38 citations · 55 across the 19 of their papers we have counts for
19 papers
Continuous 3-D Latent Diffusion for Medical Image Generation and Reconstruction
Youness Mellak, Antoine De Paepe, Dimitris Visvikis +1
High-resolution three-dimensional (3-D) medical latent diffusion remains limited by the cost of decoding full volumes, which existing latent autoencoders (AEs) handle through overl…
Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model
Hang Xu, Alexandre Bousse, Alessandro Perelli
Dual-energy X-ray Computed Tomography (DECT) constitutes an advanced technology which enables automatic decomposition of materials in clinical images without manual segmentation us…
Joint Reconstruction of Activity and Attenuation in PET by Diffusion Posterior Sampling in Wavelet Coefficient Space
Clémentine Phung-Ngoc, Alexandre Bousse, Antoine De Paepe +6
Attenuation correction (AC) is necessary for accurate activity quantification in positron emission tomography (PET). Conventional reconstruction methods typically rely on attenuati…
Adaptive Diffusion Models for Sparse-View Motion-Corrected Head Cone-beam CT
Antoine De Paepe, Alexandre Bousse, Clémentine Phung-Ngoc +2
Cone-beam computed tomography (CBCT) is an imaging modality widely used in head and neck diagnostics due to its accessibility and lower radiation dose. However, its relatively long…
Material Decomposition in Photon-Counting Computed Tomography with Diffusion Models: Comparative Study and Hybridization with Variational Regularizers
Corentin Vazia, Thore Dassow, Alexandre Bousse +6
Photon-counting computed tomography (PCCT) has emerged as a promising imaging technique, enabling spectral imaging and material decomposition (MD). However, images typically suffer…
Semi-Supervised Learning for Dose Prediction in Targeted Radionuclide: A Synthetic Data Study
Jing Zhang, Alexandre Bousse, Chi-Hieu Pham +2
Targeted Radionuclide Therapy (TRT) is a modern strategy in radiation oncology that aims to administer a potent radiation dose specifically to cancer cells using cancer-targeting r…
Dual-Input Dynamic Convolution for Positron Range Correction in PET Image Reconstruction
Youness Mellak, Alexandre Bousse, Thibaut Merlin +3
Positron range (PR) blurring degrades positron emission tomography (PET) image resolution, particularly for high-energy emitters like gallium-68 (68 Ga). We introduce Dual-Input Dy…
Evaluation of Deep Learning-based Scatter Correction on a Long-axial Field-of-view PET scanner
Baptiste Laurent, Alexandre Bousse, Thibaut Merlin +3
Objective: Long-axial field-of-view (LAFOV) positron emission tomography (PET) systems allow higher sensitivity, with an increased number of detected lines of response induced by a…
Joint Reconstruction of the Activity and the Attenuation in PET by Diffusion Posterior Sampling: a Feasibility Study
Clémentine Phung-Ngoc, Alexandre Bousse, Antoine De Paepe +3
This study introduces a novel framework for joint reconstruction of the activity and the attenuation (JRAA) in positron emission tomography (PET) using diffusion posterior sampling…
Solving Blind Inverse Problems: Adaptive Diffusion Models for Motion-corrected Sparse-view 4DCT
Antoine De Paepe, Alexandre Bousse, Clémentine Phung-Ngoc +1
Four-dimensional computed tomography (4DCT) is essential for medical imaging applications like radiotherapy, which demand precise respiratory motion representation. Traditional met…
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…
Direct3γ: A Pipeline for Direct Three-gamma PET Image Reconstruction
Youness Mellak, Alexandre Bousse, Thibaut Merlin +2
This paper presents a novel image reconstruction pipeline for three-gamma (3-γ) positron emission tomography (PET) aimed at improving spatial resolution and reducing noise in nucle…
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…
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction
Noel Jeffrey Pinton, Alexandre Bousse, Catherine Cheze-Le-Rest +1
This paper presents a novel approach for learned synergistic reconstruction of medical images using multibranch generative models. Leveraging variational autoencoders (VAEs), our m…
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…
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…
Spectral CT Two-step and One-step Material Decomposition using Diffusion Posterior Sampling
Corentin Vazia, Alexandre Bousse, Jacques Froment +6
This paper proposes a novel approach to spectral computed tomography (CT) material decomposition that uses the recent advances in generative diffusion models (DMs) for inverse prob…
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…
Uconnect: Synergistic Spectral CT Reconstruction with U-Nets Connecting the Energy bins
Zhihan Wang, Alexandre Bousse, Franck Vermet +5
Spectral computed tomography (CT) offers the possibility to reconstruct attenuation images at different energy levels, which can be then used for material decomposition. However, t…