Abstract:
This paper presents a novel approach for learned synergistic reconstruction of medical images using multi-branch generative models. Leveraging variational autoencoders (V...Show MoreMetadata
Abstract:
This paper presents a novel approach for learned synergistic reconstruction of medical images using multi-branch 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.
Published in: IEEE Transactions on Radiation and Plasma Medical Sciences ( Early Access )
Funding Agency:
INSERM, UMR 1101, Univ. Brest LaTIM, Brest, France
INSERM, UMR 1101, Univ. Brest LaTIM, Brest, France
INSERM, UMR 1101, Univ. Brest LaTIM, Brest, France
Nuclear Medicine Department, Poitiers University Hospital, Poitiers, France
INSERM, UMR 1101, Univ. Brest LaTIM, Brest, France
INSERM, UMR 1101, Univ. Brest LaTIM, Brest, France
INSERM, UMR 1101, Univ. Brest LaTIM, Brest, France
INSERM, UMR 1101, Univ. Brest LaTIM, Brest, France
Nuclear Medicine Department, Poitiers University Hospital, Poitiers, France
INSERM, UMR 1101, Univ. Brest LaTIM, Brest, France