Jean-Claude Nunes

University of Rennes | UR1 · LTSI - Laboratoire Traitement du Signal et de l'Image/ INSERM 1099

Background and purpose Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised methods are limited to specific center’s learnings and depend on registration precision. The aim of this st...

Introduction For radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown encouraging results if the MRI images used for training the deep learning network and the MRI images...

Background and Purpose: Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a chal...

Radiation therapy is moving from CT based to MRI guided planning, particularly for soft tissue anatomy. An important requirement of this new workflow is the generation of synthetic-CT (sCT) from MRI to enable treatment dose calculations. Automatic methods to determine the acceptable range of CT Hounsfield Unit (HU) uncertainties to avoid dose distr...

Synthetic-Computed Tomography (sCT) generation is a critical component of Magnetic Resonance Imaging (MRI)- only radiation therapy workflows. The sCT computed from MRI is generally assessed by measuring Hounsfield Units (HU) discrepancies with a reference CT. The aim of this work was to propose a process for the blind assessment of local errors in...

Purpose: The first aim was to generate and compare synthetic-CT (sCT) images using a conditional generative adversarial network (cGAN) method (Pix2Pix) for MRI-only prostate radiotherapy planning by testing several generators, loss functions, and hyper-parameters. The second aim was to compare the optimized Pix2Pix model with five other architectu...

The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatie...

Electron density information, usually obtained from computed tomography (CT) Hounsfield units, is critical for modern radiation therapy treatment planning. However, CT scans have limited ability to differentiate different types of soft tissue, which has motivated the use of magnetic resonance imaging (MRI) to improve treatment accuracy in radiation...

Introduction et but de l’étude La génération de pseudo-scanographie ou tomodensitométrie synthétique à partir d’IRM est nécessaire pour le calcul de la dose dans le cadre d’une radiothérapie basée sur l’IRM. De nombreuses méthodes d’apprentissage profond ont été développées à cet effet. L’objectif de cette étude est de générer des tomodensitométrie...

Purpose In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the electron density of tissue necessary for dose calculation. Several methods of synthetic-CT (sCT) generation from MRI data have been developed for radiotherapy dose c...

Several approaches have been proposed to generate pseudo computed tomography (pCT) from MR images for radiotherapy dose calculation. Quantification of errors in pCT has been reported using global scores disregarding spatial heterogeneity. The aim of this work was to propose a population voxel-based workflow allowing the local assessment of errors i...

Purpose Anatomical variations occur during head and neck (H&N) radiotherapy treatment. kV cone‐beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo‐CT (pCT) from CBCT to perform dose calculation. T...

Purpose In context of head-and-neck radiotherapy, this study aims to compare MR image quality according to diagnostic (DIAG) and radiotherapy (RT) setups; and to optimise an MRI-protocol (including 3D T1 and T2-weighted sequences) for dose-planning (based on pseudo-CT generation). Materials and methods To compare DIAG and RT setups, signal-to-nois...

Introduction et but de l’étude Différentes méthodes d’apprentissage peuvent être utilisées pour générer des pseudo-scanographies à partir d’IRM ou de tomographies coniques (cone beam computed tomography, CBCT), permettant un calcul de distribution de dose. L’objectif est d’évaluer des méthodes d’apprentissage pour générer des pseudo-scanographies à...

Purpose: Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for MRI-based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network (GAN)) using various loss functions (L2, single-scale perceptual loss (PL), multiscale PL, weighted multiscale PL), and a patch-based metho...

Introduction Several methods have been developed to generate pseudo-CT (pCT) from MRI for dose calculation. The objective of this study was to compare an original non-local mean patch-basatlas-based method (ABM) and a bulk density method (BDM) (Fig.1). Methods Thirty-nine patients received VMAT for prostate cancer. T2-weighted MR images were acqui...

Objectif de l’étude Réaliser un calcul de dose à partir d’une IRM implique de générer une pseudo-scanographie. L’objectif de cette étude était de comparer trois méthodes pour générer une pseudo-scanographie : une méthode basée sur l’utilisation de patches (patch-based method, PBM), une méthode atlas (atlas-based method, ABM) et une méthode d’assign...

