arXiv subjects

Maxime Toussaint

Publications and source records attributed to Maxime Toussaint.

6 recordsLinked to original sources

arXiv2026

Multi-Kernel TOF-PET Image Reconstruction Using ADMM

Time-of-flight positron emission tomography (TOF-PET) detectors exhibiting multiple coincidence time resolution (CTR) components, such as those induced by the mixing of Cherenkov and scintillation photons, have attracted increasing attention. However, to fully exploit the latent potential of multi-kernel TOF-PET, new iterative image reconstruction methods are required. In this study, assuming that the events are labeled with the appropriate kernels, we propose an alternating direction method of multipliers (ADMM) for multi-kernel TOF-PET reconstruction, termed TOF-decomp ADMM. As the convergence speed of the TOF-PET log-likelihood depends on the CTR, the proposed method splits the fast- and slow-CTR log-likelihood terms and optimizes them separately under a constraint. This strategy explicitly balances the contributions of fast- and slow-CTR components and enables early stopping at iterations that yield improved contrast-noise trade-offs compared with conventional methods. We validated the proposed method using brain and image quality phantom simulations, demonstrating improved contrast-noise characteristics from a more stabilized convergence. By addressing the convergence imbalance inherent to multi-kernel TOF-PET, this work establishes a framework for exploiting the timing information available in emerging detector technologies.

physics.med-ph↗

arXiv2025

CT-Less Attenuation Correction Using Multiview Ensemble Conditional Diffusion Model on High-Resolution Uncorrected PET Images

Accurate quantification in positron emission tomography (PET) is essential for accurate diagnostic results and effective treatment tracking. A major issue encountered in PET imaging is attenuation. Attenuation refers to the diminution of photon detected as they traverse biological tissues before reaching detectors. When such corrections are absent or inadequate, this signal degradation can introduce inaccurate quantification, making it difficult to differentiate benign from malignant conditions, and can potentially lead to misdiagnosis. Typically, this correction is done with co-computed Computed Tomography (CT) imaging to obtain structural data for calculating photon attenuation across the body. However, this methodology subjects patients to extra ionizing radiation exposure, suffers from potential spatial misregistration between PET/CT imaging sequences, and demands costly equipment infrastructure. Emerging advances in neural network architectures present an alternative approach via synthetic CT image synthesis. Our investigation reveals that Conditional Denoising Diffusion Probabilistic Models (DDPMs) can generate high quality CT images from non attenuation corrected PET images in order to correct attenuation. By utilizing all three orthogonal views from non-attenuation-corrected PET images, the DDPM approach combined with ensemble voting generates higher quality pseudo-CT images with reduced artifacts and improved slice-to-slice consistency. Results from a study of 159 head scans acquired with the Siemens Biograph Vision PET/CT scanner demonstrate both qualitative and quantitative improvements in pseudo-CT generation. The method achieved a mean absolute error of 32 $\pm$ 10.4 HU on the CT images and an average error of (1.48 $\pm$ 0.68)\% across all regions of interest when comparing PET images reconstructed using the attenuation map of the generated pseudo-CT versus the true CT.

q-bio.QM↗

arXiv2025

GATE 10 Monte Carlo particle transport simulation -- Part II: architecture and innovations

Over the past years, we have developed GATE version 10, a major re-implementation of the long-standing Geant4-based Monte Carlo application for particle and radiation transport simulation in medical physics. This release introduces many new features and significant improvements, most notably a Python-based user interface replacing the legacy static input files. The new functionality of GATE version 10 is described in the part 1 companion paper. The development brought significant challenges. In this paper, we present the solutions that we have developed to overcome these challenges. In particular, we present a modular design that robustly manages the core components of a simulation: particle sources, geometry, physics processes, and data acquisition. The architecture consists of parts written in C++ and Python, which needed to be coupled. We explain how this framework allows for the precise, time-aware generation of primary particles, a critical requirement for accurately modeling positron emission tomography (PET), radionuclide therapies, and prompt-gamma timing systems. We present how GATE 10 handles complex Geant4 physics settings while exposing a simple interface to the user. Furthermore, we describe the technical solutions that facilitate the seamless integration of advanced physics models and variance reduction techniques. The architecture supports sophisticated scoring of physical quantities (such as Linear Energy Transfer and Relative Biological Effectiveness) and is designed for multithreaded execution. The new user interface allows researchers to script complex simulation workflows and directly couple external tools, such as artificial intelligence models for source generation or detector response. By detailing these architectural innovations, we demonstrate how GATE 10 provides a more powerful and flexible tool for research and innovation in medical physics.

