Biomedical Physics & Engineering Express最新文献

{"title":"Shaping robust dynamic inversion control of neural cell dynamics.","authors":"Rongting Yue, Yen-Che Hsiao, Abhishek Dutta","doi":"10.1088/2057-1976/ae07e6","DOIUrl":"10.1088/2057-1976/ae07e6","url":null,"abstract":"<p><p><i>Objective.</i>In this work, we aim to enforce the spiking of the membrane potential of a single neuron or a neuronal network, described by dynamical models, by controlling the current injection in the presence of model uncertainty and synaptic noise.<i>Approach.</i>In this study, we propose Shaping Robust Dynamic Inversion (SRDI) as a robust nonlinear control technique, which uses dynamic inversion of neuronal dynamical systems and shapes the error surface to derive a current control signal that enforces the spiking of membrane potential under model uncertainty.<i>Main results.</i>We apply SRDI to Hodgkin-Huxley model, integrate-and-fire model, and FitzHugh-Nagumo model to achieve controlled neuron spiking. Comparative studies show that SRDI outperforms classical dynamic inversion in robustness and linear model predictive control in computational time.<i>Significance.</i>SRDI enables precise and efficient neural control by shaping error dynamics, handling nonlinearities, and maintaining robustness to noise and model uncertainty, achieving controlled timing for single spikes, spike trains, and small neuronal networks.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145079125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the impact of modified cell death models on hypofractionated radiation therapy efficacy.","authors":"Islam Sagov, Аида Arsenovna Сорокина, E S Sukhikh, E A Selikhova, Yu S Kirpichev","doi":"10.1088/2057-1976/ae1039","DOIUrl":"https://doi.org/10.1088/2057-1976/ae1039","url":null,"abstract":"<p><strong>Objective: </strong>Nowadays the linear-quadratic model (LQ) is the most used model to estimate the biological effective dose (BED) and the equivalent dose in 2 Gy fractions (EQD2) for different fractionation regimens. Nevertheless, it is debated of applicability to use LQ model for hypofractionation. The objective of this study is to evaluate the LQ model in comparison with other radiobiological models concerning the adequacy of biological equivalent dose in 2 Gy fractions assessment across various hypofractionation regimens.&#xD;Methods: The study was conducted for two cases: the prostate gland in the pelvic region and squamous cell carcinoma (SCC) in the head and neck region. Five radiobiological models including the LQ model, modified linear-quadratic (MLQ), linear-quadratic-linear (LQL), universal survival curve (USC), and Pade linear-quadratic (PLQ) models were compared for tumor control probability (TCP) and EQD₂ predictions. Published clinical outcomes (including local control, disease-free survival, and overall survival rates) were analyzed to identify clinically equivalent fractionation regimens. The radiobiological models were then evaluated by comparing calculated EQD2 and TCP values with clinical data for these equivalent regimens.&#xD;Results: Modified radiobiological models showed that the LQ model overestimates the dose in hypofractionation. The dose limit at which the LQ model is applicable depends on the localization and type of tumor: for the prostate gland the value was 4.3 Gy, for the head and neck region 8.5 Gy.&#xD;Conclusions: The applicability of the LQ model in hypofractionation depends on the tumor alpha/beta value: the LQ model more sensitive to locations with low alpha/beta values and, conversely, less sensitive to locations with high alpha/beta values. Among the alternatives, the MLQ model is recognized as the most practical alternative, combining a small number of parameters with resistance to variations. While modified models show efficacy, further clinical validation is needed to balance tumor control with normal tissue toxicity risks.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Undulations and bending in peripheral nerves benefit coil positions projecting transverse fields.","authors":"J Rapp, B Sandurkov, A Lindenthal, L Mallaun, W Hemmert, B Gleich","doi":"10.1088/2057-1976/ae0ad8","DOIUrl":"10.1088/2057-1976/ae0ad8","url":null,"abstract":"<p><p>For peripheral magnetic stimulation it is widely accepted that the field components parallel to the nerve are responsible for stimulation. However, experimental findings have often suggested that transverse field components contribute as well or are even dominant. A reason for that discrepancy could be undulations or curving of the nerve. As a consequence, the question of ideal coil placement for magnetic stimulation is still not conclusively answered. To identify beneficial coil positions, we quantified the impact of undulation and nerve bending in this study. First, we performed neuronal simulations with different extent of fascicle and fibre undulations inside the field distribution of a figure-of-8 coil. Second, we simulated median nerve stimulation using an anatomical model of the forearm to include the contribution of nerve bending. Third, we conducted median nerve stimulation on healthy subjects with different wrist positions to manipulate undulations. Our simulations suggested both fascicle and fibre undulations cause transverse field components to cause lower thresholds than parallel ones. Simulations on median nerve stimulation showed that the position of the coil in relation to the nerve course has more impact than the orientation itself. Finally, the experimental validations confirmed that transverse coil positions produce smaller stimulation thresholds. Further, we saw that bending the wrist has a potential influence on thresholds, possibly due to undulations. We conclude that placing a round coil centrally above the nerve yields the lowest thresholds.