Biomedical Physics & Engineering Express最新文献

{"title":"Rational design of alternative natural-based coupling media for diagnostic ultrasound imaging: a review.","authors":"Dennis Nimoh, Isaac Acquah, Elizabeth Wordui","doi":"10.1088/2057-1976/add7e2","DOIUrl":"10.1088/2057-1976/add7e2","url":null,"abstract":"<p><p>Ultrasound imaging is an indispensable diagnostic and screening tool in healthcare, renowned for its non-invasive nature, real-time visualization, and use of non-ionizing radiation. It plays a vital role in obstetrics and gynaecology by significantly reducing maternal mortality and enhancing patient care. In addition to its use in obstetrics, ultrasound is used to guide biopsies and to evaluate various diseases such as liver cirrhosis, thyroid disorders, and kidney stones. Point-of-care ultrasonography has proven to be increasingly beneficial in low-resource settings. However, the availability and cost of commercial ultrasound gels pose significant challenges. Alternative natural-based gels formulated from locally sourced materials have emerged as viable substitutes. This review critically examines alternative natural-based ultrasound gels, focusing on their physicochemical properties, formulation procedures, and the limitations associated with their use in diagnostic imaging. Furthermore, it presents a rational design approach that methodically selects the ingredients based on their properties and interactions to formulate these gels for imaging applications. This offers a promising pathway for indigenous manufacturers to develop gels that meet ideal performance criteria, ensuring better imaging outcomes and wider acceptability in clinical practice.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061923","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":"Fully customizable bronze-PLA lung shields using 3D printing for total body irradiation (TBI).","authors":"Ethan D Stolen, Tianming Wu, Joseph B Schulz, Lunya Allie, Caleb Song, Byoung Hyuck Kim, James J Sohn","doi":"10.1088/2057-1976/add898","DOIUrl":"10.1088/2057-1976/add898","url":null,"abstract":"<p><p><i>Purpose</i>. Total body irradiation (TBI) is a critical component in the treatment of hematological malignancies, but it carries a risk of radiation-induced lung damage. Traditional lung shields, typically made of lead or Cerrobend, are used to protect lung tissue during TBI. However, these shields require manual fabrication, present toxicity concerns, and are difficult to reproduce in the precise geometry needed for each individual patient. This study investigates the feasibility of 3D-printed bronze-polylactic acid (PLA) lung shields as a non-toxic, customizable alternative to conventional shields in TBI procedures.<i>Materials and Methods</i>. Bronze-PLA lung shields were 3D-printed using a PLA filament with 60% bronze powder by weight. Two thicknesses of 1.8 cm and 3.3 cm were tested. Radiation transmission was measured using a Varian TrueBeam linear accelerator (6 MV and 15 MV photon beams) at isocenter (90 cm source-to-axis distance, SAD) and extended field setup (430 cm source-to-surface distance, SSD). Measurements were repeated with a conventional wax-lead shield for comparison.<i>Results</i>. At 100 cm SAD and 6 MV, the 1.8 cm bronze-PLA shield transmitted 92.12% (standard error, SE = 0.03%), while the thicker (3.3 cm) bronze-PLA shield reduced transmission to 77.08% (SE = 0.02%). Under similar conditions, the wax-lead shield transmitted 86.43% (SE = 0.13%). For 15 MV beams, both the wax-lead and bronze-PLA shields exhibited higher transmission, with the 3.3 cm bronze-PLA shield providing improved attenuation relative to the thinner shield. At the extended SAD, the thinner bronze-PLA shield transmitted more than the wax-lead block, but increasing thickness consistently improved attenuation.<i>Conclusion</i>. The 3D-printed bronze-PLA lung shields demonstrate promising potential in TBI procedures, offering advantages in customization, reduced toxicity, and improved workflow efficiency. While differences in transmission characteristics were observed, particularly at higher energies and extended SDD, these findings provide a foundation for further optimization of 3D-printed shielding for TBI protocols.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144075607","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":"Design and validation of a minibeam treatment delivery system for use with a radiation therapy research platform.","authors":"Steven Herchko, Sridhar Yaddanapudi, C-K Chris Wang","doi":"10.1088/2057-1976/adcf2c","DOIUrl":"10.1088/2057-1976/adcf2c","url":null,"abstract":"<p><p>Minibeam radiation therapy (MBRT) is a promising treatment technique in the early stages of clinical use. In this work, two hexagonal minibeam collimators were designed, fabricated, and validated using film measurements and Monte Carlo simulations. Film and Monte Carlo results demonstrated strong agreement, with the greatest agreement near the beam central axis. One of the previously validated MBRT collimators was then modified to allow for dose calculations on a mouse cone beam computed tomography (CBCT) dataset, and a MBRT dose distribution was preserved in the animal dataset. This validated system can be used in future cell and small animal studies to further explore MBRT in a preclinical setting.