Biomedical Physics & Engineering Express最新文献

Determining event-related desynchronization onset latency of foot dorsiflexion in people with multiple sclerosis using the cluster depth tests. 使用聚类深度试验确定多发性硬化症患者足背屈的事件相关非同步发作潜伏期

IF 1.3

Biomedical Physics & Engineering Express Pub Date : 2025-01-24 DOI: 10.1088/2057-1976/adaaf8

L Carolina Carrere, Julián Furios, José A Biurrun Manresa, Carlos H Ballario, Carolina B Tabernig

{"title":"Determining event-related desynchronization onset latency of foot dorsiflexion in people with multiple sclerosis using the cluster depth tests.","authors":"L Carolina Carrere, Julián Furios, José A Biurrun Manresa, Carlos H Ballario, Carolina B Tabernig","doi":"10.1088/2057-1976/adaaf8","DOIUrl":"10.1088/2057-1976/adaaf8","url":null,"abstract":"Multiple sclerosis (MS) is a disorder in which the body's immune system attacks structures of the central nervous system, resulting in lesions that can occur throughout the brain and spinal cord. Cortical lesions, in particular, can contribute to motor dysfunction. Walking disability is reported as the main impairment by people with MS (pwMS), often due to limited ankle movement. This study explored the event-related desynchronization (ERD) onset latency of the sensorimotor rhythms during foot dorsiflexion in pwMS computed using an objective and independent of human criterion method, as an electroencephalogram (EEG) based biomarker. EEG signals were recorded in eight persons with neither neurological condition nor motor dysfunction and eight pwMS with relapsing-remitting, primary progressive or secondary progressive MS. Recordings were divided into three groups: control, more affected lower limb and less affected lower limb. The ERD-onset latency was determined using a method based on the percent of ERD time course and the cluster depth tests. The median and interquartile range of the ERD-onset latency were 1186.0 (1100.0, 1250.0) ms; 1064.0 (1031.0, 1127.0) ms for the more and less affected groups respectively, whereas the median and interquartile range for the control group was 656.0 (472.2, 950.0) ms. There was a significant delay in the ERD-onset latencies of the pwMS groups compared to the control group (p<0.001 for both comparisons). These findings suggest that the ERD-onset latency computed using the proposed method could be used as an EEG biomarker to evaluate disease progression or therapeutic interventions in pwMS.","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142999511","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}

引用次数: 0

Simulations of the potential for diffraction enhanced imaging at 8 kev using polycapillary optics. 用多毛细光学模拟8kev衍射增强成像的潜力。

IF 1.3

Biomedical Physics & Engineering Express Pub Date : 2025-01-24 DOI: 10.1088/2057-1976/ada9ed

Carmen A Bittel, Carolyn A MacDonald

{"title":"Simulations of the potential for diffraction enhanced imaging at 8 kev using polycapillary optics.","authors":"Carmen A Bittel, Carolyn A MacDonald","doi":"10.1088/2057-1976/ada9ed","DOIUrl":"10.1088/2057-1976/ada9ed","url":null,"abstract":"Conventional x-ray radiography relies on attenuation differences in the object, which often results in poor contrast in soft tissues. X-ray phase imaging has the potential to produce higher contrast but can be difficult to utilize. Instead of grating-based techniques, analyzer-based imaging, also known as diffraction enhanced imaging (DEI), uses a monochromator crystal with an analyzer crystal after the object. Analyzer-based systems most commonly employ synchrotron sources to provide adequate intensity, and typically use higher photon energies. In this work, a simulation has been devised to assess the potential for a polycapillary-based system. A polycapillary collimating optic has previously been shown to greatly enhance the intensity of the beam diffracted from the monochromatizing crystal. Detailed simulation of the optic is computationally intensive and requires comprehensive knowledge of the internal shape of the optic, so a simple geometric model using easier to obtain optic output data was developed and compared to the more detailed simulation. After verification, refraction band visibility was used as a quality parameter to address the effectiveness of the polycapillary-based DEI system at x-ray photon energies of 8 and 17.5 keV. The result shows promise for a polycapillary-coupled analyzer-based system even at low x-ray photon energy.","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142982480","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}

引用次数: 0

Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis. 多模态多视图双线性图卷积网络在轻度认知障碍诊断中的应用。

IF 1.3

Biomedical Physics & Engineering Express Pub Date : 2025-01-22 DOI: 10.1088/2057-1976/ada8af

