Physics in medicine and biology最新文献

Objective.Accurately delineating the visual pathway (VP) is crucial for understanding the human visual system and diagnosing related disorders. Exploring multi-parametric MR imaging data has been identified as an important way to delineate VP. However, due to the complex cross-sequence relationships, existing methods cannot effectively model the complementary information from different MRI sequences. In addition, these existing methods heavily rely on large training data with labels, which is labor-intensive and time-consuming to obtain.Approach.We propose a novel semi-supervised multi-parametric feature decomposition framework for VP delineation. Specifically, a correlation-constrained feature decomposition is designed to handle the complex cross-sequence relationships by capturing the unique characteristics of each MRI sequence and easing the multi-parametric information fusion process. Furthermore, a consistency-based sample enhancement module is developed to address the limited labeled data issue, by generating and promoting meaningful edge information from unlabeled data.Main results.We validate our framework using two public datasets and one in-house multi-shell diffusion MRI dataset. Experimental results demonstrate the superiority of our approach in terms of delineation performance when compared to six state-of-the-art approaches.Significance.Our proposed framework effectively mitigates the challenges of modeling complex cross-sequence relationships and limited labeled data, offering a robust solution for accurate VP delineation. This approach not only enhances the understanding of the human visual system but also holds potential for improving the diagnosis of VP-related disorders.

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 (Sarrutet al2025 arXiv:2507.09842). 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 integrated C++ and Python codes. This framework allows for the precise, time-aware generation of primary particles, a critical requirement for accurately modeling positron emission tomography, radionuclide therapies, or 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 methodological 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. This paper is not intended to be a developer guide. Its purpose is to share with the research community in-depth explanations of our development effort that made the new GATE 10 possible.

Objective.Treatment planning in proton therapy requires an accurate estimation of stopping power ratio relative to water (SPR) maps. Presently, about 4% of patients submitted to radiotherapy treatments have metallic implants, which are responsible for an incorrect determination of SPRs in prostheses and surrounding regions. This study presents the first application of the proton computed tomography (pCT) technique, able to directly measure SPRs maps, on complex metallic implants.Approach.A homogeneous Ti6Al4V alloy sample, a set of metallic devices used for prostheses and an intra-vertebral titanium alloy implant have been inspected, by means of a prototype pCT system with a 5 × 20 cm²field-of-view (FoV) developed by INFN Firenze (Italy), under a proton beam at Trento Proton Therapy Centre (APSS, Trento, Italy). For comparison, a Multi Layer Ionization Chamber (MLIC) has been used to independently determine the SPR mean value of the Ti6Al4V alloy sample.Main Results.Tomographic reconstructions of all devices and materials have been performed and SPR maps have been obtained. All pCT images and profiles, even of metallic components, are characterized by negligible artifacts. The fine spatial resolution of our pCT system, about 0.7 lp mm^-1, allowed us to resolve details within a millimeter scale. The internal grid of the meshed cage as well as details of the screws' head of the intra-vertebral titanium alloy implant are clearly visible. The SPR of the Ti6Al4V alloy sample measured with pCT, 3.14 ± 0.02, compares well with what was measured by MLIC: 3.17 ± 0.02.Significance.This study presents the first application of the pCT methodology to directly measure SPR maps of complex metal prostheses. The ability of pCT to correctly determine mean SPR values has been experimentally demonstrated. Furthermore, this technique was shown to reconstruct complex metal structures at the millimeter scale with negligible artifacts.

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 focus on 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.