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143961117","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":"Evaluating machine- and deep learning approaches for artifact detection in infant EEG: classifier performance, certainty, and training size effects.","authors":"R Kemmerich, A Wienke, U Frischen, B Mathes","doi":"10.1088/2057-1976/add740","DOIUrl":"10.1088/2057-1976/add740","url":null,"abstract":"<p><p>Electroencephalography (EEG) is essential for studying infant brain activity but is highly susceptible to artifacts due to infants' movements and physiological variability. Manual artifact detection is labor-intensive and subjective, underscoring the need for automated methods. This study evaluates the performance of three machine learning classifiers - Random Forest (RF), Support Vector Machine (SVM), and a deep learning (DL) model - in detecting artifacts in infant EEG data without prior feature extraction. EEG data were collected from 294 infants (mean age 8.34 months) as part of the Bremen Initiative to Foster Early Childhood Development (BRISE). After preprocessing and manual annotation by an expert, a total of 66,851 epochs were analyzed, with 45% labeled as artifacts. The classifiers were trained on filtered EEG data without further feature extraction to directly handle the complex and noisy signals characteristic of infant EEG. Results. indicated that both the RF classifier and the DL model achieved high balanced accuracy scores (.873 and .881, respectively), substantially outperforming the SVM (.756). Further analysis showed that increasing classifier certainty improved accuracy but reduced the amount of data classified, offering a trade-off between precision and data coverage. Additionally, the RF classifier outperformed the DL model with smaller training datasets, while the DL model required larger datasets to achieve optimal performance. These findings demonstrate that RF and DL classifiers can effectively automate artifact detection in infant EEG data, reducing preprocessing time and enhancing consistency across studies. Implementing such automated methods could facilitate the inclusion of EEG in large-scale developmental research and improve reproducibility by standardizing preprocessing pipelines.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962694","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":"The proton therapy research beamline at the Christie NHS foundation trust.","authors":"Nicholas Thomas Henthorn, John-William Warmenhoven, Samuel Peter Ingram, Samuel Paul Manger, Michael John Merchant, Karen Joy Kirkby, Ranald I Mackay, Michael J Taylor","doi":"10.1088/2057-1976/addbe8","DOIUrl":"https://doi.org/10.1088/2057-1976/addbe8","url":null,"abstract":"<p><p>Proton therapy is a relatively new modality for cancer treatment and has several open research questions, particularly in the biological realm. Due to large infrastructure costs the modality is reserved for specialist treatment, limiting the patient outcome dataset. This requires supplementation with fundamental research through in vitro and in vivo systems. Similarly, the safety and potential benefits of new treatments, such as FLASH, should be demonstrated in lab environments prior to clinical translation. Greater access to clinically relevant research platforms is required. This work presents the capabilities of the Manchester proton therapy research facility for experimentalists' assessment to meet their research goals. &#xD;Details of the research beamline geometry are presented, along with workflows for in vitro sample irradiation within an automated sample handling environmental chamber. Absolute dose and dose depth of the proton research beamline was measured. The dose calibration across a range of energies and dose rates is presented and fits are mathematically described. Methods to convert measured, or planned, dose to sample dose are presented including for biological studies investigating end of proton range effects. Elements of the beam optics, impacting on spot size and therefore field homogeneity, were measured for sample irradiation and beam model development. A Monte Carlo beam model was established to predict physically difficult measurements and is compared to measurements throughout. Achievable dose rates for FLASH are presented alongside absolute dosimetric accuracy.&#xD;There was a focus on radiobiological research in establishing the beamline. Special care was taken to develop high-throughput repeatable in vitro irradiation workflows, with an adjacent radiobiological lab for immediate processing. This will lead to a reduction in experimental uncertainties seen in the literature with demonstrated accurate dosimetry, tight environmental control, and a high degree of versatility. The infrastructure presented in this work is a unique facility in the UK.