Guanghui Wu, Xiang Li, Yunfeng Xu, Benzheng Wei

{"title":"Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis.","authors":"Guanghui Wu, Xiang Li, Yunfeng Xu, Benzheng Wei","doi":"10.1088/2057-1976/ada8af","DOIUrl":"10.1088/2057-1976/ada8af","url":null,"abstract":"Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing brain imaging data, ignoring other non-imaging data (e.g., genetic, clinical information, etc.) that may have underlying disease information. In addition, imaging data acquired from different devices may exhibit varying degrees of heterogeneity, potentially resulting in numerous noisy connections during network construction. To address these challenges, this study proposes a Multimodal Multiview Bilinear Graph Convolution (MMBGCN) framework for disease risk prediction. Firstly, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) features are extracted from magnetic resonance imaging (MRI), and non-imaging information is combined with the features extracted from MRI to construct a multimodal shared adjacency matrix. The shared adjacency matrix is then used to construct the multiview network so that the effect of potential disease information in the non-imaging information on the model can be considered. Finally, the MRI features extracted by the multiview network are weighted to reduce noise, and then the spatial pattern is restored by bilinear convolution. The features of the recovered spatial patterns are then combined with the genetic information for disease prediction. The proposed method is tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Extensive experiments demonstrate the superior performance of the proposed framework and its ability to outperform other related algorithms. The average classification accuracy in the binary classification task in this study is 89.6%. The experimental results demonstrate that the method proposed in this study facilitates research on MCI diagnosis using multimodal data.","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962165","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}

引用次数: 0

Monolithic U-shaped crystal design for TOF-DOI detectors: a flat top vs. a tapered top.

IF 1.3

Biomedical Physics & Engineering Express Pub Date : 2025-01-22 DOI: 10.1088/2057-1976/adaced

Miho Kiyokawa, Han Gyu Kang, Taiga Yamaya

引用次数: 0

Reconstruction of local three-dimensional temperature field of tumor cells with low-toxic nanoscale quantum-dot thermometer and cepstrum spatial localization algorithm. 低毒纳米量子点温度计和倒谱空间定位算法重建肿瘤细胞局部三维温度场。

IF 1.3

Biomedical Physics & Engineering Express Pub Date : 2025-01-22 DOI: 10.1088/2057-1976/ada9ee

Jun Yang, Lingyu Huang, HanLiang Du, Lei Zhang, Ben Q Li, Mutian Xu

{"title":"Reconstruction of local three-dimensional temperature field of tumor cells with low-toxic nanoscale quantum-dot thermometer and cepstrum spatial localization algorithm.","authors":"Jun Yang, Lingyu Huang, HanLiang Du, Lei Zhang, Ben Q Li, Mutian Xu","doi":"10.1088/2057-1976/ada9ee","DOIUrl":"10.1088/2057-1976/ada9ee","url":null,"abstract":"The optimal method for three-dimensional thermal imaging within cells involves collecting intracellular temperature responses while simultaneously obtaining corresponding 3D positional information. Current temperature measurement techniques based on the photothermal properties of quantum dots face several limitations, including high cytotoxicity and low fluorescence quantum yields. These issues affect the normal metabolic processes of tumor cells. This study synthesizes a low-toxicity cell membrane-targeted quantum dot temperature sensor by optimizing the synthesis method of CdTe/CdS/ZnS core-shell structured quantum dots. Compared to CdTe-targeted quantum dot temperature sensors, the cytotoxicity of CdTe/CdS/ZnS-targeted quantum dot temperature sensors is reduced by 40.79%. Additionally, a novel cepstrum-based spatial localization algorithm is proposed to achieve rapidly compute the three-dimensional positions of densely distributed quantum dot temperature sensors. Ultimately, both targeted and non-targeted CdTe/CdS/ZnS quantum dot temperature sensors were used simultaneously to label the internal and external regions of human osteosarcoma cells to obtain temperature data at these labeling positions. By combining this with the cepstrum-based spatial localization algorithm, the spatial coordinates of the quantum dot temperature sensors were obtained. Three-dimensional temperature field reconstruction of three local regions was achieved within a 12 μm axial range in living cells. The method described in this paper can be widely applied to the quantitative study of intracellular thermal responses.","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142982479","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}

引用次数: 0

GradeDiff-IM: an ensembles model-based grade classification of breast cancer. gradiff - im：基于集成模型的乳腺癌分级。

