Biomedical Physics & Engineering Express最新文献

A new Convex Hull microdosimetric technique has been incorporated in Geant4-DNA to enhance geometrical modeling of microdosimetry at nanometric scales. This new microdosimetric technique aims to provide a direct relationship between electron track structures and their effectiveness in radiobiology. Track structures in liquid water were simulated using monoenergetic electrons ranging from 0.1 keV to 1000 keV, including clinical electron beams between 6 MeV and 18 MeV. The CH technique, based on the Jarvis-March gift wrapping algorithm, has been incorporated in Geant4-DNA to increase computational efficiency by geometrically encapsulating track-associated microdosimetric quantities. Dose mean lineal energy and frequency mean lineal energy were used in conjunction with the Microdosimetric Kinetic Model to estimate radiobiological parameters and relative biological effectiveness. These results are compared with the reference results obtained using the KURBUC and FLUKA codes. The CH approach correctly predicts the reference microdosimetric quantities within 5% accuracy for the micrometer scale, while larger systematic differences are found on the nanometer scale due to the different definitions of the track volume. The α parameter increases with increasing incident particle energy, ranging from 0.094 Gy⁻¹ for an incident particle energy of 0.1 keV to 0.265 Gy⁻¹ for an incident particle energy of 5 keV, which results in an RBE of approximately 1.04, showing the effect of ionization clustering. A comparison of the results with the reference results reveals differences of 7-12%. The RBE results for clinical electron beams are found to be nearly independent of the incident particle energy and remain close to 0.85 when normalized to the results obtained for X-rays. The CH-based approach provides an efficient computational scheme for the calculation of the micro- and nanodosimetric quantities, which allows the model-based calculation of the RBE and the quantification of the quality of the radiation on the nanometric scale.

In the innate immune system, natural killer (NK) cells are effector lymphocytes which control several tumor types and microbial infections by limiting disease spread and tissue damage. With tumor cell killing abilities, with no priming or prior activation, NKs are potential anti-cancer therapies. In clinical practice, NKs are used in intravenous injections as they typically grow as suspension, similar to other blood cells. In this study, we designed a novel and effective biomaterial-based platform for NK cell delivery, which includedin situNK cell encapsulation into three-dimensional (3D) biocompatible polymeric scaffolds for potential anti-cancer treatments. Depending on physical cross-linking between an alginate (ALG) polymer and a divalent cation, two natural polymers (gelatin (GEL) and hyaluronic acid (HA)) penetrated into pores and generated an inter-penetrating hydrogel system with improved mechanical properties and stability. After extensive characterization of hydrogels, NK cells were encapsulated inside using ourin situgelation procedure to provide a biomimetic microenvironment.

Objective. Intracavitary electrical stimulation is widely used in the treatment of urinary incontinence. However, it is contraindicated for a subset of patients, for whom current external electrode alternatives offer limited adaptability and therapeutic flexibility. This study compared the performance of a mobile, pen-like electrode with fixed external patch electrodes for pelvic floor muscle electrical stimulation in asymptomatic volunteers. The proposed electrode enables sequential stimulation of motor points and visual monitoring of muscle contraction, aiming to overcome limitations associated with intracavitary and fixed patch approaches.Methods. A parallel-group study (1:1) was conducted with 28 asymptomatic participants without diagnosed pelvic floor dysfunction (mean age: 25.4 ± 4.1 years; BMI: 22.8 ± 2.5 kg m^-2). Participants were randomly allocated to Group A (pen-like electrode, n = 14) or Group B (fixed patch electrodes, n = 14). Both groups underwent 12 sessions of neuromuscular electrical stimulation (NMES) using identical stimulation parameters. The primary outcome was the change in maximum vaginal pressure, measured by perineometry, after 12 sessions. Statistical analyses included paired t-tests for intra-group comparisons and independent t-tests for inter-group comparisons, with effect size estimation using Cohen'sd. For all statistical tests, we usedα = 0.05.Results. The increase in vaginal pressure was significantly greater in Group A than in Group B. Group A showed a mean pressure gain of 15.2 ± 3.5 mmHg (p <  0.001), whereas Group B showed a mean gain of 7.8 ± 2.9 mmHg (p = 0.002). The inter-group difference was statistically significant (p = 0.004), with a large effect size (Cohen's d = 2.2). Conclusion: Preliminary data suggest that the mobile pen-like electrode is associated with a greater increase in vaginal pressure in asymptomatic volunteers compared to fixed patch electrodes. Although limited by the small sample size and the absence of a clinical population, the findings indicate that the developed device may represent a promising alternative for perineal rehabilitation, warranting validation in larger, blinded clinical studies.