physics.med-ph↗

arXiv2025

GATE 10 Monte Carlo particle transport simulation -- Part I: development and new features

We present GATE version 10, a major evolution of the open-source Monte Carlo simulation application for medical physics, built on Geant4. This release marks a transformative evolution, featuring a modern Python-based user interface, enhanced multithreading and multiprocessing capabilities, the ability to be embedded as a library within other software, and a streamlined framework for collaborative development. In this Part 1 paper, we outline GATE's position among other Monte Carlo codes, the core principles driving this evolution, and the robust development cycle employed. We also detail the new features and improvements. Part 2 will detail the architectural innovations and technical challenges. By combining an open, collaborative framework with cutting-edge features, such a Monte Carlo platform supports a wide range of academic and industrial research, solidifying its role as a critical tool for innovation in medical physics.

physics.med-ph↗

arXiv2023

A rewriting of the relation between the acolinearity of annihilation photons and their energy in the context of positron emission tomography

Acolinearity of the annihilation photons observed in Positron Emission Tomography (PET) is described as following a Gaussian distribution. However, it is never explicitly said if it refers to the amplitude of the acolinearity angle or its 2D distribution relative to the case without acolinearity (herein defined as the acolinearity deviation). Since the former is obtained by integrating the latter, a wrong interpretation would lead to very different results. The paper of Shibuya et al. (2007), differs from the previous studies since it is based on the precise measurement of the energy of the annihilation photons. They also show that acolinearity follows a Gaussian distribution in the context of PET. However, their notation, which relies on being on the plane where the two annihilation photons travel, could mean that their observation refers to the amplitude of the acolinearity angle. If that understanding is correct, it would mean that acolinearity deviation follows a 2D Gaussian distribution divided by the norm of its argument. Thus, we revisited the proof presented in Shibuya et al. (2007) by using an explicit description of the acolinearity in the 3D unit sphere.

physics.med-ph↗

arXiv2023

Parameterizing the Angular Distribution of Emission: A Model for TOF-PET Low-Count Reconstruction

Low-count reconstruction remains a challenge for Positron Emission Tomography (PET) even with the recent progress in time-of-flight (TOF) resolution. In this context, the bias between the acquired histogram, consisting of low values or zeros, and the expected histogram, obtained from the forward projector, is propagated to the image, resulting in a biased reconstruction. This situation could be exacerbated with finer resolution of the TOF information, which further sparsifies the acquired histogram. We propose a new approach to circumvent this limitation of the classical reconstruction model. It consists of extending the description of the reconstruction scheme to also explicitly include the projection domain by Parameterizing the Angular Distribution of Emission (PADE). This parametrization has greater degrees of freedom than the log-likelihood model, which can not be harnessed in classical circumstances. We hypothesize that with ultra-fast TOF this new approach would not only be viable for low-count reconstruction, but also more adequate than the classical reconstruction model. As a proof of concept, an implementation of this approach is compared to the log-likelihood model by investigating two-dimensional simulations of a hot spots phantom. The proposed model achieves similar contrast recovery coefficients as MLEM except for the smallest structures where the low-count nature of the simulations makes it difficult to draw conclusions. However, this new model seems to converge toward less noisy solutions than MLEM. These results suggest that the PADE approach has potential for low-count reconstruction with ultra-fast TOF.

physics.med-ph↗