Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909472241221632 |
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| author | Pinton, Noel Jeffrey Bousse, Alexandre Cheze-Le-Rest, Catherine Visvikis, Dimitris |
| author_facet | Pinton, Noel Jeffrey Bousse, Alexandre Cheze-Le-Rest, Catherine Visvikis, Dimitris |
| contents | This paper presents a novel approach for learned synergistic reconstruction of medical images using multibranch generative models. Leveraging variational autoencoders (VAEs), our model learns from pairs of images simultaneously, enabling effective denoising and reconstruction. Synergistic image reconstruction is achieved by incorporating the trained models in a regularizer that evaluates the distance between the images and the model. We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/computed tomography (CT) datasets, showcasing improved image quality for low-dose imaging. Despite challenges such as patch decomposition and model limitations, our results underscore the potential of generative models for enhancing medical imaging reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08748 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction Pinton, Noel Jeffrey Bousse, Alexandre Cheze-Le-Rest, Catherine Visvikis, Dimitris Image and Video Processing Computer Vision and Pattern Recognition Medical Physics This paper presents a novel approach for learned synergistic reconstruction of medical images using multibranch generative models. Leveraging variational autoencoders (VAEs), our model learns from pairs of images simultaneously, enabling effective denoising and reconstruction. Synergistic image reconstruction is achieved by incorporating the trained models in a regularizer that evaluates the distance between the images and the model. We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/computed tomography (CT) datasets, showcasing improved image quality for low-dose imaging. Despite challenges such as patch decomposition and model limitations, our results underscore the potential of generative models for enhancing medical imaging reconstruction. |
| title | Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2404.08748 |