Biomedical Physics & Engineering Express最新文献

{"title":"Design and characterization of practical small-field collimators for radiotherapy calibration unit at a secondary standard dosimetry laboratory.","authors":"Toufik Medjadj, Mehenna Arib, Silia Hayoune","doi":"10.1088/2057-1976/add34f","DOIUrl":"https://doi.org/10.1088/2057-1976/add34f","url":null,"abstract":"<p><p>We aimed to design small fields collimators made of lead blocks, with diameters of 40 mm, 30 mm, 20 mm, and 10 mm, for the calibration of small volume radiotherapy ionization chambers using the Algerian Secondary Standard Dosimetry Laboratory (SSDL) ELDORADO 78⁶⁰Co calibration unit. The radiation properties of the designed collimators were assessed using GAFChromic EBT3 dosimetry film (Ashland, USA) and two PTW small volume ionization chambers (PTW, Freiburg, Germany). Additionally, the photons spectra for the four different fields were also investigated using Monte Carlo (MC) method. The MC method was also utilized to define the dimensions of the collimators that achieve the desired field sizes at the reference distance of 80 cm. We simulated the photon beam of the⁶⁰Co source from the irradiation unit to estimate the influencing quantities including field size and beam attenuation. EBT3 film, PTW Pinpoint and PTW Semiflex ionization chambers were used to measure the beam profiles, the output factors (OF), and the percentage depth doses (PDD) for the designed collimators. These measurements were performed at a depth of 5 cm and a source-to-surface distance of 80 cm. The beam profiles simulated with MC method and experimentally measured were found to be agreed within 3%/1 mm gamma criteria. The ionization chambers underestimate the OFs in comparison to EBT3 film for the four field sizes. The PDD curves were measured by the Pinpoint chamber and calculated using MC simulation. Significant differences were noted between the curves of the 20 and 10 mm collimators compared to those of the 40 mm and 30 mm ones. The curves were in agreement with the relative simulated data. Custom small-field collimators were successfully produced and characterized, which will facilitate studies on the variation and stability of calibration coefficients for small volume detectors operating within small-fields at our SSDL.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960567","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":"An automated cascade framework for glioma prognosis via segmentation, multi-feature fusion and classification techniques.","authors":"Meriem Hamoud, Nour El Islem Chekima, Abdelkader Hima, Nedjoua Houda Kholladi","doi":"10.1088/2057-1976/add26c","DOIUrl":"https://doi.org/10.1088/2057-1976/add26c","url":null,"abstract":"<p><p>Glioma is one of the most lethal types of brain tumors, accounting for approximately 33% of all diagnosed brain tumor cases. Accurate segmentation and classification are crucial for precise glioma characterization, emphasizing early detection of malignancy, effective treatment planning, and prevention of tumor progression. Magnetic Resonance Imaging (MRI) serves as a non-invasive imaging modality that allows detailed examination of gliomas without exposure to ionizing radiation. However, manual analysis of MRI scans is impractical, time-consuming, subjective, and requires specialized expertise from radiologists. To address this, computer-aided diagnosis (CAD) systems have greatly evolved as powerful tools to support neuro-oncologists in the brain cancer screening process. In this work, we present a glioma classification framework based on 3D multi-modal MRI segmentation using the CNN models SegResNet and Swin UNETR which incorporates transformer mechanisms for enhancing segmentation performance. MRI images undergo preprocessing with a Gaussian filter and skull stripping to improve tissue localization. Key textural features are then extracted from segmented tumor regions using Gabor Transform, Discrete Wavelet Transform (DWT), and deep features from ResNet50. These features are fused, normalized, and classified using a Support Vector Machine (SVM) to distinguish between Low-Grade Glioma (LGG) and High-Grade Glioma (HGG). Extensive experiments on benchmark datasets, including BRATS2020 and BRATS2023, demonstrate the effectiveness of the proposed approach. Our model achieved Dice scores of 0.815 for Tumor Core, 0.909 for Whole Tumor, and 0.829 for Enhancing Tumor. Concerning classification, the framework attained 97% accuracy, 94% precision, 96% recall, and a 95% F1-score. These results highlight the potential of the proposed framework to provide reliable support for radiologists in the early detection and classification of gliomas.