Biomedical Physics & Engineering Express最新文献

{"title":"CT-guided CBCT multi-organ segmentation using a multi-channel conditional consistency diffusion model for lung cancer radiotherapy.","authors":"Xiaoqian Chen, Richard L J Qiu, Shaoyan Pan, Joseph W Shelton, Xiaofeng Yang, Aparna H Kesarwala","doi":"10.1088/2057-1976/addac8","DOIUrl":"10.1088/2057-1976/addac8","url":null,"abstract":"<p><p>In cone beam computed tomography (CBCT)-guided adaptive radiotherapy, rapid and precise segmentation of organs-at-risk (OARs) is essential for accurate dose verification and online replanning. The quality of CBCT images obtained with current onboard CBCT imagers and clinical imaging protocols, however, is often compromised by artifacts such as scatter and motion, particularly for thoracic CBCT scans. These artifacts not only degrade image contrast but also obscure anatomical boundaries, making accurate segmentation on CBCT images significantly more challenging compared to planning CT images. To address these persistent challenges, we propose a novel multi-channel conditional consistency diffusion model (MCCDM) for segmentation of OARs in thoracic CBCT images (CBCT-MCCDM), which harnesses its domain transfer capabilities to improve segmentation accuracy across different imaging modalities. By jointly training the MCCDM with CT images and their corresponding masks, our framework enables an end-to-end mapping learning process that generates accurate segmentation of OARs. This CBCT-MCCDM was used to delineate esophagus, heart, left and right lungs, and spinal cord on CBCT images from patients receiving radiation therapy. We quantitatively evaluated our approach by comparing model-generated contours with ground truth contours from 33 patients with lung or metastatic cancers treated with 5-fraction stereotactic body radiation therapy (SBRT), demonstrating its potential to enhance segmentation accuracy despite the presence of challenging CBCT artifacts. The proposed method was evaluated using average Dice similarity coefficients (DSC), sensitivity, specificity, 95th Percentile Hausdorff Distance (HD95), and mean surface distance (MSD) for each of the five OARs. The method achieved average DSC values of 0.82, 0.88, 0.95, 0.96, and 0.96 for the esophagus, heart, left lung, right lung, and spinal cord, respectively. Sensitivity values were 0.813, 0.922, 0.956, 0.958, and 0.929, respectively, while specificity values were 0.991, 0.994, 0.996, 0.996, and 0.995, respectively. We compared the proposed method with two state-of-art methods, CBCT-only method and U-Net, and demonstrated that the proposed CBCT-MCCDM method achieved superior performance across all metrics.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109602","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":"End-expiratory lung impedance as a tool for PEEP optimization in patients with intra-abdominal hypertension: a laparoscopic surgery model.","authors":"Fildza Sasri Peddyandhari, Andi Ade Wijaya Ramlan, Sidharta Kusuma Manggala, Achmad Kemal Harzif, Amina Nada, Theodorus Samuel Rahardja","doi":"10.1088/2057-1976/ade159","DOIUrl":"10.1088/2057-1976/ade159","url":null,"abstract":"<p><p>Increased intra-abdominal pressure (IAP) that is frequently found on patients requiring mechanical ventilation in the intensive care unit (ICU) can disrupt splanchnic perfusion and ventilation management. Elevated IAP, resulting from various factors including hemorrhage or abdominal masses, can lead to multi-organ dysfunction if not managed effectively. Interestingly, IAP is also prevalent in healthy individuals undergoing laparoscopic surgery, making it a valuable model for studying ventilation strategies applicable to critically ill patients. This study investigates the effects of varying positive end-expiratory pressure (PEEP) levels on end-expiratory lung impedance (EELI) during laparoscopic procedures, hypothesizing that alterations in PEEP can significantly influence EELI, particularly in dependent lung regions. Conducted at Cipto Mangunkusumo Hospital, this prospective cohort study included adult patients without severe pulmonary or cardiovascular conditions, assessing EELI through electrical impedance tomography (EIT). EIT was used to assess global and regional EELI changes at PEEP levels of 5, 8 11 and 14 cmH₂O following CO₂insufflation. The findings indicated that insufflation raised global EELI (ΔEELI-G) following PEEP adjustments, which contradicts expectations from increased IAP. Regional analysis highlighted that dependent lung areas exhibited more significant changes, suggesting a complex relationship between PEEP and lung mechanics during elevated IAP. Despite no adverse respiratory complications observed, obesity notably influenced EELI post-anesthesia, underscoring the necessity for tailored PEEP strategies to enhance pulmonary function in at-risk populations. This study advances understanding of optimal ventilatory management in patients with altered IAP and calls for further investigation into individualized PEEP applications and the exploration of advanced imaging modalities for lung assessment.