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Multi-Branch Generative Models for Multichannel Imaging with an Application to PET/CT Joint Reconstruction
April 16, 2024, 4:48 a.m. | Noel Jeffrey Pinton, Alexandre Bousse, Catherine Cheze-Le-Rest, Dimitris Visvikis
cs.CV updates on arXiv.org arxiv.org
Abstract: This paper presents a proof-of-concept approach for learned synergistic reconstruction of medical images using multi-branch generative models. Leveraging variational autoencoders (VAEs) and generative adversarial networks (GANs), our models learn 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, in a similar fashion to multichannel dictionary learning (DiL). We demonstrate the efficacy …
abstract adversarial application arxiv autoencoders concept cs.cv denoising eess.iv enabling gans generative generative adversarial networks generative models image images imaging learn medical networks paper pet physics.med-ph proof-of-concept type variational autoencoders
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