Publications (31)
Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospective multi-center cohort
Marie-Charlotte Desseroit, Florent Tixier, Wolfgang Weber +4
Purpose: The main purpose of this study was to assess the reliability of shape and heterogeneity features in both Positron Emission Tomography (PET) and low-dose Computed Tomograph…
A Fast and Generic Energy-Shifting Transformer for Hybrid Monte Carlo Radiotherapy Calculation
Chi-Hieu Pham, Didier Benoit, Vincent Bourbonne +3
We introduce a novel learning framework for accelerated Monte Carlo (MC) dose calculation termed Energy-Shifting. This approach leverages deep learning to synthesize highly complex…
Deep vessel segmentation with joint multi-prior encoding
Amine Sadikine, Bogdan Badic, Enzo Ferrante +4
The precise delineation of blood vessels in medical images is critical for many clinical applications, including pathology detection and surgical planning. However, fully-automated…
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…
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…
Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation
Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu +3
The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in c…
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…
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…
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…
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…
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…
Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images
Andrei Iantsen, Dimitris Visvikis, Mathieu Hatt
Development of robust and accurate fully automated methods for medical image segmentation is crucial in clinical practice and radiomics studies. In this work, we contributed an aut…
Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation
Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu +4
Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this res…
Regularized directional representations for medical image registration
Vincent Jaouen, Pierre-Henri Conze, Guillaume Dardenne +2
In image registration, many efforts have been devoted to the development of alternatives to the popular normalized mutual information criterion. Concurrently to these efforts, an i…
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…
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…
DUG-RECON: A Framework for Direct Image Reconstruction using Convolutional Generative Networks
V. S. S. Kandarpa, Alexandre Bousse, Didier Benoit +1
This paper explores convolutional generative networks as an alternative to iterative reconstruction algorithms in medical image reconstruction. The task of medical image reconstruc…
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…
Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images
Vincent Andrearczyk, Valentin Oreiller, Sarah Boughdad +8
This paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th International Conference on Medical…
Cross-modal tumor segmentation using generative blending augmentation and self training
Guillaume Sallé, Pierre-Henri Conze, Julien Bert +3
\textit{Objectives}: Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions. A related practical problem is cross-modal…
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…
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 nucl…
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Alessa Hering, Lasse Hansen, Tony C. W. Mok +50
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medi…
On-line Dose Calculation Using Deep Learning for Beams Selection in Non-Coplanar Radiotherapy
Fang Guo, Franklin Okoli, Ulrike Schick +3
Non-coplanar Intensity-Modulated Radiation Therapy (IMRT) goes a step further by orienting the gantry carrying the radiation beam and the patient couch in a non-coplanar manner to…
CT respiratory motion synthesis using joint supervised and adversarial learning
Yi-Heng Cao, Vincent Bourbonne, François Lucia +4
Objective: Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is comm…
Continuous 3-D Latent Diffusion for Medical Generation and Reconstruction
Youness Mellak, Antoine De Paepe, Dimitris Visvikis +1
High-resolution three-dimensional (3-D) medical diffusion models remain constrained by the cost of processing full volumes, even when denoising is performed in a compact latent spa…
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…
Multilevel Stochastic Plug-and-Play for Sparse-View CT Reconstruction
Antoine De Paepe, Alexandre Bousse, Dimitris Visvikis
Sparse-view computed tomography (SVCT) reduces radiation exposure and acquisition time, but the limited number of projection views makes the reconstruction problem severely ill-pos…
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…
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…
Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction
Alessandro Perelli, Suxer Alfonso Garcia, Alexandre Bousse +3
Objective. Dual-energy computed tomography (DECT) has the potential to improve contrast, reduce artifacts and the ability to perform material decomposition in advanced imaging appl…