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126395","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":"Development and evaluation of a bayesian optimization FDG population-based input function for implementing parametric imaging in the clinical practice.","authors":"Alessia Artesani, Lorenzo Leonardi, Jelena Jandric, Lorenzo Muraglia, Charalampos Tsoumpas, Marcello Rodari, Laura Evangelista","doi":"10.1088/2057-1976/add73e","DOIUrl":"10.1088/2057-1976/add73e","url":null,"abstract":"<p><p><i>Aim.</i>Parametric imaging from dynamic positron emission tomography (PET) has gained interest for tumour diagnostics and treatment response evaluation. However, the lack of a standardized method for generating the<i>input function</i>-reference curve for kinetic modelling-has led to inconsistent descriptors, contributing to uncertainties in parametric imaging reliability. This study aims to address this challenge by proposing a hyperparametric optimization method for deriving FDG population-based input function (PBIF), independent of acquisition and injection protocols.<i>Method</i>. This study included ten patients undergoing FDG PET scans using a standard axial field of view scanner. Image-derived input functions (IDIF) were extracted from the descending aorta, normalized, and utilized as input for PBIF modelling. Bayesian hyperparameter optimization was employed to estimate global optima for ten parameters that describe the input function through independent runs of up to 600 iterations each. The injection profile was integrated as a double rectangular profile, representing both the tracer injection and the saline flush tracer residual.<i>Results</i>. The Bayesian optimization successfully modelled patient-specific IDIFs (R² = 0.97). The algorithm estimated injection and flush durations in agreement with recorded values. Parameter distributions showed low variability, with median amplitude and time constant values varying by around 15%. The glucose-affine molecule dynamics were characterized by distinct time constants of 6 s, 4 min, and 70 min. Analytical and numerical comparisons of parametric imaging from IDIF and PBIF show that Patlak analysis is unaffected by the injection characteristics.<i>Conclusion</i>. The study highlights the benefits of Bayesian optimization for modelling PBIF without prior knowledge of injection characteristics. These findings support the existence of unified FDG PBIF, facilitating the utilization of parametric imaging across PET centres. Although the present study is based on a limited, single-centre cohort, this methodological development is intended as a foundational study to further multi-centre validation on larger population.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970804","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":"Comment on 'Determination of calibration reference values <1 cGy MU^-1as detailed in the IPEM 2020 high-energy photon code of practice' by Clarke<i>et al</i>[2025<i>Biomed. Phys. Eng. Express</i>,<b>11</b>025046].","authors":"David J Eaton","doi":"10.1088/2057-1976/add6ab","DOIUrl":"https://doi.org/10.1088/2057-1976/add6ab","url":null,"abstract":"","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118732","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":"MRI-based diagnostic model for Alzheimer's disease using 3D-ResNet.","authors":"Dongkui Chen, Hong Yang, Hao Li, Xuanlong He, Hongbo Mu","doi":"10.1088/2057-1976/add73d","DOIUrl":"10.1088/2057-1976/add73d","url":null,"abstract":"<p><p>Alzheimer's disease (AD), a progressive neurodegenerative disorder, is the leading cause of dementia worldwide and remains incurable once it begins. Therefore, early and accurate diagnosis is essential for effective intervention. Leveraging recent advances in deep learning, this study proposes a novel diagnostic model based on the 3D-ResNet architecture to classify three cognitive states: AD, mild cognitive impairment (MCI), and cognitively normal (CN) individuals, using MRI data. The model integrates the strengths of ResNet and 3D convolutional neural networks (3D-CNN), and incorporates a special attention mechanism(SAM) within the residual structure to enhance feature representation. The study utilized the ADNI dataset, comprising 800 brain MRI scans. The dataset was split in a 7:3 ratio for training and testing, and the network was trained using data augmentation and cross-validation strategies. The proposed model achieved 92.33% accuracy in the three-class classification task, and 97.61%, 95.83%, and 93.42% accuracy in binary classifications of AD versus CN, AD versus MCI, and CN versus MCI, respectively, outperforming existing state-of-the-art methods. Furthermore, Grad-CAM heatmaps and 3D MRI reconstructions revealed that the cerebral cortex and hippocampus are critical regions for AD classification. These findings demonstrate a robust and interpretable AI-based diagnostic framework for AD, providing valuable technical support for its timely detection and clinical intervention.