Physics in medicine and biology最新文献

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.

The high-throughput extraction of radiomics features from medical images for predictive modelling holds great promise to improve the clinical management of patients. Previous meta-analyses into the radiomics quality score (RQS) applied in the literature have shown that after more than a decade of investigation, issues with workflow standardisation, model reproducibility, validation, and data accessibility persist and impede the clinical translation of radiomics-based models. These systematic findings have informed a timely review of the best practices and pitfalls to avoid within radiomics and predictive modelling, with a focus on realistic radiomics modelling in the context of limited sample sizes. Each section covers a radiomics topic that encompasses one or more RQS criteria and is broken into subsections as follows: 1) a discussion of the background and recommendations on the respective topic, 2) key findings from our meta-analyses and discovered pitfalls, and 3) a succinct list of actionable items that reflect best practice. New and emerging quality appraisal tools and the future direction of radiomics is also discussed.

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.

A comprehensive understanding of the energy-dependent stochastic risks associated with neutron exposure is crucial to develop robust radioprotection systems. However, the scarcity of experimental data presents significant challenges in this domain. Track-structure Monte Carlo simulations with DNA models have demonstrated their potential to further our fundamental understanding of neutron-induced stochastic risks. To date, most track-structure Monte Carlo studies on the relative biological effectiveness (RBE) of neutrons have focused on various types of DNA damage clusters defined using base pair distances. In this study, we extend these methodologies by incorporating the simulation of non-homologous end joining (NHEJ) DNA repair in order to evaluate the RBE of neutrons for misrepairs. To achieve this, we adapted our previously published Monte Carlo DNA damage simulation pipeline, which combines condensed-history and track-structure Monte Carlo methods, to support the standard DNA damage (SDD) data format. This adaptation enabled seamless integration of neutron-induced DNA damage results with the DNA Mechanistic Repair Simulator (DaMaRiS) toolkit. Additionally, we developed a clustering algorithm that reproduces pre-repair endpoints studied in prior works, as well as novel damage clusters based on Euclidean distances. The neutron RBE for misrepairs obtained in this study exhibits a qualitatively similar shape as the RBE obtained for previously reported pre-repair endpoints. However, it peaks higher, reaching a maximum RBE value of 23(1) at a neutron energy of 0.5 MeV. Furthermore, we found that misrepair outcomes were better reproduced using the pre-repair endpoint defined with the Euclidean distance between double-strand breaks rather than with previously published pre-repair endpoints based on base-pair distances. The optimal maximal Euclidean distances were 18 nm for 0.5 MeV neutrons and 60 nm for 250 keV photons. Although this may indicate that Euclidean-distance-based DSB clustering more accurately reflects the DNA damage configurations that lead to misrepairs, the fact that neutrons and photons require different distances raises doubts on whether a single, universal pre-repair endpoint can used as a stand-in for larger-scale aberrations across all radiation qualities.

Purpose.Ocular torsion is a challenge occasionally encountered in ocular proton therapy (OPT) consisting of a rotation of the eye about the visual axis. This can result in the safety margin being compromised and reduced conformity of the dose field to the target. This note investigates the effect of ocular torsion on the lateral margin to verify and explore quantitative adaptation strategies to mitigate the adverse effect on this margin.Methods.OCULARIS, an in-house OPT research planning tool, was used to simulate 14 patients undergoing OPT. The lateral margin was determined for each patient at ocular torsion angles ranging from -8° to 8° in discrete steps of 2°, with 19 collimator rotations simulated at each torsion angle.Results.Margin loss increases with greater ocular torsion, with significant inter-patient variability being influenced by the shape of the target. Aligning collimator rotation with ocular torsion (NTM) retains 61% of the margin, patient-specific adaptations achieve superior dose conformity to the target. A simple regression method, setting the collimator rotation to the ocular torsion angle minus 1° for torsions greater than 2°, offers some benefit over NTM in this cohort.Conclusions.Margin loss increases with ocular torsion, with the extent of loss being influenced by patient-specific geometry. The NTM collimator rotation strategy was found to adequately compensate for torsion-induced margin loss. Alternative collimator rotation strategies were also explored, including a framework for optimising collimator rotation in the event of ocular torsion.

Objective: Large irradiated volumes are a major contributor to severe side-effects in patients with head and neck cancer undergoing curatively intended radiotherapy. We propose a data-driven approach for defining the elective clinical target volume (CTV-E) on a patient-specific basis, with the potential to reduce irradiated volumes compared to standard guidelines.