Ultrasound image degradation in the human body arises from the propagation and reflection of acoustical waves in a complex acoustical environment. The heterogeneous distribution of soft tissue and the variation in acoustical properties distort the ultrasonic beam causing deterioration in image quality, including loss of resolution and contrast. Here we establish a framework to construct images based on a separable (additive or multiplicative) representation of aberration, multiple reverberation, and trailing reverberation. A separable approach enables high modularity and flexibility when generating quantitatively degraded image datasets. The framework provides the capability to generate images with quantitative levels of image degradation related directly to imaging physics, thus allowing for a flexible approach for augmentation techniques in ultrasound imaging data sets, as demonstrated in the included repository code. Experimentally calibrated abdominal simulations were performed in Fullwave2 by matching relevant imaging metrics such as phase aberration, reverberation strength, speckle brightness and coherence length, to experimental measurements. Then, simulations were performed to separate and characterize the different components of image degradation. Finally, these components were scaled and combined to construct quantitatively degraded image datasets. Reverberation is shown to be depth and brightness dependent, while aberration and trailing clutter are not. This general framework was tested for values in acoustical ranges that significantly, synthetically, and independently enhance or reduce these effects compared to levels naturally occurring in the body. Identifying, quantifying, and modeling these differing and complex mechanisms of degradation can be used to develop and test rational approaches to overcome these degradation mechanisms to improve image quality, particularly for traditionally harder to image patients. Additionally, the framework to synthetically modify the effects of aberration, multiple reverberation, and trailing clutter is provided, allowing for the generation of augmented datasets with a wide range of degradation effects, based on imaging physics, to improve machine learning models.

Aims: Preoperative observation of computed tomography (CT) cross sections corresponding to standard transesophageal echocardiographic (TEE) views is useful for treatments using TEE. However, manually locating those CT cross sections from the CT volume is time consuming. This study aimed to develop a fully automatic method to locate those CT cross sections corresponding to standard TEE views.Methods: Developed method crops CT volume based on the heart segmentation model if input is a chest CT. CT cross sections corresponding to standard TEE views are coarsely located from cropped chest CT or cardiac CT based on the esophagus segmentation model and the plane localization model. The coarse cross sections are refined based on the anatomical landmark detection model. Accuracies of located CT cross sections were evaluated by five-fold cross validation with comparison to manually set cross sections, using 110 CT volumes. The results were also compared to the top and the middle axial section of the heart as baselines.Results: The mean of rotation error, probe position error, landmark error and structural similarity index between the predicted and manually set cross sections were 12.52 ± 8.27 degree, 13.90 ± 10.62 mm, 3.95 ± 3.48 mm and 0.64 ± 0.15, respectively, and these errors were significantly smaller than the baselines. The mean processing time was 41.34 ± 22.75 s.Conclusions: Our developed method can provide CT cross sections corresponding to standard TEE views without user operations, resulting in faster treatment planning with less effort and without inter user variability in treatments using TEE.

