Physics in medicine and biology最新文献

Objective.Cerebrovascular diseases are a major global health challenge due to their high morbidity and mortality rates. Accurate segmentation of cerebrovascular structures in TOF-MRA is crucial for accurate diagnosis and treatment planning. However, it remains difficult due to the variability in vessel morphology and the scarcity of annotations.Approach.In this paper, we propose BDD-CL, a boundary-aware and discrepancy-guided dynamic pseudo-labeling consistency learning framework for semi-supervised 3D TOF-MRA cerebrovascular segmentation. The framework is equipped with three carefully designed modules: (1) a boundary enhancement (BE) module that introduces shape constraints to improve vessel boundary delineation; (2) a shape-aware discrepancy (SAD) module that detects and refines prediction inconsistencies between networks, boosting robustness in regions with complex vessel morphology; and (3) a dynamic pseudo-label selection mechanism that adaptively delegates pseudo-label generation to the better-performing network, mitigating error propagation and improving label efficiency.Main results.Extensive experiments on COSTA and IXI datasets demonstrate that BDD-CL surpasses seven state-of-the-art semi-supervised methods in both quantitative and qualitative evaluations.Significance.These results highlight the framework's potential for label-efficient and reliable cerebrovascular segmentation in clinical practice. The code and model will be made publicly available athttps://github.com/nazikelsayed/Boundary-aware-and-discrepancy-guided-dynamic-pseudo-labeling-with-consistency-learning.

Objective: Surface electromyographic (sEMG) signals of the diaphragm provide a valuable physiological signal for real-time respiratory monitoring, particularly in clinical applications such as radiotherapy tracking and intensive care, where accurate estimation of respiratory motion is essential. However, these signals are often contaminated by electrocardiographic (ECG) interference. Traditional signal processing methods introduce certain delays while suppressing ECG artifacts and rely on linear assumptions for quantifying respiratory motion, limiting their real-time adaptability and accuracy in clinical applications. This study aims to develop a robust solution for real-time respiratory motion quantification form sEMG signals.

Approach: A cascaded deep learning framework was proposed which consisting of 1) a CNN-LSTM hybrid model that isolates respiratory sEMG components and 2) a multi-scale CNN with nonlinear feature abstraction for quantifying respiratory motion. sEMG and respiratory data from 45 subjects was acquired, with 20 subjects for training and 25 for validation. Cross-correlation analysis was performed to assess correlation coefficient between sEMG and respiratory signal.

Main results: The proposed method achieved superior correlation with abdominal pressure-derived respiration (Pearson's r = 0.949 ± 0.030) compared to gating (0.910 ± 0.046) and template subtraction (0.859 ± 0.081) using the same filtering post-processing technology. Notably, the proposed method demonstrated significantly higher correlation with reference signals without requiring any post-processing, highlighting its real-time processing capability in artifact suppression.

Significance: This study demonstrates that the proposed deep learning framework provides an efficient solution for high-fidelity artifact suppression and realtime respiratory monitoring in clinical settings.

Objective.Artificial intelligence (AI) can enable automation, improve treatment accuracy, allow for a more efficient workflow, and improve the cost-effectiveness of radiotherapy (RT). To implement AI in RT, clinicians have expressed a desire to understand the AI outputs. Explainable AI (XAI) methods have been put forward as a solution, but the multidisciplinary nature of RT complicates the application of trustworthy and understandable XAI methods. The objective of this review is to analyze XAI in the RT landscape and understand how XAI can best support the diverse user groups in RT by exploring challenges and opportunities with a critical lens.Approach. We performed a review of XAI in RT, evaluating how explanations are built, validated, and embedded across the RT workflow, with attention to XAI purposes, evaluation and validation, interpretability trade-offs, and RT's multidisciplinary context.Main results. XAI in RT serves five purposes: (1) knowledge discovery, (2) model verification, (3) model improvement, (4) clinical verification, and (5) clinical justification/actionability. Many studies favor interpretability but neglect fidelity and seldom include user-specific evaluation. Key challenges include stakeholder diversity, evaluation of XAI, cognitive bias, and causality; we also outline opportunities.Significance. By linking XAI purposes to RT tasks and highlighting challenges and opportunities, we provide actionable recommendations and a user-centric framework to guide the development, validation, and deployment of XAI in RT.

