Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction

• URL: http://arxiv.org/abs/2409.06198v1
• Date: Tue, 10 Sep 2024 03:57:31 GMT
• Title: Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction
• Authors: Cameron Dennis Pain, Yasmeen George, Alex Fornito, Gary Egan, Zhaolin Chen,
• Abstract summary: 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. 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.
• Score: 0.09362267584678274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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