Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.06198 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910597426184192 |
|---|---|
| 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 |