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143973369","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 Systematic Review on Physiology-based Anxiety Detection using Machine Learning.","authors":"Shikha Shikha, Divyashikha Sethia, S Indu","doi":"10.1088/2057-1976/add5fc","DOIUrl":"https://doi.org/10.1088/2057-1976/add5fc","url":null,"abstract":"<p><p>Anxiety disorder poses a significant challenge to mental health. Diagnosing anxiety is complicated due to its various symptoms and factors, often resulting in extended periods of untreated patient suffering. As a result, patients often endure prolonged periods without treatment. This scenario has prompted researchers to step into the domain of non-invasive physiological signals, including electroencephalography, electrocardiogram, electromyography, electrodermal activity, and respiration. By integrating machine learning into the physiological signals, clinicians can identify distinct anxiety patterns and effectively differentiate between individuals with the disorder and those in good health. This paper presents a systematic literature review of physiological sensors and machine learning methods to diagnose and predict anxiety disorder. It also presents an overview of wearable devices employed in previous studies. A key contribution of this review is the exploration of the relationship between physiological features and anxiety disorders through machine learning models. The paper discusses methodologies, open datasets, and identifies research gaps and challenges related to the machine learning-based analysis of physiological signals for anxiety detection. Furthermore, a novel multimodal approach for anxiety classification is proposed, utilizing a combination of physiological signals. This review aims to provide a comprehensive understanding of the current trends, architectures, and techniques employed in the field of anxiety detection.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143968327","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 geometric calibration method for a multi-segment static CT based on ordered subsets of sources and detectors.","authors":"Jing Li, Changyu Chen, Yuxiang Xing, Zhiqiang Chen","doi":"10.1088/2057-1976/adce0f","DOIUrl":"https://doi.org/10.1088/2057-1976/adce0f","url":null,"abstract":"<p><p>Multi-segment static computed tomography (MS-staticCT) is a generalized and efficient configuration of static CT systems, achieving high temporal resolution imaging by sequentially firing x-ray sources, instead of rotation. However, it contains numerous geometric parameters. Due to the dense arrangement of both the x-ray sources and detectors within their respective configurations, there are some coupled illumination relationships where some x-ray sources simultaneously illuminate multiple detectors. To address these calibration challenges, we propose a geometric calibration method based on ordered subsets. We categorize two types of ordered subsets of sources and detectors: source subsets and detector subsets. Each source subset includes a group of sources that illuminate the same detectors, along with the illuminated detectors. Similarly, each detector subset includes a group of detectors illuminated by the same sources, along with the sources that illuminate them. The calibration of the sources in source subsets and the detectors in detector subsets is performed alternately until convergence, ensuring that the calibrated geometry to accurately describe all the illumination relationships. These calibration steps are detailed in a workflow. During each step, the estimations for different ordered subsets are independent and parallelizable to significantly improving computational efficiency. A calibration phantom is involved in our method. During the calibration, we iteratively estimate the parameters by minimizing the average re-projection error (aRPE) of the balls in the calibration phantom. We evaluated the proposed method by simulation and actual experiments. The aRPE was reduced to 0.0087 mm and the reconstructed images were clear without obvious misalignment in simulation. Compared to estimating all parameters together, our method improved computational efficiency by a factor of 2.20. The targeted spatial resolution (2.5 lp·mm^-1) of an actual MS-staticCT system was obtained. These results verified the efficiency and accuracy of the proposed method.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958556","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":"Optimization of signal and noise in x-ray phase and dark field imaging with a wire mesh.","authors":"Uttam Pyakurel, Arthur W Redgate, Carolyn A MacDonald, Jonathan C Petruccelli","doi":"10.1088/2057-1976/adcd7d","DOIUrl":"https://doi.org/10.1088/2057-1976/adcd7d","url":null,"abstract":"<p><p>Phase differences imparted by tissue are significantly larger than attenuation differences. In addition, small angle scatter from tissue microstructure can provide a dark field signal that is complementary to attenuation and phase. Unfortunately, the low spatial coherence of clinical sources reduces phase and dark field contrast. Our method structures the beam with a single low-cost wire mesh that does not need precise alignment and relaxes the coherence requirement on the source. In addition, focusing polycapillary optics, which can be permanently attached to sources, are employed to allow for the use of high-power primary sources by increasing the phase signal after the focus. However, the coarseness of the mesh reduces the phase and dark field signal-to-noise ratio (SNR) compared with grating-based techniques, so optimization of the phase and dark-field SNR is an important consideration. Here, we consider the impact on the SNR of the distances between the mesh and the source and detector, and of x-ray tube voltages, to optimize the system.