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233053","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":"Addressing BCI inefficiency in c-VEP-based BCIs: A comprehensive study of neurophysiological predictors, binary stimulus sequences, and user comfort.","authors":"Jordy Thielen","doi":"10.1088/2057-1976/ade316","DOIUrl":"10.1088/2057-1976/ade316","url":null,"abstract":"<p><p><i>Objective.</i>This study investigated the presence of brain-computer interface (BCI) inefficiency in BCIs using the code-modulated visual evoked potential (c-VEP). It further explored neurophysiological predictors of performance variability and evaluated a wide range of binary stimulus sequences in terms of classification accuracy and user comfort, aiming to identify strategies to mitigate c-VEP BCI inefficiency.<i>Approach.</i>In a comprehensive empirical analysis, ten different binary stimulus sequences were offline evaluated. These sequences included five code families (m-sequence, de Bruijn sequence, Golay sequence, Gold code, and a Gold code set), each in original and modulated form. To identify predictors of performance variability, resting-state alpha activity, heart rate and heart rate variability, sustained attention, and flash-VEP characteristics were studied.<i>Main Results.</i>Results confirmed substantial inter-individual variability in c-VEP BCI efficiency. While all participants reached a near-perfect classification accuracy, their obtained speed varied substantially. Four flash-VEP features were found to significantly correlate with the observed performance varibility: the N2 latency, the P2 latency and amplitude, and the N3 amplitude. Among the tested stimulus conditions, the m-sequence emerged as the best-performing universal stimulus. However, tailoring stimulus selection to individuals led to significant improvements in performance. Cross-decoding was successful between modulated stimulus conditions, but showed challenges when generalizing across other stimulus conditions. Lastly, while overall comfort ratings were comparable across conditions, stimulus modulation was associated with a significant decrease in user comfort.<i>Significance.</i>This study challenges the assumption of universal efficiency in c-VEP BCIs. The findings highlight the importance of accounting for individual neurophysiological differences and underscore the need for personalized stimulus protocols and decoding strategies to enhance both performance and user comfort.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265190","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":"Test-retest reliability of kinematic and EEG low-beta spectral features in a robot-based arm movement task.","authors":"Sebastian Rueda-Parra, Russell Hardesty, Darren E Gemoets, N Jeremy Hill, Disha Gupta","doi":"10.1088/2057-1976/ade317","DOIUrl":"10.1088/2057-1976/ade317","url":null,"abstract":"<p><p><i>Objective.</i>Low-beta (L<i>β</i>, 13-20 Hz) power plays a key role in upper-limb motor control and afferent processing, making it a strong candidate for a neurophysiological biomarker. We investigate the test-retest reliability of L<i>β</i>power and kinematic features from a robotic task over extended intervals between sessions to assess its potential for tracking longitudinal changes in sensorimotor function.<i>Approach.</i>We designed and optimized a testing protocol to evaluate L<i>β</i>power and kinematic features (maximal and mean speed, reaction time, and movement duration) in ten right-handed healthy individuals that performed a planar center-out task using a robotic device and EEG for data collection. The task was performed with both hands, and the experiment was repeated approximately 40 days later under similar conditions, to resemble real-life intervention periods. We first characterized the selected features within the task context for each session, then assessed intersession agreement, the test-retest reliability (Intraclass Correlation Coefficient, ICC), and established threshold values for meaningful changes in L<i>β</i>power using Bland-Altman plots and repeatability coefficients.<i>Main Results.</i>L<i>β</i>power showed the expected contralateral reduction during movement preparation and onset. Both L<i>β</i>power and kinematic features exhibited good to excellent test-retest reliability (ICC > 0.8), displaying no significant intersession differences. Kinematic results align with prior literature, reinforcing the robustness of these measures in tracking motor performance over time. Changes in L<i>β</i>power between sessions exceeding 11.4% for right-arm and 16.5% for left-arm movements reflect meaningful intersession differences.<i>Significance.