data. Code:https://github.com/cameronPain<|reference_end|>
@article{pain2024deep,
    title={Deep kernel representations of latent space features for low-dose PET-MR
  imaging robust to variable dose reduction},
    author={Cameron Dennis Pain, Yasmeen George, Alex Fornito, Gary Egan, Zhaolin
  Chen},
    journal={arXiv preprint arXiv:2409.06198},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06198},
    primaryClass={cs.CV}
}