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Histopathological cancer images classification with Deng entropy.","authors":"Elva Estrada-Estrada, Aldo Ramirez-Arellano, Pilar Ortiz-Vilchis","doi":"10.1088/2057-1976/ae07e8","DOIUrl":"10.1088/2057-1976/ae07e8","url":null,"abstract":"<p><p>Histopathological imaging is of paramount importance for the initial detection, diagnosis, and classification of tumors. Recurrent neural networks and convolutional neural networks have led to substantial advancements in digital pathology, thereby enhancing classification accuracy. Tsallis and Shannon entropies were employed to optimize cancer classification. However, certain constraints remain to be addressed, including the need to mitigate noise and uncertainty. This study aimed to classify histopathological images of cancer using Deng entropy and long short-term memory (LSTM) as a novel approach that provides accurate assessments for pathologists to differentiate between normal and abnormal tissues. The computed Deng entropy, based on the box covering method, is used as a vector to feed bidirectional LSTM (bLSTM) networks and obtain Deng's information dimensions. This innovative approach obtains Deng entropy from different scales (box sizes), yielding a measure that captures differences in complexity. Three histopathological datasets (BreakHis, Lung-colon, and PANDA) were analyzed. Statistical tests were performed on each dataset to determine the most effective discrimination of histopathological images between the information and Deng information dimensions. The binary breast classification exhibited a higher performance accuracy rate of 0.98. For multiclass analysis, the accuracy was 0.99. The lung image model exceeded a classification accuracy of 0.98, and for the colon, it was 0.99. The prostate image accuracy was 0.924. Deng entropy provides a precise classification system for histopathological images of breast, colon, and lung cancers. Our results demonstrated that the proposed methodology can achieve satisfactory cancer classification.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145079635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel sEMG-based hand gesture prediction method using a new motion detection algorithm and an LCNN model.","authors":"Jiapeng Wang, Zhiheng Sheng","doi":"10.1088/2057-1976/ae0a57","DOIUrl":"10.1088/2057-1976/ae0a57","url":null,"abstract":"<p><p>This paper proposes a novel gesture prediction method for accurately predicting hand gesture types from raw sEMG signals in real time. First, we utilize a linear combination of the mean and standard deviation of sEMG signals within a sliding window to define a new information index in the time domain. Based on this information index, we introduce a new motion detection algorithm that more accurately captures the start and end times of hand gesture motions. Second, we design a new LCNN model, in which LSTM is integrated into the middle of the encoder, allowing for the direct fusion of multi-scale features to prevent the separation of local and temporal features. An ablation study demonstrates that each functional module of the proposed LCNN model positively contributes to the performance of sEMG pattern recognition. The evaluation of the proposed hand gesture prediction method was conducted by comparing it with existing methods using two publicly available datasets. In the experiment involving the dataset Zhang<i>et al</i>(2020<i>Sensors</i>,<b>20</b>3994), the average prediction accuracy for 21 gestures reaches 92.4%. In the experiment with the dataset Krilova<i>et al</i>(2018<i>UCI Machine Learn. Repo.</i>doi: 10.24432/C5ZP5C), the average prediction accuracy for six hand gestures reaches 82.7%. The results of this study indicate that our motion detection algorithm significantly outperforms the threshold method based on a single time-domain information standard deviation (92.4%,<i>p</i>= 0.0136). Furthermore, our LCNN model also surpasses GRU, LSTM, and other models in terms of prediction accuracy and real-time performance. The research results of this paper highlights the superiority in accuracy and real-time performance of our proposed hand gesture prediction method, which holds great potential for practical applications.