</i>This study provides evidence that L<i>β</i>power remains stable over extended intersession intervals comparable to rehabilitation timelines. The strong reliability of both L<i>β</i>power and kinematic features supports their use in monitoring upper-extremity sensorimotor function longitudinally, with L<i>β</i>power emerging as a promising biomarker for tracking therapeutic outcomes, postulating it as a reliable feature for long-term applications.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265191","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":"Permeation dynamics of organic moiety-tuned organosilica nanoparticles across porcine corneal barriers: experimental and mass transfer analysis for glaucoma drug delivery.","authors":"Yifan Sun, Hengjun Mei, Hongguang Cui, Maomao Song, Yonghui Qiao, Jian Chen, Yuan Lei","doi":"10.1088/2057-1976/ade0c3","DOIUrl":"10.1088/2057-1976/ade0c3","url":null,"abstract":"<p><p>Elevated intraocular pressure is a key pathogenic factor in glaucoma, often leading to irreversible vision loss. In previous studies, we developed a novel hollow mesoporous organosilica nanocarrier functionalized with manganese(III) tetrakis(1-methyl-4-pyridyl)porphyrin (HMMN), demonstrating superior efficacy in treating primary open-angle glaucoma. This study presents a comparative analysis of the transcorneal permeability of HMMNs modified with thioether, biphenyl, and thioether/phenylene moieties in the porcine eye. Transcorneal permeability was evaluated using fluorescence intensity measurements in the porcine aqueous humor, followed by the calculation of diffusion and apparent permeability coefficients (Papp). Results reveal that thioether-modified HMMN exhibits significantly enhanced corneal permeability compared to biphenyl and thioether/phenylene-modified variants, with a diffusion coefficient of 4.88 × 10^-6cm²s^-1and a Papp of 1.30 × 10^-5cm s^-1in the porcine eye. These findings provide valuable insights into the permeability properties of hollow mesoporous organosilica nanocarriers, positioning them for broader applications in the treatment of various ocular diseases and potentially beyond. Furthermore, this work serves as a valuable reference for future studies exploring the potential of hybridized hollow mesoporous organosilica nanoparticles functionalized with diverse organic moieties for enhanced therapeutic outcomes.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144224181","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":"Fast and accurate lung cancer subtype classication and localization based on Intraoperative frozen sections of lung adenocarcinoma.","authors":"Zhihong Chen, Yanxi Li, Chenchen Nie, Hao Cai, Yongfei Xu, Zhibo Yuan","doi":"10.1088/2057-1976/ade157","DOIUrl":"10.1088/2057-1976/ade157","url":null,"abstract":"<p><p><i>Objective.</i>Current lung cancer diagnostic techniques primarily focus on tissue subtype classification, yet remain inadequate in distinguishing pathological progression subtypes (particularly between adenocarcinoma<i>in situ</i>and invasive adenocarcinoma) on frozen sections. This study develops a deep neural network-based auxiliary diagnostic system specifically for surgical frozen sections, aiming to reduce pathologists' diagnostic workload while improving differentiation accuracy.<i>Approach.</i>We developed an innovative deep learning system (FSG-TL Model) for lung adenocarcinoma frozen section analysis, combining multi-instance learning with EMA/SimAM/SE attention-enhanced ResSimAM_Hybrid model for classification. Create carefully annotated frozen section datasets. FSG-TL Model integrates down sampling, tissue localization and classification to achieve automatic cancer detection, and improves classification performance through image enhancement and classification model optimization.<i>Main</i><i>Results.</i>The method developed in this study exhibited significant accuracy in identifying cancerous regions in frozen sections while successfully distinguishing between various cancer subtypes. A comprehensive automated localization system for lung adenocarcinoma full-scan sections was adeptly constructed, enabling swift localization of a 40,000×60,000 pixel full slide image in around 3 minutes. Notably, in the subtype instance classification of tumor region localization, ResSimAM_Hybrid achieved a classification accuracy (ACC) of 90.72%, outperforming the computational-pathology foundation model UNI. For the tumor localization task, the FSG-TL Model attained a tumor localization Dice score of 0.82. The localization Dice score for AIS and IAC reached 0.77 and 0.69, respectively.<i>Significance.</i>This study provides a fast and accurate method for localizing cancer and lung adenocarcinoma subtypes in frozen sections. It provides important support for future research on AI-assisted clinical diagnosis of lung adenocarcinoma in frozen sections and reveals the research potential of AI-assisted diagnosis of subtypes of lung adenocarcinoma in the stage of pathological progression.