Approach: We introduce a bilateral Bayesian Network (BN), trained on a large cohort, to estimate the patient-specific risk of undetected nodal involvement for both ipsilateral and contralateral lymph node levels (LNLs) I, II, III, and IV, using clinical features, such as patterns of nodal involvement, T-stage, tumour location. By applying risk thresholds, we generated individualized, risk-dependent CTV-E's for representative patient scenarios and compared the resulting treatment volumes and residual risk to those recommended by standard clinical guidelines.

Main results: We computed the risks for a set of representative patient scenarios including 1) N0 (T1 and T2 tumour stage), 2) N+ in ipsilateral LNL II (T1 and T2 tumour stage), 3) N+ in ipsilateral LNL II and III (T1 and T2 tumour stage), and 4) N+ of both ipsilateral and contralateral LNL II (T3 and T4 tumour stage). Depending on the chosen risk threshold, the bilateral BN allowed for reductions in irradiated volumes relative to standard clinical protocols. For every patient scenario considered, the CTV-E's defined by the applied risk thresholds were associated with a low estimated probability of undetected nodal involvement in any excluded LNL.

Significance: We present a data-driven framework for personalized CTV-E definition, encouraging the discussion of more patient-specific elective nodal target volumes, with potential for de-escalation of irradiated elective volumes.

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.

Objective: Accurate and personalized radiation dose estimation is crucial for effective Targeted Radionuclide Therapy (TRT). Deep learning (DL) holds promise for this purpose. However, current DL-based dosimetry methods require large-scale supervised data, which is scarce in clinical practice.

Approach: To address this challenge, we propose exploring semi-supervised learning (SSL) framework that leverages readily available pretherapy PET data, where only a small subset requires dose labels, to predict radiation doses, thereby reducing the dependency on extensive labeled datasets. In this study, traditional classification-based SSL approaches were adapted and extended in regression task specifically designed for dose prediction. To facilitate comprehensive testing and validation, we developed a synthetic dataset that simulates PET images and dose calculation using Monte Carlo simulations.

Main results: In the experiment, several regression-adapted SSL methods were compared and evaluated under varying proportions of labeled data in the training set. The overall mean absolute percentage error of dose prediction remained between 9% and 11% across different organs, which achieved comparable performance than fully supervised ones.

Significance: The preliminary experimental results demonstrated that the proposed SSL methods yield promising outcomes for organ-level dose prediction, particularly in scenarios where clinical data are not available in sufficient quantities.

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: Fast and precise delivery of ion-beam therapy is essential for improving clinical throughput and intrafractional motion management, yet synchrotron-based systems require multiple energy layers for depth dose coverage, resulting in delays on the order of minutes. To eliminate energy layer switching times, a fast Monte Carlo (MC)-based workflow for patient-specific 3D range modulators (3D-RMs) was developed to enable monoenergetic, conformal carbon irradiation at clinically viable speeds. To mirror realistic clinical use, the dosimetric impact of setup and RM geometry deviations from simulated models were assessed. Approach: The workflow begins with spot extraction from clinical intensity modulated particle therapy (IMPT) plans, followed by RM geometry optimization, fast MC dose verification using MonteRay, and 3D printing final geometries. Experimental validations were performed for spread-out Bragg peaks (SOBPs) in water, and two targets in an anthropomorphic head phantom: 1) in a homogeneous brain region and 2) across a heterogeneous bone-soft tissue interface. Robustness against realistic setup and printing errors were assessed in the heterogeneous case. Main results: Each RM geometry was optimized in under one minute and the RM-based plans achieved dose distributions comparable to IMPT with similar target coverage and homogeneity. Simulated and measured depth dose profiles for SOBP plans agreed within 1.2% local deviation in the target. In the head phantom, measured 2D dose maps achieved local gamma pass rates >99% (2%/2 mm, 10% threshold) in both uniform and anatomically complex settings. Plans were robust to setup deviations up to 1 mm, and manufacturing deviations up to 100 µm. Significance: This rapid, clinically feasible workflow enables conformal, monoenergetic carbon ion delivery with dosimetric quality comparable to IMPT even in heterogenous scenarios. The substantially reduce treatment delivery time facilitates motion mitigation and higher patient throughput, and may also provide a technical basis for exploring FLASH regimes in synchrotron-based ion beam facilities.