Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910597426184192 |
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| author | Pain, Cameron Dennis George, Yasmeen Fornito, Alex Egan, Gary Chen, Zhaolin |
| author_facet | Pain, Cameron Dennis George, Yasmeen Fornito, Alex Egan, Gary Chen, Zhaolin |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06198 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction Pain, Cameron Dennis George, Yasmeen Fornito, Alex Egan, Gary Chen, Zhaolin Computer Vision and Pattern Recognition 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 |
| title | Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.06198 |