most citedA Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches

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physics.med-ph20242 cited

Synergistic PET/CT Reconstruction Using a Joint Generative Model

Noel Jeffrey Pinton, Alexandre Bousse, Zhihan Wang +5

We propose in this work a framework for synergistic positron emission tomography (PET)/computed tomography (CT) reconstruction using a joint generative model as a penalty. We use a…

physics.med-ph2024

CConnect: Synergistic Convolutional Regularization for Cartesian T2* Mapping

Juan Molina, Alexandre Bousse, Tabita Catalán +5

Magnetic resonance imaging (MRI) is fundamental for the assessment of many diseases, due to its excellent tissue contrast characterization. This is based on quantitative techniques…

physics.med-ph2024

Fast-Track of F-18 Positron paths simulations using GANs

Youness Mellak, Konstantinos Chatzipapas, Alexandre Bousse +3

In recent years, the use of Monte Carlo (MC) simulations in the domain of Medical Physics has become a state-of-the-art technology that consumes lots of computational resources for…

physics.med-ph2024

Diffusion Posterior Sampling for Synergistic Reconstruction in Spectral Computed Tomography

Corentin Vazia, Alexandre Bousse, Béatrice Vedel +6

Using recent advances in generative artificial intelligence (AI) brought by diffusion models, this paper introduces a new synergistic method for spectral computed tomography (CT) r…

physics.med-ph202438 cited

A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches

Alexandre Bousse, Venkata Sai Sundar Kandarpa, Kuangyu Shi +4

Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the…