Objective: This study aims to investigate the impact of the beam temporal profile on the radical dynamics and inter-track interactions of FLASH radiotherapy, supporting parameter optimization for the equipment development, radio-biological experiments and clinical implementation. Approach: Monte-Carlo simulations based on the independent reaction time (IRT) method were performed to analyze the dynamics after irradiation, including single-pulse or multi-pulses irradiation, pulse repetition rate, pulse width and dose. The physicochemical experiments were performed to measure the hydrated electron lifetimes for validation. The generation and recombination of hydroxyl radicals and hydrated electrons were recorded under 6 MeV electron irradiation with varying beam temporal profiles. The radial distributions of the radicals were statistically analyzed, and the inter-track interactions were assessed through a mathematical model. Main results: The spatial distribution and temporal evolution of radicals were significantly affected by the beam temporal profiles. Compared with multi-pulses irradiation, single-pulse irradiation mode with a pulse width less than 1/10 of the radical lifetime, a repetition interval longer than the radical lifetime, and a dose exceeding 1 Gy/pulse can lead to rapid consumption of radicals within the first 30% of their lifetime, hence reduced the residual radical content. Instantaneous high dose rates induced overlapping of radical tracks. When the single-pulse dose exceeded 1 Gy, the overlap probability approached 100%, aligning with the dose threshold for the instantaneous radical combination. Significance: Under a low-duty cycle and high instantaneous dose-rate temporal profile, the radicals were rapidly consumed through track overlap, affecting FLASH effect. The optimized temporal profile can be used to guide the development of equipment and parameter settings in clinical practice to maximize the FLASH effect, such as the laser accelerators and superconducting photocathode guns.

Magnetoacoustic tomography with magnetic induction (MAT-MI) offers non-invasive imaging of tissue conductivity distribution with ultrasound-comparable resolution based on multi-physical field coupling effects. However, practical clinical translation of MAT-MI is hampered by reconstruction challenges, particularly the trade-off between image fidelity and speed under realistic noise levels and data incompleteness. Conventional analytical algorithms are fast but prone to artifacts and inaccuracies due to simplified physics assumptions, while model-based iterative reconstruction provides superior fidelity but often suffers from high computational cost and challenges in effectively integrating complex priors. This work introduces SCG-MAR (superiorized conjugate gradient magnetoacoustic reconstruction), a novel algorithm for high-fidelity, real-time MAT-MI reconstruction. SCG-MAR synergistically integrates a precise physics-based magnetoacoustic forward model, accounting for crucial experimental factors, with the computationally efficient perturbed SCG method. Implemented via parallel graphics processing unit acceleration, SCG-MAR achieves real-time inversion speeds in MAT-MI (∼16 fps for multi-frame parallel reconstruction); note that this real-time capability refers specifically to the iterative image reconstruction process. Comprehensive benchmarking of SCG-MAR against conventional methods (filtered back-projection; delay-and-sum; algebraic reconstruction technique) and model-based reconstruction methods (CG-based MAR, CG-MAR; unconstrained superiorized variant, uSCG-MAR) across simulations, phantoms, andin vivomouse studies demonstrates significant improvements in reconstruction accuracy, background contrast, robustness to noise, and artifact suppression. To our knowledge, this is the first demonstration of high-quality real-timein vivoMAT-MI imaging achieved using a model-based inversion algorithm, significantly advancing the potential for MAT-MI in biomedical research and clinical applications.

Objective.It is generally assumed that the FLASH effect is triggered at dose rates (DRs) of at least 40 Gy s^-1, while recent studies indicate that this threshold is not binary but follows a sigmoid across samples. Some patients may thus already experience the FLASH effect at lower DRs, while the current FLASH models do not account for this. We propose a method that aims to maximally exploit the FLASH effect over a wider dose-rate range through dose-rate-dependent FLASH delivery pattern optimization (DPO) functions while maintaining the FLASH effect at the currently accepted binary dose-rate threshold of 40 Gy s^-1.Approach.We optimized and evaluated FLASH-weighted dose (FWD) distributions for 1397 FLASH optimization functions. All FLASH optimization functions were used to optimize the FWD distribution using DPO. The generated FWD distributions were evaluated in case FLASH is triggered at DRs ranging from 10 to 60 Gy s^-1and compared to the FWD distribution that was optimized under the assumption that FLASH is only and maximally triggered at 40 Gy s^-1.Main results.(i) Substantial improvements in FWD distributions were obtained using FLASH optimization functions. (ii) The optimal FLASH optimization function differs both per patient and per beam. (iii) FLASH optimization function class solutions can also lead to an overall improvement of FWD distributions.Significance.We demonstrated that substantial improvements in FWD distributions can be achieved by using FLASH optimization functions that exploit the FLASH effect over a wider dose-rate range.