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143952912","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":"Feature selection for classification based on machine learning algorithms for prostate cancer.","authors":"Swathypriyadharsini P, Rupashini P R, Premalatha K","doi":"10.1088/2057-1976/adcf2b","DOIUrl":"https://doi.org/10.1088/2057-1976/adcf2b","url":null,"abstract":"<p><p>Microarray technology has transformed the biotechnological research to next level in the recent years. It provides the expression levels of various genes involved in a particular disease. Prostate cancer disease turned into life threatening cancer. The genes causing this disease are identified through the classification methods. These gene expression data have problems like high dimensional with low sample size which imposes active challenges in the existing classification algorithms. Feature selection techniques are applied in order to address the dimensionality issues. . This paper aims in analyzing the feature selection methods for classification of gene expression data of Prostate and identify the significant genes that have a major influence on the disease. The three different feature selection methods such as Filters, wrappers and embedded selectors are applied before the classification process for selecting the top ranked genes. Then, the extracted top ranked genes are applied on the classification algorithms such as SVM, k-NN, Random Forest and Artificial Neural Network. After the inclusion of feature selection technique, the classification accuracy is significantly boosted even with less number of genes. Random Forest classification algorithm outperforms other classification methods. The significant genes that has the major influence in prostate cancer disease are identified such as KLK3, GFI1, CXCR2 and TNFRSF10C.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143964565","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 survey of dosimetry quality assurance practice at UK small animal radiation research platform (SARRP) facilities.","authors":"Emma R Biglin, Ileana Silvestre Patallo, Anna Morris, Rebecca Carter, Emer Curley, Mihaela Ghita-Pettigrew, Mark Hill, Thierry L Lefebvre, David Lewis, Simeon Nill, Giuseppe Schettino, Katrina Stevenson, Adam H Aitkenhead","doi":"10.1088/2057-1976/adcc35","DOIUrl":"https://doi.org/10.1088/2057-1976/adcc35","url":null,"abstract":"<p><p><i>Introduction</i>: Improvements in preclinical radiation research have been made to better mimic the equipment and techniques implemented in the clinic. The development of dedicated small animal radiation units facilitates such advances by combining treatment planning, image guidance and conformal delivery. One area significantly behind its clinical equivalent are standardised dosimetry quality assurance (QA) protocols, hampering the translatability of results into the development of clinical interventions.<i>Approach</i>: The aim of the study described herein was to summarise the current QA procedures implemented at several institutions on Small Animal Radiation Research Platforms (SARRPs), the system used by the six institutions surveyed, and to determine the barriers to implementing a standard dosimetry protocol. Participants at UK research institutions were invited to complete a questionnaire to ascertain their current preclinical QA practice.<i>Main results</i>: All participants involved undertake regular dose output measurements and most perform image guidance QA measurements. Consistency in QA procedures differed when more complex plan verification or end-to-end testing was discussed.<i>Significance</i>: This survey demonstrates that, although improvements are being made in the awareness of the importance of regular dosimetry tests, there is still a way to go to standardise the procedures with regards to more complex verifications. Incorporating robust QA procedures and strict dose constraints would ensure the reliability and ethical integrity of experiments involving small animals. This approach not only protects the welfare of the animals but also enhances the quality and reproducibility of the preclinical results.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960425","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":"Evaluation of the utility of quantitative assessment using standardized uptake value in myocardial¹²³I-MIBG scintigraphy: a phantom study on the influence of lung and liver uptake.","authors":"Yoshiaki Yasumoto, Hiromitsu Daisaki, Mitsuru Sato","doi":"10.1088/2057-1976/adccc9","DOIUrl":"https://doi.org/10.1088/2057-1976/adccc9","url":null,"abstract":"<p><p>In this study, we aimed to clarify the utility of the myocardial standardized uptake value (SUV) method in myocardial¹²³I-metaiodobenzylguanidine (MIBG) scintigraphy by evaluating the impact of lung and liver uptake changes on both the heart-to-mediastinum (H/M) and myocardial SUV. The Monte Carlo simulation code simulating medical imaging nuclear detectors was used to simulate the imaging of a myocardial phantom HL under the assumption of normal uptake in myocardial MIBG scintigraphy. In addition, imaging of multiple myocardial phantom HLs with different lung