Recent advancements in cognitive impairment research have led to significant progress. Electroencephalography (EEG)-based cognitive state identification can detect early cognitive decline in the elderly, providing a critical window for intervention. As cognitive function worsens, neural activity patterns in the brain also change. Functional connectivity between brain regions, a key indicator of synchronized neural activity, is widely used to reveal brain network characteristics under different cognitive states. In this study, we introduce a novel framework, PowerSyncNet, based on functional connectivity to identify cognitive states. PowerSyncNet mainly consists of three modules. The Channel-Pair Feature Sequences Builder extracts features that characterize functional connectivity across different frequency bands. The Encoder4Band module captures temporal-frequency representations that reflect cognitive states and combines cross-band information to improve feature clarity. The Classifier then determines the corresponding cognitive states. We tested PowerSyncNet on the publicly available Chung-Ang University Hospital EEG (CAUEEG) dataset and our own collected Emotion and Cognition EEG (ECED) dataset. Results show that PowerSyncNet has superior cognitive identification capabilities compared with existing deep learning frameworks, facilitating early assessment and timely intervention for patients with cognitive impairment.

The non-invasive acoustic recording of fetal cardiac activity, known as fetal phonocardiogram (fPCG), provides valuable information for the early identification of fetal cardiac anomalies. However, dynamic artifacts, background noise, and powerline disturbances usually degrade signal quality, challenging analysis. The article proposes a multiscale frequency-time analysis denoising architecture. Robust peak detection is possible using a statistical envelope based on the Hilbert transform, following moving-average smoothing. Applying predefined rate intervals, physiological gating differentiates maternal and fetal heart rate (HR) streams. Fetal and maternal peaks can be accurately distinguished using only their physiological rate ranges, as this involves gating. Metrics such as the correlation coefficient, Root Mean Square Error(RMSE), and Signal-to-Noise Ratio (SNR) were used to evaluate system performance. The proposed method works well, especially in medical signal augmentation situations, where fine-grained oscillatory features are difficult to handle with conventional methods.

The increasing prevalence of cardiovascular diseases (CVDs) calls for innovative diagnostic solutions that are both accurate and scalable. ElectroCardioGrams (ECGs) remain central to cardiac assessment: However, manual interpretation is time consuming and error-prone. To address this challenge, we propose a lightweight multimodal generative AI framework capable of automatically interpreting ECG images and producing structured clinical reports. The framework builds upon the SmolVLM-500M-Instruct model, fine-tuned via Quantized Low-Rank Adaptation (QLoRA) to enable efficient deployment on standard hardware. A custom multimodal ECG dataset ,comprising image report pairs curated from authoritative clinical sources and augmented to mitigate class imbalance, served as the foundation for training. The proposed architecture integrates a vision encoder, a cross-modal fusion mechanism, and a language decoder to effectively align visual ECG representations with diagnostic narratives. Experimental evaluations demonstrate significant improvements in BLEU, ROUGE-L, and BERTScore metrics through a two-phase fine-tuning strategy, highlighting the model's ability to generate clinically coherent and semantically rich reports. Overall, this work contributes a scalable, interpretable, and resource efficient AI framework for cardiac diagnostics, bridging the gap between state of the art deep learning research and real-world clinical practice.

Differentiating between Alzheimer's disease (AD), frontotemporal dementia (FTD), and cognitively normal (CN) subjects remains a significant challenge in clinical neurodiagnosis. This study introduces an automated framework that combines electroencephalography (EEG) signal processing with graph-based deep learning (DL) to improve disease classification. The process begins with artifact suppression and a DL-driven filtering model to enhance EEG signal quality. Once filtered, the signals are segmented, and essential features are extracted to build graph representations that reflect brain connectivity patterns. These graphs are then analyzed utilizing a transformer-based graph neural network, enabling accurate classification of AD, FTD, and CN subjects. Results show that the model achieved highly competitive and well-balanced performance in both binary (AD-CN and FTD-CN) and ternary (AD-CN-FTD) classification tasks, with higher accuracy than existing EEG-based diagnostic methods, demonstrating the benefits of integrating signal filtration, graph representations, and transformer architectures. Overall, the findings suggest that this framework can serve as a reliable tool to support clinical decision-making for the early detection and differentiation of neurodegenerative disorders.