Objective.Ultrafast nonlinear ultrasound imaging of gas vesicles (GVs) promises high-sensitivity biomolecular visualization for applications such as targeted molecular imaging and real-time tracking of gene expression. However, separating GV-specific signals from tissue remains challenging due to tissue clutter and limitations of current methods, which require complex transmit schemes and suffer from incomplete tissue suppression. This study aims to develop and validate harmonic amplitude-modulated singular value decomposition (HAM-SVD), a novel technique that represents a shift from current GV imaging methods by exploiting the unique nonlinear pressure-dependence of the GV harmonic signature.Approach.HAM-SVD employs single-cycle plane waves transmitted at 9.6 MHz across five tilted angles at a pulse repetition frequency of 2500 Hz, under four duty cycles (DCs) with alternating polarity. Beamformed data are reshaped into a space-pressure Casorati matrix and decomposed via SVD. Tissue background is suppressed by discarding the first (weakly nonlinear tissue) and lowest (noise) singular modes, yielding images comprised solely of pressure-dependent second-harmonic GV signals. The method was validated through numerical simulations,in vitrophantom experiments, andin vivorat lower limb imaging.Main results.HAM-SVD achieved a signal-to-background ratio of 19.16 ± 1.63 dBin vivo, significantly outperforming pulse inversion (14.19 ± 1.41 dB) and amplitude modulation (AM-SVD) (15.79 ± 1.38 dB). Simulation and phantom studies demonstrated superior spatial coherence in singular vector decomposition and reduced nonlinear artifacts compared to AM-SVD. HAM-SVD enables wide-field, ultrafast imaging without complex transmit sequences while maintaining robust tissue clutter suppression across varying pressure levels.Significance.By combining harmonic imaging with AM-SVD's adaptive clutter filtering, HAM-SVD overcomes limitations of conventional nonlinear techniques, including depth restrictions in xAM and incomplete tissue cancellation in pulse inversion. This approach enhances molecular imaging specificity for GVs and holds translational potential for ultrasound localization microscopy of slow-flowing contrast agents and preclinical disease-targeted molecular imaging.

Objective.To evaluate the trade-offs among model resolution, anatomical fidelity, computational cost, and localization accuracy in electroencephalography (EEG) source imaging using personalized head models. Special emphasis was placed on segmentation-free modeling and sparse inverse analysis for rapid brain mapping.Approach. Forward problems were solved using a scalar-potential finite-difference method combined with orthogonal matching pursuit for inverse source localization. Tissue conductivity was directly estimated from magnetic resonance images using machine learning models, including CondNet and CondNet-TART, to generate personalized, continuous conductivity distributions without explicit segmentation. Localization accuracy and computational efficiency were compared across different model resolutions and against a finite-element method (FEM) using a five-tissue head model.Main results. Segmentation-free models achieved localization errors below 23 mm while maintaining stable accuracy even at coarser resolutions. Estimated current density distributions exhibited smooth transitions across tissue boundaries. Computational time was reduced by 87.9% compared with FEM at 2.0 mm resolution. Among segmentation-free models, CondNet-TART yielded the highest localization accuracy and stability.Significance.The proposed segmentation-free framework provides efficient and accurate EEG source localization with substantially reduced computational cost. These features support time-sensitive applications such as presurgical functional mapping and real-time neuroimaging.

Aortic dissection (AD) is a life-threatening cardiovascular emergency. Non-contrast-enhanced computed tomography (NCE-CT) could provide timely AD screening with fewer contraindications compared to CE-CT imaging. However, NCE-CT examinations lack distinctive imaging characteristics of AD, leading to high rates of missed diagnoses and misdiagnoses, and increased radiologist workload. In this paper, we propose a novel end-to-end multi-task framework for automated aortic segmentation and AD detection using NCE-CT images. The framework comprises three main components: a deformable feature extractor enhancing aorta tubular-feature attention, an adaptive geometric information extraction module to optimize feature sharing between segmentation and classification tasks via the transformer cross-attention mechanism, and a knowledge distillation module transferring diagnostic information from the CE-CT-based teacher model to the NCE-CT-based student model. Multi-center tests across 3 internal and 2 external centers demonstrated that our model outperformed existing methods both for segmenting the aorta and detecting AD. Specifically, for segmenting the aorta, our framework achieved dice of 0.928 and 0.909, Jaccard index (JI) of 0.867 and 0.858, mean intersection over union (MIoU) of 0.932 and 0.913, and frequency-weighted IoU (FWIoU) of 0.995 and 0.994, in internal and external testing datasets, respectively. For identifying AD patients from non-AD patients, our framework achieved accuracies of 0.911 and 0.840, sensitivities of 0.925 and 0.888, andF1-scores of 0.922 and 0.836, in internal and external testing datasets, respectively. Ablation experiment demonstrates the effectiveness of each module. The proposed model may serve as an effective diagnostic assistant for radiologists, acting as a 'second pair of eyes' to assist in AD screening using NCE-CT images.