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233054","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":"Supervised and unsupervised learning for lung perfusion data segmentation in electrical impedance tomography.","authors":"Marcus Victor, Arthur Ribeiro, Monica Matsumoto, Yi Xin, Alice Nova, Timothy Gaulton, Maurizio Cereda","doi":"10.1088/2057-1976/ade158","DOIUrl":"10.1088/2057-1976/ade158","url":null,"abstract":"<p><p><i>Objective</i>: Effective lung gas exchange relies on the balance between alveolar ventilation and perfusion, which can be disrupted in mechanically ventilated patients. Lung perfusion assessment using electrical impedance tomography (EIT) typically involves a sudden injection of a hypertonic saline solution. The large field of view provided by EIT often results in ambivalent behavior of many voxel waveforms following an indicator injection, where some exhibit indicator kinetics solely through the lungs (pulmonary), while others show passage through both the heart and lungs (hybrid). Consequently, a segmentation algorithm is essential for accurate perfusion evaluation.<i>Approach</i>: Sixteen pigs (29-35 kg) were mechanically ventilated and received a 10 ml bolus of 7.5% NaCl solution to assess lung perfusion during a healthy stage and, later, in an injured stage after receiving 3.5 ml kg^-1of HCl to induce acute lung injury. Supervised (Bagged Trees, Neural Networks, and Support Vector Machine) and unsupervised (K-means, Hierarchical, and Principal Component Analysis) learning methods were employed using 115 saline injections comprising voxel waveforms to label voxels as either hybrid or pulmonary. All segmentation methods were compared to a ground-truth mask manually drawn. A training dataset (81 injections) was used to train and cross-validate (five-fold) the supervised methods using previously extracted features. The test dataset (34 injections) was used to test both supervised and unsupervised learning algorithms.<i>Main Results</i>: A Principal Component Analysis (unsupervised learning) method exhibited the best overall performance, achieving 83% sensitivity, 92% specificity, 89% accuracy, and 84% dice similarity coefficient. No significant difference in performance was observed between healthy and injured subsets. Unsupervised methods consistently yielded more physiologically plausible and less scattered regions of interest.<i>Significance</i>: Accurate voxel labeling is crucial for lung perfusion assessment, as it enables discrimination of the indicator passage through the heart and lungs, thereby improving the estimation of regional pulmonary blood flow.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233055","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":"Cone beam computed tomography in 6- and 60-second acquisitions: implications for adaptive radiotherapy when respiratory motion is present.","authors":"P A K Oliver, L Montgomery, D A Granville","doi":"10.1088/2057-1976/adde65","DOIUrl":"10.1088/2057-1976/adde65","url":null,"abstract":"<p><p><i>Purpose</i>. To investigate the effects of respiratory motion during fast (∼6 s) and slow (∼60 s) cone beam computed tomography (CBCT) acquisition modes, with a focus on implications for adaptive radiotherapy (ART).<i>Methods</i>. CBCT images are compared with 4D fan beam CT acquisitions, considering average ('AVE') and maximum ('MIP') intensity projections. Data are acquired using a respiratory motion phantom representing a human thorax with a lung tumour. A range of sup-inf motion amplitudes (3 to 11 mm) and periods (3 to 5 s) are considered. HU perturbations, target contouring implications, and dosimetric effects are considered.<i>Results</i>. Fast mode CBCT motion artefacts are more severe for larger amplitudes and longer periods. Motion artefacts are minimal in slow mode. The standard deviation of HU differences (CBCT minus AVE) in regions-of-interest encompassing the tumour are within 44 HU for slow mode, increasing up to 75 HU for fast mode. Target volumes contoured using HU thresholding on slow mode CBCTs are smaller than those on the AVE/MIP by up to 7%/29%. HU thresholding was not applied to fast mode CBCTs because motion artefacts were judged to be too severe. Gamma pass rates for dose distributions calculated on fast or slow mode CBCTs compared to the AVE are≥99% (criteria: 1%, 1 mm, 10% dose threshold). Dose differences (fast mode CBCT minus AVE) are larger for larger amplitudes and longer periods, and tend toward negative values. Dose differences (slow mode CBCT minus AVE) are generally smaller and more consistent across all amplitudes and periods considered.<i>Conclusions</i>. Dosimetric perturbations resulting from motion artefacts are not severe for the amplitudes and periods considered. However, motion artefacts (especially in fast mode) have implications for image registration, target contouring, and treatment plan optimization for ART.