Purpose: Methods have been recently developed to generate pseudo-CT (pCT) for dose calculation in MRI-only radiotherapy. This study aimed to propose an original non-local mean patch-based method (PBM), and to compare this PBM to an atlas-based method (ABM) and to a bulk density method (BDM) for prostate MRI-only radiotherapy. Materials and method...

Artis-Zeego is an advanced interventional imaging system that can provide the three-dimensional (3-D) reconstruction of the coronary artery in real time. However, the mechanical accuracy will degenerate after long-time use. The inaccurate geometry will affect the spatial resolution of the 3-D reconstruction. We propose a calibration algorithm to ta...

Background 3D reconstruction of the coronary arteries can provide more information in the interventional surgery. Motion compensation is one kind of the 3D reconstruction method. Methods We propose a novel and complete 2D motion compensated reconstruction method. The main components include initial reconstruction, forward projection, registration...

Dose calculation from MRI is a topical issue. New treatment systems combining a linear accelerator with a MRI have been recently being developed. MRI has good soft tissue contrast without ionizing radiation exposure. However, unlike CT, MRI does not provide electron density information necessary for dose calculation. We propose in this paper a mach...

Over the past decades, a multitude of different brain source imaging algorithms have been developed to identify the neural generators underlying the surface electroencephalography measurements. While most of these techniques focus on determining the source positions, only a small number of recently developed algorithms provides an indication of the...

Selective internal radiation therapy (SIRT) using Yttrium-90 loaded glass microspheres injected in the hepatic artery is an emerging, minimally invasive therapy of liver cancer. A personalized intervention can lead to high concentration dose in the tumor, while sparing the surrounding parenchyma. We propose a computational model for patient-specifi...

MRI-based radiotherapy planning is a topical subject due to the introduction of a new generation of treatment machines combining a linear accelerator and a MRI. One of the issues for introducing MRI in this task is the lack of information to provide tissue density information required for dose calculation. To cope with this issue, two strategies ma...

Over the past decades, a multitude of different brain source imaging algorithms have been developed to identify the neural generators underlying the surface electroencephalography measurements. While most of these techniques focus on determining the source positions, only a small number of recently developed algorithms provides an indication of the...

Coronary tree matching is applied to plan percutaneous vascular procedures. This work, which allows following each segment of non-isomorphic coronary trees over time, precedes the determination of the best 2D angiography view from C-arm acquisition system for angioplasty procedure. To match two 3D coronary trees which represent two successive cardi...

The paper describes an inexact tree-matching algorithm to register non-isomorphic 3D coronary artery trees over time. This work is carried out in the frame of the determination of the optimal viewing angles on the C-arm acquisition system for coronary percutaneous procedure. The matching method is based on association graph and maximum clique. Diff...

Tree matching algorithms have various applications in medical imaging for anatomical vessel system such as navigation in the tree structures, planning and treatment procedure or follow-up cardiac therapy. We consider that a dynamic sequence of 3D coronary trees is available from pre-segmented CT data or from reconstruction of the projections acquir...

We propose a new similarity measure for iconic medical image registration, an Edgeworth-based third order approximation of Mutual Information (MI) and named 3-EMI. Contrary to classical Edgeworth-based MI approximations, such as those proposed for independent component analysis, the 3-EMI measure is able to deal with potentially correlated variable...

Treatment of coronary lesions by percutaneous transluminal angioplasty is performed from 2D observations acquired according to an optimal view, i.e. a point of view showing the segment including the stenosis in its most extended and unobstructed dimension over a given perimeter around the lesion. In clinical routine, the research of this view gener...

We present herein a level set approach to the X-ray tomography problem with sparse projection data and study the impact of the projection operator on the binary reconstruction accuracy and computation time. The comparison is carried out on three projectors: the Separable Footprint (Trapeze-Trapeze, SF-TT) [3], a classical Raydriven (RD) and a Simpl...