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145130054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurofeedback & P300 toolbox for neurorehabilitation: measurement and analysis of brain activity, network and cognitive decline-related P300.","authors":"Eunggyu Lee, Youjin Kang, Hyunjin Kim, Jungsoo Lee","doi":"10.1088/2057-1976/ae0ad7","DOIUrl":"10.1088/2057-1976/ae0ad7","url":null,"abstract":"<p><p>Neurofeedback is a training technique to modulate neural functions by training self-regulation of brain activity in real-time. This technique has been applied in neurorehabilitation in patients with neurological and cognitive disorders. In addition, P300 has been used as a tool to evaluate these patients' cognitive functions. One type of event-related potential called the P300 reflects cognitive abilities and attention toward stimuli. However, setting up an environment to measure these is challenging due to a lack of software design experience. In this study, an electroencephalography (EEG)-based Neurofeedback and P300 Toolbox (NPT) was developed by integrating the Neurofeedback Tool and the P300 Tool. Most existing EEG analysis tools are specialized for offline analysis and do not support real-time signal processing or feedback during experiments. Although some tools offer real-time capabilities, using them for neurofeedback often involves additional configuration or development. Therefore, the NPT is designed specifically for real-time analysis environments. It provides real-time functional connectivity and neurofeedback protocol features such as alpha asymmetry and high beta down, with adjustable parameters for visualization. Furthermore, the NPT offers an integrated structure that enables seamless transitions between real-time data acquisition and offline analysis. This feature enhances the repeatability of experiments and the consistency of analysis, which is particularly useful for neurorehabilitation. Traditional P300 tools primarily offer signal processing and analysis functions, while P300 measurement is often performed using separate systems or software, likely due to time delays that can affect P300 latency. NPT is designed to support time-delayed compensation algorithms to integrate measurement and analysis within a single environment. All functions and options were confirmed to work properly. Experiments were conducted with the Neurofeedback Tool and P300 Tool on six participants without diagnosed conditions using NPT, confirming that all signals were detected properly.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of metal nanoparticles on cell survival predicted by the local effect model for cells in suspension and tissue. Part 1: theoretical framework.","authors":"Hans Rabus, Leo Thomas","doi":"10.1088/2057-1976/ae07e7","DOIUrl":"10.1088/2057-1976/ae07e7","url":null,"abstract":"<p><p>This work investigates the change in cell survival predicted by the local effect model (LEM) for an irradiated cell containing metal nanoparticles (MNPs) depending on the distribution of neighboring cells and the uptake of MNPs into the cells. In this first part of the paper, the theoretical framework is described, which is based on analytical weighting functions for the energy deposition around a single MNP and radially symmetric distributions of MNPs. The weighting functions allow calculation of the radial profile of the absorbed dose in the cell nucleus as well as the mean dose and the mean square of the dose in the nucleus. The latter two quantities determine cell survival according to the LEM. The weighting functions are applied to isolated cells in a localized MNP distribution, cells in solution, and densely packed cells in tissue. It is shown that only for the idealistic case of complete uptake of MNPs it is sufficient to consider an isolated cell, as this otherwise leads to significant underestimation in more realistic situations. In the case of cells in tissue, the MNP concentration within the range of secondary particles around the cell must be taken into account. Different packing densities of the cells may lead to values differing by up to 30% for the mean dose in the cell nucleus, depending on the conceived scenario for the uptake of MNPs. The weighting function offers a versatile method for assessing cell survival under irradiation in the presence of MNPs by the LEM, which is more general than previously reported approaches which relied on a power-law dependence of the radial dose distribution.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145079642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Streamlining custom bolus fabrication via 3D-to-2D unfolding using spectral mesh flattening.","authors":"James J Sohn, Ethan Stolen, Siddhant Sen, Amritha Praveen, Jeonghoon Park","doi":"10.1088/2057-1976/ae09b1","DOIUrl":"10.1088/2057-1976/ae09b1","url":null,"abstract":"<p><p>Accurate fabrication of custom boluses is essential in radiation therapy to ensure optimal dose delivery to superficial tumors, particularly in anatomically complex regions. This study presents a novel method that utilizes spectral mesh flattening (SMF) to unfold three-dimensional (3D) virtual bolus designs into two-dimensional (2D) contours, aiming to enhance the fabrication workflow and improve reproducibility in bolus shape and placement. Using computed tomography (CT) scans of a phantom with radiopaque wires delineating target regions such as the nose, chin, and ear, virtual boluses of 0.5 cm thickness were designed within a treatment planning system. The 3D mesh geometries of these boluses were then exported and processed using a custom-developed software tool, ONCOFLAT, which implements the SMF algorithm to generate 2D representations while minimizing geometric distortion. These 2D contours were printed and used