Biomedical Physics & Engineering Express最新文献

{"title":"Hierarchical agent transformer network for COVID-19 infection segmentation.","authors":"Yi Tian, Qi Mao, Wenfeng Wang, Yan Zhang","doi":"10.1088/2057-1976/adbafa","DOIUrl":"10.1088/2057-1976/adbafa","url":null,"abstract":"<p><p>Accurate and timely segmentation of COVID-19 infection regions is critical for effective diagnosis and treatment. While convolutional neural networks (CNNs) exhibit strong performance in medical image segmentation, they face challenges in handling complex lesion morphologies with irregular boundaries. Transformer-based approaches, though demonstrating superior capability in capturing global context, suffer from high computational costs and suboptimal multi-scale feature integration. To address these limitations, we proposed Hierarchical Agent Transformer Network (HATNet), a hierarchical encoder-bridge-decoder architecture that optimally balances segmentation accuracy with computational efficiency. The encoder employs novel agent Transformer blocks specifically designed to capture subtle features of small COVID-19 lesions through agent tokens with linear computational complexity. A diversity restoration module (DRM) is innovatively embedded within each agent Transformer block to counteract feature degradation. The hierarchical structure simultaneously extracts high-resolution shallow features and low-resolution fine features, ensuring comprehensive feature representation. The bridge stage incorporates an improved pyramid pooling module (IPPM) that establishes hierarchical global priors, significantly improving contextual understanding for the decoder. The decoder integrates a full-scale bidirectional feature pyramid network (FsBiFPN) with a dedicated border-refinement module (BRM), collectively enhancing edge precision. The HATNet were evaluated on the COVID-19-CT-Seg and CC-CCII datasets. Experimental results yielded Dice scores of 84.14% and 81.22% respectively, demonstrating superior segmentation performance compared to state-of-the-art models. Furthermore, it achieved notable advantages in model parameters and computational complexity, highlighting its clinical deployment potential.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143522303","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 image quality and fetal dose through current modulation CT-scan using pregnancy phantoms.","authors":"Endarko Endarko, Fathul Jannah, Aditya Prayugo Hariyanto, Isfina Uniatunada, Choirul Anam, Nurhanifa Tri Budiarti","doi":"10.1088/2057-1976/adb9ed","DOIUrl":"10.1088/2057-1976/adb9ed","url":null,"abstract":"<p><p>Computed Tomography (CT) is often required in special circumstances during pregnancy to determine internal medicine, specifically when information from other imaging techniques, such as ultrasound and magnetic resonance imaging (MRI), is still inadequate. In CT-Chest, abdomen, and pelvis (CAP) examination, direct irradiation of the fetal is necessary, indicating that the potential for fetal exposure must be considered. Therefore, this study evaluated the effects of current modulation on image quality and fetal absorbed dose in pregnancy CT scan. Calculation using IndoseCT and film dosimeters were used with a 3D-printed anthropomorphic pregnant phantom thorax-abdomen-pelvic during the first and third trimesters of pregnancy. Image quality analysis and image noise were then measured by IndoseCT, while fetal dose analysis was performed using IndoseCT software, as well as through direct measurements with an XR-QA2 film dosimeter. Statistical tests were performed to compare the data obtained using both methods. The results showed that the use of current modulation increased the image noise. The fetal dose can be significantly reduced by adjusting the tube current and patient diameter without affecting image quality in the first and third trimesters. It was also revealed that the smallest diameter received the largest dose in both trimesters for nonmodulation. Although there were differences in the dose values obtained from the IndoseCT and measurements, the data patterns were not significantly different. Furthermore, based on these results, the dosage value was below the tolerance threshold for deterministic effects (i.e., <50 mGy).</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498526","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":"Bilateral network with text guided aggregation architecture for lung infection image segmentation.","authors":"Xiang Pan, Hanxiao Mei, Jianwei Zheng, Herong Zheng","doi":"10.1088/2057-1976/adb290","DOIUrl":"10.1088/2057-1976/adb290","url":null,"abstract":"<p><p><i>Objective.</i>Lung image segmentation is a crucial problem for autonomous understanding of the potential illness. However, existing approaches lead to a considerable decrease in accuracy for lung infection areas with varied shapes and sizes. Recently, researchers aimed to improve segmentation accuracy by combining diagnostic reports based on text prompts and image vision information. However, limited by the network structure, these methods are inefficient and ineffective.