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180164","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":"ADC-MambaNet: a lightweight U-shaped architecture with mamba and multi-dimensional priority attention for medical image segmentation.","authors":"Thi-Nhu-Quynh Nguyen, Quang-Huy Ho, Van Quang Nguyen, Van-Truong Pham, Thi-Thao Tran","doi":"10.1088/2057-1976/adde66","DOIUrl":"10.1088/2057-1976/adde66","url":null,"abstract":"<p><p><i>Objective.</i>Medical image segmentation is becoming a growing crucial step in assisting with disease detection and diagnosis. However, medical images often exhibit complex structures and textures, resulting in the need for highly complex methods. Particularly, when Deep Learning methods are utilized, they often require large-scale pretraining, leading to significant memory demands and increased computational costs. The well-known Convolutional Neural Networks (CNNs) have become the backbone of medical image segmentation tasks thanks to their effective feature extraction abilities. However, they often struggle to capture global context due to the limited sizes of their kernels. To address this, various Transformer-based models have been introduced to learn long-range dependencies through self-attention mechanisms. However, these architectures typically incur relatively high computational complexity.<i>Methods.</i>To address the aforementioned challenges, we propose a lightweight and computationally efficient model named ADC-MambaNet, which combines the conventional Depthwise Convolutional layers with the Mamba algorithm that can address the computational complexity of Transformers. In the proposed model, a new feature extractor named Harmonious Mamba-Convolution (HMC) block, and the Multi-Dimensional Priority Attention (MDPA) block have been designed. These blocks enhance the feature extraction process, thereby improving the overall performance of the model. In particular, the mechanisms enable the model to effectively capture local and global patterns from the feature maps while keeping the computational costs low. A novel loss function called the Balanced Normalized Cross Entropy is also introduced, bringing promising performance compared to other losses.<i>Results.</i>Evaluations on five public medical image datasets: ISIC 2018 Lesion Segmentation, PH2, Data Science Bowl 2018, GlaS, and Lung x-ray demonstrate that ADC-MambaNet achieves higher evaluation scores while maintaining a compact parameters and low computational complexity.<i>Conclusion.</i>ADC-MambaNet offers a promising solution for accurate and efficient medical image segmentation, especially in resource-limited or edge-computing environments. Implementation code will be publicly accessible at:https://github.com/nqnguyen812/mambaseg-model.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180684","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":"Quality assurance of beam base data in modern radiotherapy: a Monte Carlo simulation approach.","authors":"Matthias Kowatsch, Eva Partoll, Thomas Künzler, Christian Attenberger, Philipp Szeverinski, Patrick Clemens, Peter Tschann","doi":"10.1088/2057-1976/adde64","DOIUrl":"10.1088/2057-1976/adde64","url":null,"abstract":"<p><p>Accurate dose computation in radiotherapy is critical due to the complexity of modern treatment modalities. Beam base data (BBD) underpin the precision of dose calculations in techniques such as Volumetric Modulated Arc Therapy (VMAT), Intensity-Modulated Radiation Therapy (IMRT), Stereotactic Radiosurgery (SRS), and Stereotactic Body Radiation Therapy (SBRT). Even minor discrepancies in BBD can compromise the accuracy of dose computations, necessitating quality assurance (QA). This study investigates the application of Monte Carlo (MC) simulations, considered the 'gold standard' in dose calculations, for BBD QA using SciMoCa. SciMoCa is a Monte Carlo dose engine which shares its concepts with the VMC family of codes. A total of 87 BBD sets, from 39 datasets, representing diverse linacs, were analyzed, provided by the vendor of the MC-system. Systematic errors (e.g., dose, point dose, spectrum, output errors) were categorized into error classes: severe (Type 1), moderate (Type 2), minor (Type 3). Measurements were conducted using ionization chambers and diodes, and results were compared to MC simulations. The virtual source model was tested against measurements as a proof of concept, showing an overall deviation of less than 1%, with output factors differing by less than 0.3%. The analysis of the 87 BBD sets presented that 86% of BBD sets passed the criterions of Type 1 errors, 60% for Type 2 and 28% for Type 3 criteria. In absolute terms, 24 of the 87 BBD sets met the minimum criteria and would not compromise dose calculation in TPS. The study highlights the potential of MC simulations in establishing a standardized approach to BBD QA. This approach allows for robust validation of BBD quality and self-consistency, achieving typical precision within ±0.5%. In more challenging cases, the precision may expand to ±1.0%.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179798","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}