as cutting guides for the fabrication of flat bolus materials. After fabrication, the boluses were applied to the phantom and rescanned, and their accuracy was assessed by comparing the physical boluses to the original virtual designs using the Dice Similarity Coefficient (DSC) and the Hausdorff distance. The SMF algorithm successfully unfolded complex 3D geometries into 2D contours, and the ONCOFLAT software enabled a streamlined process that reduced the total design-to-fabrication time to under five minutes. The fabricated boluses closely conformed to the intended anatomical surfaces, with DSC values ranging from 0.59 to 0.62 and average Hausdorff distances below 1.3 mm. The 95% Hausdorff distances ranged from 3.50 mm to 4.22 mm. These results demonstrate that the integration of SMF within ONCOFLAT offers a fast, reproducible method for fabricating patient-specific boluses for complex anatomy. The approach shows promise in improving the consistency and effectiveness of dose delivery in radiation therapy, representing a meaningful advancement in personalized treatment planning.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MEFD dataset and GCSFormer model : Cross-subject emotion recognition based on multimodal physiological signals.","authors":"Xiangyu Deng, Zhecong Fan, Wenbo Dong","doi":"10.1088/2057-1976/ae0e28","DOIUrl":"https://doi.org/10.1088/2057-1976/ae0e28","url":null,"abstract":"<p><p>Cross-subject emotion recognition is an important research direction in the fields of affective computing and brain-computer interfaces, aiming to identify the emotional states of different individuals through physiological signals such as functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG). Currently, most EEG-based emotion recognition datasets are unimodal or bimodal, which may overlook the emotional information reflected by other physiological signals of the subjects. In this paper, a multimodal dataset named Multimodal Emotion Four Category Dataset (MEFD) is constructed, which includes EEG, Heart Rate Variability (HRV), Electrooculogram (EOG), and Electrodermal Activity (EDA) data from 34 participants in four emotional states: sadness, happiness, fear, and calm. This will contribute to the development of multimodal emotion recognition research. To address the recognition difficulty caused by individual differences in cross-subject emotion recognition tasks, a classification model named Global Convolution Shifted Window Transformer (GCSFormer) composed of an EEG-Swin Convolution module and an improved Global Adaptive Transformer (GAT) module is proposed. By using a parallel network, the feature discrimination ability and generalization ability are enhanced. The model is applied to classify the EEG data in the self-built MEFD dataset, and the results are compared with those of mainstream methods. The experimental results show that the proposed EEG classification method achieves the best average accuracy of 85.36%, precision of 85.23%, recall of 86.35%, and F1 score of 84.52% in the cross-subject emotion recognition task. The excellent performance of GCSFormer in cross-subject emotion recognition task was verifie.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precise Path Planning for Robot-assisted Craniotomy: A CT-driven Virtual Center Method.","authors":"Li Zhichao, Wenqing Ren, Hao Ren, Xiaodong Ma, Dan Wu","doi":"10.1088/2057-1976/ae0e26","DOIUrl":"https://doi.org/10.1088/2057-1976/ae0e26","url":null,"abstract":"<p><strong>Objective: </strong>Craniotomy is a critical prerequisite for numerous neuro-surgeries, including intracranial tumor resection and cerebral hemorrhage decompression. However, conventional manual craniotomy methods are often time-consuming, labor-intensive, and associated with limited efficiency and safety. Robotic systems offer significant potential to enhance craniotomy procedures by enabling precise positioning and stable motion control, thereby improving safety, accuracy, and efficiency. In this study, we proposed a novel path planning method for robotic craniotomy that automatically generates surgical paths using solely computed tomography (CT) images. &#xD;&#xD;Approach: The craniotomy process is divided into two stages: drilling and subsequent milling to connect the drilled holes. The drilling path is determined by the intersection of the skull structure and surgeon-defined drilling intents. A virtual-center method is introduced to adaptively compute an initial milling path from the drilling path, which is further optimized to minimize invasiveness and smoothed for robotic cranial milling. &#xD;&#xD;Results: Validation and evaluation were conducted using 10 skull phantoms and 3 living dogs. The results of high success rates demonstrated that our method generated clinically approved outcomes at both anatomical profile and in vivo levels. &#xD;&#xD;Significance: This research proposes an CT image-based preoperative path planning method for robotic craniotomy operations. The proposed approach demonstrates seamless integration with force-based robotic surgical systems, highlighting their potential to enhance current craniotomy techniques while establishing a foundation for future developments in autonomous robotic neurosurgery.&#xD.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}