and liver uptake concentrations was simulated. Quantitative values for the myocardial SUV and H/M ratio were calculated from the simulated data of the digital myocardial phantom HL. The effects of lung and liver uptake variation were evaluated by assessing the relative standard deviation (RSD) of each calculated value. For lung uptake variations, the RSD of the myocardial SUV method was 1.34% for the maximum count and 1.64% for the mean count, whereas the RSD of the H/M ratio was 4.73%. For variations in liver uptake, the RSD of the myocardial SUV method was 1.73% for the maximum count and 0.72% for the mean count, with the RSD of the H/M ratio being 0.02%. After removing the segments clearly influenced by liver uptake, the RSD of the myocardial SUV method was 0.007% for the maximum value and 0.004% for the mean value, which were both smaller than the RSD of the H/M ratio. These findings indicate that the myocardial SUV method allows for a quantitative evaluation that is less influenced by lung and liver uptake compared with the H/M ratio, thereby enabling a more stable assessment of myocardial uptake.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143965550","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":"Physical contributors to dose in patients with dual-port temporary tissue expanders treated post-mastectomy with 10 MV x-rays: a Monte Carlo study.","authors":"Ramon Ortiz, Bruce Faddegon, Manju Sharma, José Ramos-Méndez","doi":"10.1088/2057-1976/adce10","DOIUrl":"10.1088/2057-1976/adce10","url":null,"abstract":"<p><p><i><b>Objective</b></i>. To evaluate how radiation interactions, influenced by a dual-port temporary tissue expander (TTE), impact dosimetry in post-mastectomy radiotherapy (PMRT) with 10 MV x-rays.<i><b>Approach</b></i>. The individual dose contributions from the radiation interaction processes within the patient, specifically the photoelectric effect, pair production, bremsstrahlung, and neutrons, were evaluated in a PMRT treatment involving the dual-port AlloX2 TTE using Monte Carlo simulations. The plan setup was two 10 MV tangential half-beam-blocked fields (40 Gy in fifteen fractions). Individual contributions of the different physical processes were computed using a dedicated physics list that allows to activate/deactivate each process. The yield of photoneutrons produced in TTE neodymium ports (ρ = 7.4 g/cm³) and their impact on equivalent neutron dose were computed using previously validated physics modules. The effect of the presence of the TTE was estimated by comparing results in plans with and without the TTE.<i><b>Results</b></i>. The presence of the TTE reduced the dose to the breast skin distal to the ports up to 19.3% of the prescribed dose. The contribution of the photoelectric effect and bremsstrahlung was confined to the metallic ports, accounting for 9% and 1% of the total dose. Pair production accounted for 20% of the dose deposited within the ports and contributed 2.2 Gy and 0.9 Gy to the maximum dose to the lung and heart, respectively. We found that no photoneutron was produced in the TTE, not having an effect on the equivalent neutron dose to the patient.<i><b>Significance</b></i>. This work extended the current knowledge on the impact of TTE on dose distributions, including neutron contamination, in PMRT treatments.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12128857/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reparameterization lightweight residual network for super-resolution of brain MR images.","authors":"Yang Geng, Pingping Wang, Jinyu Cong, Xiang Li, Kunmeng Liu, Benzheng Wei","doi":"10.1088/2057-1976/adc935","DOIUrl":"10.1088/2057-1976/adc935","url":null,"abstract":"<p><p>As the demand for high-resolution medical images increases, super-resolution (SR) technology becomes particularly important. In recent years, SR technology based on deep learning has achieved remarkable achievements, and its application in medical images is also growing. Brain magnetic resonance imaging (MRI), a critical tool for clinical diagnosis, often suffers from artifacts caused by long scanning times or motion, compromising diagnostic reliability. While deep learning-based SR methods have significantly improved, their computational complexity and resource demands hinder real-time applications in constrained environments. To address these challenges, this paper proposes a lightweight SR MRI model based on BSRN, combined with structural reparameterization, to enhance efficiency. During training, the model employs a multi-branch structure, integrating branches into a single 3 × 3 convolution in inference, significantly reducing computational complexity and storage requirements while retaining crucial feature information. Experimental results on the IXI dataset demonstrate superior performance, with notable improvements in image clarity and detail reconstruction, especially for noisy and blurred inputs. Compared to existing methods, the proposed approach balances lightweight design and performance and has good application potential, providing new ideas for future medical image processing technology development.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143787604","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}