Objective.Incorporating iodine into deoxyribonucleic acid (DNA) bases offers a strategy to enhance radiotherapy. The iodine increases the photoabsorption cross-section and can promote DNA disruption and cell death in cancerous tissue. In this study, we investigate the local fragmentation mechanisms of iodinated DNA and the spatial extent of damage propagation following photoactivation.Approach.Single-stranded DNA oligonucleotides consisting of 2-5 bases, in which the methyl group of thymine is substituted with an iodine atom, were irradiated with synchrotron x-rays above the iodine L-shell ionisation threshold (4900 eV). Fragmentation patterns were extracted by subtracting background spectra obtained below the threshold (4500 eV), and the results were complemented by Born-Oppenheimer molecular dynamics simulations to resolve bond breaking at the atomic level.Main results.We find that longer oligonucleotide chains predominantly generate larger, high-m/zfragments, while shorter sequences produce a wider variety of small fragments. Backbone cleavage is observed in all sequences, with phosphate- and sugar-based ions dominating the spectra. Bond scission extends up to five bases from the iodination site, with the heaviest stable fragment containing two bases.Significance.Suppose this effect is extrapolated to genomic DNA, which includes about 29.5% thymine. In that case, the amount of thymine replaced by iodinated uracil can help estimate the extent of DNA damage that might occur during radiation therapy using iodine as a radiosensitiser.

Objective.To design and validate a single, reconfigurable gamma probe that overcomes the static compromise between spatial resolution and sensitivity in radio-guided surgery, enabling both rapid lesion detection and precise margin delineation.Approach.A dual-layer lead collimator was designed for a LaBr₃(Ce)-SiPM detector. A validated analytical model coupled with a multi-objective genetic algorithm (NSGA-II) was used to explore the theoretical performance limits and identify optimal geometries. A two-phase computational search identified a single, universal geometry that can be switched intraoperatively between a high-sensitivity (HS) mode and a high-resolution (HR) mode by adjusting collimator positions.Main results.The universal design, at a 30 mm distance, achieves a spatial resolution of 6.41 mm full width at half maximum (FWHM) in HR mode and a sensitivity of 1483 counts per second (cps)/MBq in HS mode. The optimization framework identified specialized, distance-specific theoretical designs with resolutions as fine as 3.26 mm FWHM. The underlying detector's energy resolution is sufficient to distinguish between ⁹⁹_mTc (140.5 keV) and¹²³I (159 keV).Significance.This work presents a practical, single-instrument solution that offers surgeons the intraoperative flexibility to prioritize either rapid detection or precise delineation. The developed design methodology provides a robust framework for creating next-generation, application-specific surgical guidance tools.

Objective.Adaptive radiotherapy aimed to account for inter- and intra-fractional anatomical changes to improve target coverage and spare normal tissues. Commercial softwares could quantify anatomical and setup differences between planning computed tomography (pCT) and image-guidance cone-beam computed tomography (CBCT) using density gamma passing rate (dGPR). This study evaluated the utility of dGPR as an automated, site-specific trigger for offline adaptive workflows, using statistical process control (SPC) to define tolerance thresholds and correlating dGPR with setup errors, planning target volume (PTV) coverage, and longitudinal stability.Approach.240 patients across six anatomical sites were retrospectively analysed. First-fraction CBCTs were compared to pCTs using MobiusCB to compute dGPR. SPC analysis was performed to establish site-specific tolerance limits. Rigid phantom studies were conducted to quantify dGPR sensitivity to setup errors. Correlation between dGPR and PTVV_100%was assessed in patients with repeat CTs, and longitudinal stability of dGPR across fractions was evaluated.Main results.SPC-derived lower action limits (Al) ranged from 97.3% (brain) to 82.2% (breast), reflecting site-specific anatomical variability and imaging protocols. Phantom studies verified dGPR sensitivity due to rigid shifts, with head dGPR decreasing to as low as 83.5% at 8 mm translation, while pelvis dGPR remained above 93.2% for the same shift, reflecting greater tolerance due to its larger volume. In head-and-neck patients, dGPR correlated moderately with changes in PTVV_100%(r= 0.56), with no coverage losses >10% observed when dGPR exceeded 90%. Longitudinal analysis showed stable dGPR for most sites, but gradual declines in head-and-neck patients, with some values falling below SPC-derived threshold of 94.3% towards the end of treatment.Significance.dGPR offered a practical, automated, and site-specific metric for detecting anatomical changes and setup errors during radiotherapy. SPC-derived thresholds provided robust action levels tailored to each site, and MobiusCB enabled automated alerts when thresholds were exceeded, reducing reliance on subjective image inspection.