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143973433","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":"Multi-omic feature reliability of deformable image registration-based images.","authors":"Owen Paetkau, Ekaterina Tchistiakova, Charles Kirkby","doi":"10.1088/2057-1976/add73f","DOIUrl":"10.1088/2057-1976/add73f","url":null,"abstract":"<p><p><i>Purpose</i>. To evaluate the reliability of radiomic and dosiomic (multi-omic) features extracted from synthetic CT images generated using two commercially available deformable image registration workflows.<i>Materials and Methods</i>. Multi-omic features were extracted from organs at risk (OAR) contoured on a cohort of 58 head and neck (HN) radiotherapy patients. The contours were propagated from the planning CT to synthetic CTs of the final fraction cone-beam CT (CBCT) anatomy using MIM and Velocity deformable image registration workflows. The workflows were validated using radiation oncologist contours on the planning CT and final fraction CBCT according to TG-132 guidelines. The OAR volumes and mean dose on the synthetic CTs from two workflows were compared using a signed Wilcoxon rank test. In addition, the dose distributions were evaluated using a gamma analysis using clinical criteria. The multi-omic features were extracted using region-of-interest extraction on the OAR with the original and wavelet filters. The feature reliability was evaluated for four OAR: spinal cord, parotid glands, submandibular glands, and pharyngeal constrictors. The reliability was evaluated using the intraclass correlation coefficient (ICC) with features exceeding 0.75 considered moderately reliable.<i>Results</i>. The volume and mean OAR dose were found to be statistically similar between the MIM and Velocity synthetic CT workflows. In addition, the gamma analysis resulted in 83% of plans exceeding 95% gamma passing rate at 3%/3 mm criteria. Across all HN OAR multi-omic features, fewer radiomic features (21%) were found to be moderately reliable compared to dosiomic features (59%) between the two synthetic CT workflows. The HN OAR with the most moderately reliable features was the spinal cord (46% radiomic, 85% dosiomic).<i>Conclusion</i>. Radiomics features presented worse reliability compared to dosiomic features across different synthetic CT deformable image registration workflows. Care should be taken when implementing predictive models using features extracted from different synthetic CT workflows.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143959348","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":"Feasibility study of a general model for synthetic CT generation in MRI-guided extracranial radiotherapy.","authors":"Shu-Hui Hsu, Zhaohui Han, Yue-Houng Hu, Dianne Ferguson, Ritchell van Dams, Raymond H Mak, Jonathan E Leeman, Atchar Sudhyadhom","doi":"10.1088/2057-1976/add26b","DOIUrl":"10.1088/2057-1976/add26b","url":null,"abstract":"<p><p>This study aims to investigate the feasibility of a single general model to synthesize CT images across body sites, thorax, abdomen, and pelvis, to support treatment planning for MRI-only radiotherapy. A total of 157 patients who received MRI-guided radiation therapy in the thorax, abdomen, and pelvis on a 0.35T MRIdian Linac were included. A subset of 122 cases were used for model training and the remaining 35 cases were used for model validation. All patient datasets had semi-paired CT-simulation image and 0.35T MR image acquired using TrueFISP. A conditional generative adversarial network with a multi-planar method was used to generate synthetic CT images from 0.35T MR images. The effect of preprocessing methods (with and without bias field corrections) on the quality of synthetic CT was evaluated and found to be insignificant. The general models trained on all cases performed comparably to the site-specific models trained on individual body sites. For all models, the peak signal-to-noise ratios ranged from 31.7 to 34.9 and the structural index similarity measures ranged from 0.9547 to 0.9758. For the datasets with bias field corrections, the mean-absolute-errors in HU (general model versus site-specific model) were 49.7 ± 9.4 versus 49.5 ± 8.9, 48.7 ± 7.6 versus 43 ± 7.8 and 32.8 ± 5.5 versus 31.8 ± 5.3 for the thorax, abdomen, and pelvis, respectively. When comparing plans between synthetic CTs and ground truth CTs, the dosimetric difference was on average less than 0.5% (0.2 Gy) for target coverage and less than 2.1% (0.4 Gy) for organ-at-risk metrics for all body sites with either the general or specific models. Synthetic CT plans showed good agreement with mean gamma pass rates of >94% and >99% for 1%/1 mm and 2%/2 mm, respectively. This study has demonstrated the feasibility of using a general model for multiple body sites and the potential of using synthetic CT to support an MRI-guided radiotherapy workflow.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969195","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}