This paper deals with the D reconstruction of sparse data in X-ray rotational imaging. Due to the cardiac motion, the number of available projections for this reconstruction is equal to four, which leads to a strongly under-sampled reconstruction problem. We address thus this illness problem through a regularized iterative method. The whole algorit...

Empirical Mode Decomposition (EMD) is an emerging topic in signal processing research, applied in various practical fields due in particular to its data-driven filter bank properties. In this paper, a novel EMD approach called X-EMD (eXtended-EMD) is proposed, which allows for a straightforward decomposition of mono- and multivariate signals withou...

Nous présentons une méthode de reconstruction 3D des artères coronaires à partir de 4 projections acquises en imagerie rotationnelle R-X. L'approche retenue considère un problème d'optimisation d'une fonction "objectif", en se basant sur un estimateur Bayésien (MAP : Maximum à postériori) et un modèle de distribution des données de projection de ty...

A novel Empirical Mode Decomposition (EMD) algorithm, called 2T-EMD, for both mono- and multivariate signals is proposed in this paper. It differs from the other approaches by its computational lightness and its algorithmic simplicity. The method is essentially based on a redefinition of the signal mean envelope, computed thanks to new characterist...

In this paper, we present a Bayesian maximum a posteriori method for multi-slice helical CT reconstruction based on an L0-norm prior. It makes use of a very low number of projections. A set of surrogate potential functions is used to successively approximate the L0-norm function while generating the prior and to accelerate the convergence speed. Si...

Mutual Information (MI) has been extensively used as a similarity measure in image registration and motion esti-mation, and it is particularly robust for 3D multimodal medical image registration. However, MI estimators are known i) to have a high variance and ii) to be computationally costly. In order to overcome these drawbacks, we propose a new s...

Mutual Information (MI) has been extensively used as a similarity measure in image registration and motion estimation, and it is particularly robust for 3D multimodal medical image registration. However, MI estimators are known i) to have a high variance and ii) to be computationally costly. In order to overcome these drawbacks, we propose a new si...

This paper presents a model-based reconstruction method of the coronary tree from a few number of projections in rotational angiography imaging. The reconstruction relies on projections acquired at a same cardiac phase and an energy function minimization that aims to lead the deformation of the 3D model to fit projection data whereas preserving coh...

A method is proposed for 3-D reconstruction of coronary from a limited number of projections in rotational angiography. A Bayesian maximum a posteriori (MAP) estimation is applied with a Poisson distributed projection to reconstruct the 3D coronary tree at a given instant of the cardiac cycle. Several regularizers are investigated L0-norm, L1 and L...

EMD is an emerging topic in signal processing research and is applied in various practical fields. Its recent extension to multivariate signals, motivated by the need to jointly analyze multi-channel signals, is an active topic of research. However, all the existing etensions specifically hold either mono-, bi- or tri-variate signals or require mul...

Mutual Information (MI) has been extensively studied as similarity measure for the registration of medical images, and it has been found to be especially robust for multimodal image registration. However, MI estimators are known i) to have a very high variance and ii) to be computationally costly. In order to overcome these drawbacks, we propose a...

Mutual Information (MI) has been extensively studied as similarity measure for the registration of medical im-ages, and it has been found to be especially robust for multimodal image registration. However, MI estimators are known i) to have a very high variance and ii) to be computationally costly. In order to overcome these draw-backs, we propose...

This book describes some of the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic...

In this paper, we propose some recent works on data analysis and synthesis based on Empirical Mode Decomposition (EMD). Firstly, a direct 2D extension of original Huang EMD algorithm with application to texture analysis, and fractional Brownian motion synthesis. Secondly, an analytical version of EMD based on PDE in 1D-space is presented. We propos...

This introductory chapter does not pretend to give a full overview of the biomedical field but some ideas about the breakthroughs that are on the way or will happen tomorrow. It will emphasize the importance to look outside its own field and understand how the new advances made elsewhere can help in solving specific problems or, and perhaps more, b...