IF 1.3

Biomedical Physics & Engineering Express Pub Date : 2025-01-22 DOI: 10.1088/2057-1976/ada8ae

Sweta Manna, Sujoy Mistry, Keshav Dahal

{"title":"GradeDiff-IM: an ensembles model-based grade classification of breast cancer.","authors":"Sweta Manna, Sujoy Mistry, Keshav Dahal","doi":"10.1088/2057-1976/ada8ae","DOIUrl":"10.1088/2057-1976/ada8ae","url":null,"abstract":"Cancer grade classification is a challenging task identified from the cell structure of healthy and abnormal tissues. The practitioners learns about the malignant cell through the grading and plans the treatment strategy accordingly. A major portion of researchers used DL models for grade classification. However, the behavior of DL models is hidden type, it is unknown which features contribute to the accuracy and how the features are chosen for grading. To address the issue the study proposes a Grade Differentiation Integrated Model (GradeDiff-IM) to classify the grades G1, G2, and G3. In GradeDiff-IM, different ML models, are used for grade classification from clinical and pathological reports. The biological-significant features with ranking technique prioritize influential features are used to identify grades G. Subsequently, histopathological images are used by DL models for grade classification and compared with ML models. Instead of employing a single ML model, the GradeDiff-IM model uses the stack-ensembled approach to improve the grade G classification performance. The maximum accuracy is attained by stacking G1-98.2, G2-97.6, and G3-97.5. The proposed study shows that the ML ensemble model is more accurate than the DL models. As a result, the proposed model achieved higher accuracy for G by implementing the stacking technique than the other state-of-the-art models.","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962164","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}

引用次数: 0

DAU-Net: a novel U-Net with dual attention for retinal vessel segmentation.

IF 1.3

Biomedical Physics & Engineering Express Pub Date : 2025-01-22 DOI: 10.1088/2057-1976/ada9f0

Muwei Jian, Wenjing Xu, ChangQun Nie, Shuo Li, Songwen Yang, Xiaoguang Li

{"title":"DAU-Net: a novel U-Net with dual attention for retinal vessel segmentation.","authors":"Muwei Jian, Wenjing Xu, ChangQun Nie, Shuo Li, Songwen Yang, Xiaoguang Li","doi":"10.1088/2057-1976/ada9f0","DOIUrl":"https://doi.org/10.1088/2057-1976/ada9f0","url":null,"abstract":"In fundus images, precisely segmenting retinal blood vessels is important for diagnosing eye-related conditions, such as diabetic retinopathy and hypertensive retinopathy or other eye-related disorders. In this work, we propose an enhanced U-shaped network with dual-attention, named DAU-Net, divided into encoder and decoder parts. Wherein, we replace the traditional convolutional layers with ConvNeXt Block and SnakeConv Block to strengthen its recognition ability for different forms of blood vessels while lightweight the model. Additionally, we designed two efficient attention modules, namely Local-Global Attention (LGA) and Cross-Fusion Attention (CFA). Specifically, LGA conducts attention calculations on the features extracted by the encoder to accentuate vessel-related characteristics while suppressing irrelevant background information; CFA addresses potential information loss during feature extraction by globally modeling pixel interactions between encoder and decoder features. Comprehensive experiments in terms of public datasets DRIVE, CHASE_DB1, and STARE demonstrate that DAU-Net obtains excellent segmentation results on all three datasets. The results show an AUC of 0.9818, ACC of 0.8299, and F1 score of 0.9585 on DRIVE; 0.9894, 0.8499, and 0.9700 on CHASE_DB1; and 0.9908, 0.8620, and 0.9712 on STARE, respectively. These results strongly demonstrate the effectiveness of DAU-Net in retinal vessel segmentation, highlighting its potential for practical clinical use.","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143021732","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}

引用次数: 0

Development of a machine learning tool to predict deep inspiration breath hold requirement for locoregional right-sided breast radiation therapy patients. 一种机器学习工具的开发，用于预测局部区域右侧乳房放射治疗患者的深吸气屏气需求。

IF 1.3

Biomedical Physics & Engineering Express Pub Date : 2025-01-22 DOI: 10.1088/2057-1976/ad9b30

Fletcher Barrett, Sarah Quirk, Kailyn Stenhouse, Karen Long, Michael Roumeliotis, Sangjune Lee, Roberto Souza, Philip McGeachy