<i>Method.</i>To address this issue, this paper proposes a Bilateral Network with Text Guided Aggregation Architecture (BNTGAA) to fully fuse local and global information for text and image vision. This proposed architecture involves (i) a global fusion branch with a Hadamard product to align text and vision feature representation and (ii) a multi-scale cross-fusion branch with positional coding and skip connection, performing text-guided segmentation in different resolutions. (iii) The global fusion and multi-scale cross-fusion branches are combined to feed a mamba module for efficient segmentation.<i>Results.</i>Extensive quantitative and qualitative evaluations demonstrate that the proposed architecture performs better both in accuracy and efficiency. Our architecture outperforms the current best methods on the QaTa-COVID19 dataset, improving mIoU and Dice scores by 3.08% and 2.35%, respectively. Meanwhile, our architecture surpasses the computational speed of existing multimodal networks. Finally, the architecture has a quick convergence and generality. It can exceed the performance of the current best methods even if it is trained with only 50% of the dataset.<i>Conclusion.</i>With the backbone Mamba, the proposed fusion architecture, which performs text-guided aggregation under different scales, can greatly improve segmentation performance both in accuracy and efficiency. Codes are available at https://github.com/Meihanxiao/BNTGAA.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254550","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":"Coupled ionizing-radiation/optical-photon transport Monte Carlo simulations for characterisation of light signal in an optical fiber radioluminescence dosimetry system.","authors":"F Moradi, A Oresegun, A Khodaei, D A Bradley, A Taheri, M U Khandaker, H A Abdul-Rashid","doi":"10.1088/2057-1976/adba65","DOIUrl":"10.1088/2057-1976/adba65","url":null,"abstract":"<p><p>Optical fiber radioluminescence (RL) dosimetry has gained prominence in modern radiation therapy, offering real-time measurement and high spatial resolution. Our research group has developed a system utilizing a polymethyl methacrylate (PMMA) transmission fiber coupled with a photodetector and various scintillators, including doped silica fibers. A critical challenge in RL dosimetry lies in distinguishing the stem signal, generated by the transmission optical fiber, from the primary light signal produced by the RL sensor. To address this issue, we employed the Geant4 simulation tool, allowing for the simultaneous tracking of ionizing radiation and optical photons. In this study, the Geant4-based code, TOPAS, was utilized to conduct Monte Carlo simulations, aiming to gain insights into the radioluminescence signal in an optical fiber RL dosimeter and specifically characterize the stem signal for enhanced measurement accuracy. The simulations encompassed interactions of a medical photon beam from an Elekta linac within a solid water phantom, subsequent energy deposition within the RL sensor, and the generation and transmission of light signals within the optical fiber. Our emphasis was placed on detailed characterization of the light signals originating from both the Ge-doped silica fiber and PMMA transmission fiber. The primary focus was not only to discern the stem signal from the main signal but also to differentiate between the fluorescence and Cerenkov signals. Importantly, our study showcases how Monte Carlo simulations can be used to spectrally distinguish the stem signal from the scintillation signal of the sensor. This provides valuable information, especially in scenarios where spectrometry is unavailable, contributing to the understanding and refinement of optical fiber RL dosimetry systems.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143514569","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":"On the feasibility of an online brain-computer interface-based neurofeedback game for enhancing attention and working memory in stroke and mild cognitive impairment patients.","authors":"Suhail T A, Subasree R, A P Vinod, Suvarna Alladi","doi":"10.1088/2057-1976/adb8ef","DOIUrl":"10.1088/2057-1976/adb8ef","url":null,"abstract":"<p><p><i>Background</i>. Neurofeedback training (NFT) using Electroencephalogram-based Brain Computer Interface (EEG-BCI) is an emerging therapeutic tool for enhancing cognition.<i>Methods</i>. We developed an EEG-BCI-based NFT game for enhancing attention and working memory of stroke and Mild cognitive impairment (MCI) patients. The game involves a working memory task during which the players memorize locations of images in a matrix and refill them correctly using their attention levels. The proposed NFT was conducted across fifteen participants (6 Stroke, 7 MCI, and 2 non-patients). The effectiveness of the NFT was evaluated using the percentage of correctly filled matrix elements and EEG-based attention score. EEG varitions during working memory tasks were also investigated using EEG topographs and EEG-based indices.