Physics in medicine and biology最新文献

Objective: In this work, we solve the fully non-linear dynamic pharmacokinetic fluorescence photoacoustic tomography (PK-FPAT) problem for pointwise reconstructions for the first time in literature. Approach: We use the 2D-blob basis functions to represent the object, which have been known to yield very good localization in reconstructions, while significantly reducing the number of unknowns. The underlying state derivatives are evaluated via an efficient non-sequential sensitivity scheme for obtaining the derivatives of the two-compartment model. The inverse problem is solved in a dual-grid framework, where the forward problem is solved on a standard finite element (FEM) grid at each iterate, while reconstructing the parameters in blob basis via Gauss-Newton and gradient filtering schemes. Main results: To the best of our knowledge, the current work demonstrates the first pointwise formulation and reconstructions for fully nonlinear PK-FPAT. Non-sequential sensitivity based gradient and Gauss-Newton filters-based reconstruction frameworks in a blob-basis representation have been developed. Numerical studies on cancer-mimicking phantoms validate the proposed scheme, yielding good localization of the reconstructed parameters with satisfactory correspondence to the ground-truth parameter values. Significance: The proposed non-sequential state derivative framework with the blob-basis representations offers significant computational advantages via both efficient evaluation of state derivatives and the sparse representations of the parameters, therein enabling the scalability of PK-FPAT based pharmacokinetic imaging to full 3D point-wise reconstructions for real world imaging.

Objective.With every treatment vault being different, the impact of geometrical parameters such as the signal source-to-camera distance on dose proportionality must be evaluated. The aim of this study is to characterize the Cherenkov signal and polarization state as a function of the source-to-camera distance. Our hypothesis is that with increasing distance, the need for angular correction distributions decreases, resulting in acquisition of a polarized Cherenkov signal directly proportional to dose.Approach.A water tank and a polyvinyltoluene-based plastic scintillator volume were irradiated by a 6 MV beam to respectively produce Cherenkov emissions as well as a control signal. Monte Carlo reference simulations were performed using TOPAS. Acquisitions of the Cherenkov signal were achieved using a cooled CCD camera and a time-gated intensified CMOS camera. By fitting a modified Malus Law to the Cherenkov acquisitions, the total Cherenkov signal intensity and its purely polarized component was extracted. Signal source-to-camera distance of 0.5, 1, 2, 3 and 4 m were tested to evaluate this distance's impact on the signal distributions. Projected percent depth dose (PPDD) and projected transverse profiles calculated from the different signal sources were then compared.Main results.All PPDDs at camera distances of 3 and 4 m agree with Monte Carlo (⩽5%) over depths ranging from 1.5 to 16 cm. Cherenkov PPDDs at camera distances of 0.5 and 1 m show significant discrepancies (⩾5%) compared to MC because no angular corrections are applied. Over the plateau region of projected transverse profiles, general agreement with MC is achieved. Thirteen of the 17 luminescence-based beam widths show⩽5% differences with MC.Significance.This study confirms the above-mentioned hypothesis up until the image quality diminishes. For this work's setup, the optimal camera distance for dosimetry using Cherenkov polarized imaging was found to be between 3 and 4 m.

Objective: This study aimed to determine the optimal coil orientation for transcranial magnetic stimulation (TMS) of the hand motor area by integrating physiological and computational approaches.

Approach: Resting motor thresholds (RMTs) were measured in 10 healthy volunteers for the first dorsal interosseous (FDI) and abductor digiti minimi (ADM) muscles when stimulating the primary motor cortex (M1) with a coil set at several orientations ranging from 0° to 90°. Electric field (EF) distributions were estimated using individualized head models constructed from magnetic resonance imaging (MRI) data of the same 10 participants in the measurements, as well as additional 135 MRI-derived models. Simulations employed a scalarpotential finite-difference method to quantify the EF strength in the M1-hand region across orientations.

Main results: The lowest RMTs were obtained between 30° and 45° for both muscles, and the optimal angle depended on the target muscle (45° for FDI and 30° for ADM). EF simulations supported the above RMT findings. It showed a maximum EF strength in the same angle range across all head models, with consistent angular dependence of RT despite small coil displacements. Anatomical analysis revealed that the cortical surface orientations in the M1-hand area were frequently 30°-45° to the parasagittal plane.

Significance: These findings support the current guidelines' recommendation of a ~45° orientation, but suggest that a 30°-45° range better aligns EF with cortical geometry. Individualized optimization can further improve the precision and efficacy of TMS.

Objective: The generalized equivalent uniform dose (gEUD) is a well-established metric for radiotherapy dose optimization, particularly for normal tissues. However, the lack of theoretical clarity in its application has often led to empirical use in clinical practice. This study aims to reformulate gEUD-based optimization in a theoretical framework using the gEUD curve concept, and to develop a robust optimization strategy tailored for stereotactic radiation therapy (SRT), with a specific focus on bone and spinal metastases.