CT angiography (CTA) is essential for early diagnosis, preoperative assessment, and postoperative monitoring of vascular conditions. Traditional CTA depends on substantial amounts of contrast agents to obtain adequate vascular differentiation, potentially leading to contrast-induced nephropathy and adverse reactions. While low-dose contrast techniques reduce patient risk, they often degrade image quality, specifically impairing the detection of intricate, small vessels, thus restricting their clinical usefulness. To address this challenge, we propose a novel low-dose agent CTA (LDCTA) image enhancement network that integrates a structure-aware perceptual loss module with an adaptive deformable convolution module to improve vascular detail reconstruction under low-dose agent conditions. The perceptual loss utilizes a pre-trained vascular segmentation model to focus on anatomical areas, improving semantic coherence and structural accuracy. In addition, the deformable convolution module dynamically adjusts convolution kernel shapes based on local structures, improving feature extraction for irregular and small-scale vessels. The proposed method has been thoroughly validated on head-neck and thoracic datasets, with experimental results demonstrating superior image enhancement quality and vascular structure preservation compared to existing approaches.

Computed tomography (CT) technology reduces radiation exposure to the human body through sparse sampling, but fewer sampling angles pose challenges for image reconstruction. When the projection angles are significantly reduced, the quality of image reconstruction deteriorates. To improve the quality of image reconstruction under sparse angles, an ultra-sparse view CT reconstruction method utilizing multi-scale diffusion models is proposed. This method aims to focus on the global distribution of information while facilitating the reconstruction of local image features in sparse views. Specifically, the proposed model ingeniously combines information from both comprehensive sampling and selective sparse sampling techniques. By precisely adjusting the diffusion model, diverse noise distributions are extracted, enhancing the understanding of the overall image structure and assisting the fully sampled model in recovering image information more effectively. By leveraging the inherent correlations within the projection data, an equidistant mask is designed according to the principles of CT imaging, allowing the model to focus attention more efficiently. Experimental results demonstrate that the multi-scale model approach significantly improves image reconstruction quality under ultra-sparse views and exhibits good generalization across multiple datasets.

Objective.Digital mammography (DM) and dual-energy contrast-enhanced mammography (CEM) use the same anti-scatter grid, despite their differences in x-ray spectra. This study investigated grid performance under clinically relevant conditions in DM and CEM across representative ranges of patient characteristics and assessed the effect of increased grid ratior.Approach.Monte Carlo simulations of low-energy (LE) and high-energy (HE) images of a clinical DM/CEM system were performed using realistically shaped compressed breast phantoms with varying thickness, size, and composition. Linear grids withr= 5, 10, 15, and 20 were simulated. Grid performance was measured via the signal-difference-to-noise-ratio (SDNR) improvement factor (SIF), and differences in spatial scatter distributions were assessed via scatter-to-primary ratio (SPR) profiles.Main results.Breast composition had no considerable impact on grid performance for DM or CEM. SIF increased with breast size by an average of 1.3% (LE) and 6.9% (HE), and with breast thickness by 21%-30% (LE) and 19%-27% (HE). The standard grid (r=5) did not reduce SDNR in LE or HE images for the examined thickness range (30-90 mm). Higher grid ratios improved SPR homogeneity between LE and HE images in the inner projected breast area. A grid withr=10yielded up to 4% higher SIF at the centre of mass thanr=5in HE images, while causing a maximum SDNR loss of 3.5% for 30 mm thick breasts in LE images.Significance.This study provides clinically relevant measures of grid performance in DM and CEM, closing a gap on missing insights of breast size effects. Contrary to previous findings, SDNR is not necessarily degraded for thin breasts under standard imaging conditions. Increasing the grid ratio tor=10improves HE image quality and might reduce rim artifacts in CEM due to increased SPR homogeneity, with minimal SDNR loss in DM.