{"title":"Development of a machine learning tool to predict deep inspiration breath hold requirement for locoregional right-sided breast radiation therapy patients.","authors":"Fletcher Barrett, Sarah Quirk, Kailyn Stenhouse, Karen Long, Michael Roumeliotis, Sangjune Lee, Roberto Souza, Philip McGeachy","doi":"10.1088/2057-1976/ad9b30","DOIUrl":"10.1088/2057-1976/ad9b30","url":null,"abstract":"Background and purpose. This study presents machine learning (ML) models that predict if deep inspiration breath hold (DIBH) is needed based on lung dose in right-sided breast cancer patients during the initial computed tomography (CT) appointment.Materials and methods. Anatomic distances were extracted from a single-institution dataset of free breathing (FB) CT scans from locoregional right-sided breast cancer patients. Models were developed using combinations of anatomic distances and ML classification algorithms (gradient boosting, k-nearest neighbors, logistic regression, random forest, and support vector machine) and optimized over 100 iterations using stratified 5-fold cross-validation. Models were grouped by the number of anatomic distances used during development; those with the highest validation accuracy were selected as final models. Final models were compared based on their predictive ability, measurement collection efficiency, and robustness to simulated user error during measurement collection.Results. This retrospective study included 238 patients treated between 2016 and 2021. Model development ended once eight anatomic distances were included, and the validation accuracy plateaued. The best performing model used logistic regression with four anatomic distances achieving 80.5% average testing accuracy, with minimal false negatives and positives (<27%). The anatomic distances required for prediction were collected within 3 min and were robust to simulated user error during measurement collection, changing accuracy by <5%.Conclusion. Our logistic regression model using four anatomic distances provided the best balance between efficiency, robustness, and ability to predict if DIBH was needed for locoregional right-sided breast cancer patients.","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789595","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}

引用次数: 0

Nyquist ghost elimination for diffusion MRI by dual-polarity readout at low b-values. 低b值双极性读出弥散MRI奈奎斯特鬼影消除。

IF 1.3

Biomedical Physics & Engineering Express Pub Date : 2025-01-21 DOI: 10.1088/2057-1976/ada8b0

Oscar Jalnefjord, Nicolas Geades, Guillaume Gilbert, Isabella M Björkman-Burtscher, Maria Ljungberg

引用次数: 0

Systematic application of saliency maps to explain the decisions of convolutional neural networks for glaucoma diagnosis based on disc and cup geometry. 系统应用显著性图来解释卷积神经网络在青光眼诊断中的决策。

IF 1.3

Biomedical Physics & Engineering Express Pub Date : 2025-01-21 DOI: 10.1088/2057-1976/ada8ad

Francisco Fumero, Jose Sigut, José Estévez, Tinguaro Díaz-Alemán

{"title":"Systematic application of saliency maps to explain the decisions of convolutional neural networks for glaucoma diagnosis based on disc and cup geometry.","authors":"Francisco Fumero, Jose Sigut, José Estévez, Tinguaro Díaz-Alemán","doi":"10.1088/2057-1976/ada8ad","DOIUrl":"10.1088/2057-1976/ada8ad","url":null,"abstract":"This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simplified eye fundus images that contain only disc and cup outlines. These simplified images, a methodological novelty, were used to relate features highlighted in the saliency maps to the geometrical clues that experts consider in glaucoma diagnosis. Despite their simplicity, these images retained sufficient information for accurate classification, with balanced accuracies ranging from 0.8331 to 0.8890, compared to 0.8090 to 0.9203 for networks trained on the original images. The study used a dataset of 606 images, along with RIM-ONE DL and REFUGE datasets, and explored nine saliency methods. A discretization algorithm was applied to reduce noise and compute normalized attribution values for standard eye fundus sectors. Consistent with other medical imaging studies, significant variability was found in the attribution maps, influenced by the method, model, or architecture, and often deviating from typical sectors experts examine. However, globally, the results were relatively stable, with a strong correlation of 0.9289 (p < 0.001) between relevant sectors in our dataset and RIM-ONE DL, and 0.7806 (p < 0.001) for REFUGE. The findings suggest caution when using saliency methods in critical fields like medicine. These methods may be more suitable for broad image relevance interpretation rather than assessing individual cases, where results are highly sensitive to methodological choices. Moreover, the regions identified by the networks do not consistently align with established medical criteria for disease severity.","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962167","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}

引用次数: 0