Physics in medicine and biology最新文献

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.

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.

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.

Objective.This study aims to develop a multi-modality-guided dose prediction (MMDP)-based auto-planning algorithm for functional lung avoidance radiotherapy (FLART) guided by voxel-wise lung function images.Approach.The proposed auto-planning algorithm consists of a novel MMDP model and a function-guided dose mimicking algorithm. The MMDP model features extracting complementary features from multi-modality images for predicting dose distributions close to FLART plans. An instance-weighting anatomy-to-function training strategy is tailored to enhance prediction accuracy. A function-guided voxel-wise dose mimicking algorithm is developed to convert predicted dose into FLART (MMDP-FLART) plans. We retrospectively collected data from 163 lung cancer patients across three institutions, comprising 114/28 cases for training/validation and 21 cases with SPECT ventilation (V) images for testing. Furthermore, we prospectively collected 33 cases with SPECT perfusion (Q) images for evaluation. MMDP-FLART plans were compared against conventional radiotherapy (ConvRT) and FLART plans manually created by senior clinicians.Main results.MMDP achieved accurate dose predictions, with median prediction errors for all assessed dose-volume histogram (DVH) metrics within ±1 Gy/±1%. The MMDP model reduced prediction absolute errors for functionally weighted mean lung dose (fMLD) by 12.77% compared to an anatomy-guided dose prediction model and the instance-weighting anatomy-to-function training strategy reduced prediction absolute errors for fMLD by 22.64%. Compared to manual ConvRT plans, MMDP-FLART plans effectively reduced fMLD by 0.80 Gy (11.9%,p< 0.01) and 0.46 Gy (6.0%,p< 0.01) on SPECT V and Q datasets respectively. Compared to manual FLART plans, MMDP-FLART plans exhibited lower and comparable fMLD on SPECT V and Q datasets respectively with lower dose to heart and esophagus.Significance. The MMDP model with instance-weighting anatomy-to-function training can achieve accurate dose prediction for FLART. The MMDP-based auto-planning algorithm can produce FLART plans leveraging voxel-wise lung function information from V/Q images. It shows promise in promoting FLART planning efficiency, consistency, and quality.

Objective.We evaluated different breakpoint (BP) strategies and the impact of scan path optimization on dose accuracy, beam interruptions, and delivery efficiency in proton dose-driven continuous scanning (DDCS). Our goal is to provide insights for the effective clinical implementation of DDCS.Approach.Proton pencil beam scanning plans were retrospectively simulated for DDCS with beam current optimized for the shortest beam delivery time (BDT). Five BP strategies were evaluated: three spot distance (SD)-based (SD1, SD1.5, SD2) using SD thresholds, and two SR-based (SR1, SR0) using the ratio of MU delivered at the planned spot to that delivered in transit. Simulations included three scan paths (default, length-optimized, time-optimized). Comparative analysis included BP fraction (beam interruptions), dose accuracy, and BDT.Main results.SD-based approaches achieved excellent dosimetric accuracy, with 2%/2 mm Gamma pass rates >98% and CTV DVH RMSE <1% across all BP thresholds and scan paths. SD2 with length-optimized path minimized BPs (median 1.1%, range 0%-6.7%) while maintaining high dose accuracy, making it the preferred choice when minimizing dose deviations and BPs is the priority. SR-based approaches had shorter BDTs, maintaining >95% Gamma pass rates and <2% CTV DVH RMSE with optimized scan path. SR0 with time-optimized path is suitable when BDT is critical. Scan path optimization reduced BPs for SD-based methods and improved dose accuracy for SR-based methods. If only the default serpentine path is available, caution is required for lung treatments to ensure clinically acceptable dose with SR-based methods.Significance.Dose accuracy can be maintained without reducing the beam current optimized for BDT in DDCS. SD- and SR-based methods show complementary strengths: SD2 with a length-optimized path minimizes dose deviations and BPs, whereas SR0 with time-optimized path offers shorter BDT and maintaining acceptable dose deviations. These findings provide guidance for implementing proton DDCS to balance dose accuracy, beam interruptions, and delivery efficiency according to clinical needs.

Objective.The aim of this study is to investigate the relation between two figures of merit for the low contrast resolution of computed tomography (CT) imaging systems, with the perspective of its use for acceptance and constancy testing.Approach.We use simulated data as well as 29 CT image datasets of the MITA body phantom CCT189 obtained using a previously published protocol, including CT devices from five different manufacturers and various image reconstruction methods. From these data, the detectability indexd' is determined using the channelised Hotelling observer (CHO), which requires hundreds of images per setting. We compared' to the minimum detectable contrast (MDC), a statistically defined measure of low contrast detectability, that can be determined using only few images per setting.Main results.For the CHO with circular symmetric DDOG (dense difference of Gaussians) channels,d' is proportional to the inverse of the product of MDC and the diameter of the object to be detected. The proportionality factor depends strongly on the texture of the noise.Significance.The findings provide the basis for the development of an acceptance and constancy test for CT low contrast resolution, making use ofd'CHO and MDC.