Approach: We interpreted the gEUD as a smooth function of its a-parameter, forming a continuous curve whose deformability decreases with increasinga-value. Based on this understanding, we proposed two methods: the multiple gEUD objective (MgEUDO) for optimizing the remaining volume at risk (RVR), and the selective gEUD objective (SgEUDO) for critical organs at risk (OARs). These methods were retrospectively evaluated in 22 patients who received five-fraction SRT (35 Gy total prescription).

Main results: Using consistent gEUD-based optimization, all cases achieved clinically favorable dose distributions. Compared to conventional normal tissue objective (NTO) constraints, the proposed strategy reduced mean dose to surrounding tissues by 10%, while improving tumor dose coverage by 0.6%. SgEUDO further achieved 39% and 37.5% mean dose reductions in the left and right kidneys, respectively.

Significance: Our theoretical and practical refinement of gEUD optimization enables systematic control of dose distribution with reduced inter-planner variability. The combined MgEUDO and SgEUDO strategies provide a generalizable and clinically effective framework for high-precision radiotherapy.

Objective.Phase-contrast computed tomography (PCT) of the breast has previously been shown to produce higher-quality images at lower radiation doses without the need for breast compression. The present study is aimed at further reduction of the radiation dose in PCT, while preserving or further increasing the image quality, by applying supervised deep learning denoising of reconstructed PCT images. This work was carried out in preparation for live patient PCT breast cancer imaging, initially at specialised synchrotron facilities.Approach.PCT scans of 34 fresh full mastectomy samples were acquired using propagation-based phase-contrast imaging with 32 keV monochromatic parallel x-rays at mean glandular doses of 4 mGy and 24 mGy. All scans were reconstructed using Filtered Back Projection algorithm with Paganin's phase retrieval. A supervised U-Net-based deep learning denoising model was trained on 28 pairs of 4 mGy and 24 mGy scans and then applied to denoise the remaining 6 stacks of reconstructed 4 mGy images. Denoised PCT images were quantitatively evaluated using signal-to-noise ratio (SNR), spatial resolution, structural similarity index measure (SSIM) and peak-SNR (PSNR). The images were also visually compared and systematically assessed by experienced medical imaging specialists and radiologists.Main results.Deep learning denoising increased SNR by a factor of four while spatial resolution remained unchanged. SSIM and PSNR improved from 0.89 and 37 dB to 0.96 and 42 dB, respectively. Visual assessors significantly preferred the denoised images over the original 4 mGy images, and visual assessment indicated no increase in perceived artefacts in denoised images compared with the original 4 mGy images.Significance.Deep learning-based image denoising can further improve image quality in PCT without increasing radiation dose in imaging of mastectomies, supporting the feasibility of lower-dose PCT protocols or improved image quality for future clinical applications.

Objective.This study aims to develop a motion-robust magnetic resonance fingerprinting (MR-MRF) technique for liver cancer imaging to eliminate the need for breath-hold scanning.Approach.To mitigate respiratory motion artifacts in free-breathing abdominal MRF, the MR-MRF technique comprising two core components. First, respiratory motion is modeled by applying an isotropic total variation (TV)-regularized registration algorithm between a target end-of-exhalation (EOE) phase and three motion phases. Second, motion-resolved tissue property maps are reconstructed using a low-rank TV optimization framework, which incorporates the estimated inter-phase motion to align all acquired MRF dynamics to the EOE phase. MR-MRF is evaluated by 22 patients (mean age, 62 years ± 10 [SD]; 15 males and 7 females) with hepatocellular carcinoma. Radiologist's blinded assessment and organ boundary sharpness measurements are performed to evaluate the image quality of MR-MRF-derived tissue maps. The test-retest tissue quantification repeatability is assessed by two consecutive MRF scans with distinct breathing patterns. Paired Student'st-test is used for statistical significance analysis with ap-value threshold of 0.05.Main results.MR-MRF achieved successful reconstruction of motion-resolved tissue maps at EOE phase, with blinded radiologist assessment yielding an average score of 3 (moderate quality-sufficient for diagnosis) for overall image impression. The FWHM of organ boundaries in MR-MRF-derived tissue maps is 3.1 mm ± 1.7 mm, significantly lower than motion-blurred tissue maps (9.9 mm ± 3.4 mm,p-value < 0.0001). Test-retest analysis demonstrated good repeatability: liver coefficient of variation was 5.5% ± 7.1% (T1), 8.2% ± 4.4% (T2), and 5.0% ± 2.0% (PD), with excellent linear agreement (R²= 0.96, 0.80, and 0.85 for T1, T2, and PD, respectively).Significance.This study establishes the technical foundation of MR-MRF to achieve repeatable and quantitative liver T1/T2/PD mapping under free-breathing conditions at 3 T. The results validate the feasibility of addressing respiratory motion in abdominal multi-parametric quantitative MRI.

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.