<i>Results</i>. The EEG-based attention score showed an enhancement ranging from 4.29-32.18% in the Stroke group from the first session to the third session, while in the MCI group, the improvement ranged from 4.32% to 48.25%. We observed significant differences in EEG band powers during working memory operation between the stroke and MCI groups.<i>Significance</i>. The proposed neurofeedback game operates based on attention and aims to improve multiple cognitive functions, including attention and working memory, in patients with stroke and MCI.<i>Conclusions</i>. The experimental results on the effect of NFT in patient groups demonstrated that the proposed neurofeedback game has the potential to enhance attention and memory skills in patients with neurological disorders. A large-scale study is needed in the future to prove the efficacy on a wider population.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472107","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":"Deep learning-assisted identification and localization of ductal carcinoma from bulk tissue in-silico models generated through polarized Monte Carlo simulations.","authors":"Janaki Ramkumar, Sujatha Narayanan Unni","doi":"10.1088/2057-1976/adb495","DOIUrl":"10.1088/2057-1976/adb495","url":null,"abstract":"<p><p>Despite significant progress in diagnosis and treatment, breast cancer remains a formidable health challenge, emphasizing the continuous need for research. This simulation study uses polarized Monte Carlo approach to identify and locate breast cancer. The tissue model Mueller matrix derived from polarized Monte Carlo simulations provides enhanced contrast for better comprehension of tissue structures. This study explicitly targets tumour regions found at the tissue surface, a possible scenario in thick tissue sections obtained after surgical removal of breast tissue lumps. We use a convolutional neural network for the identification and localization of tumours. Nine distinct spatial positions, defined relative to the point of illumination, allow the identification of the tumour even if it is outside the directly illuminated area. A system incorporating deep learning techniques automates processes and enables real-time diagnosis. This research paper aims to showcase the concurrent detection of the tumour's existence and position by utilizing a Convolutional Neural Network (CNN) implemented on depolarized index images derived from polarized Monte Carlo simulations. The classification accuracy achieved by the CNN model stands at 96%, showcasing its optimal performance. The model is also tested with images obtained from in-vitro tissue models, which yielded 100% classification accuracy on a selected subset of spatial positions.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143397936","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 finite element analysis model for magnetomotive ultrasound elastometry magnet design with experimental validation.","authors":"Jacquelline Nyakunu, Christopher T Piatnichouk, Henry C Russell, Niels J van Duijnhoven, Benjamin E Levy","doi":"10.1088/2057-1976/adb8f0","DOIUrl":"10.1088/2057-1976/adb8f0","url":null,"abstract":"<p><p><i>Objective</i>. Magnetomotive ultrasound (MMUS) using magnetic nanoparticle contrast agents has shown promise for thrombosis imaging and quantitative elastometry via magnetomotive resonant acoustic spectroscopy (MRAS). Young's modulus measurements of smaller, stiffer thrombi require an MRAS system capable of generating forces at higher temporal frequencies. Solenoids with fewer turns, and thus less inductance, could improve high frequency performance, but the reduced force may compromise results. In this work, a computational model capable of assessing the effectiveness of MRAS elastometry magnet configurations is presented and validated.<i>Approach</i>. Finite element analysis (FEA) was used to model the force and inductance of MRAS systems. The simulations incorporated both solenoid electromagnets and permanent magnets in three-dimensional steady-state, frequency domain, and time domain studies.<i>Main results</i>. The model successfully predicted that a configuration in which permanent magnets were added to an existing MRAS system could be used to increase the force supplied. Accordingly, the displacement measured in a magnetically labeled validation phantom increased by a factor of 2.2 ± 0.3 when the force was predicted to increase by a factor of 2.2 ± 0.2. The model additionally identified a new solenoid configuration consisting of four smaller coils capable of providing sufficient force at higher driving frequencies.<i>Significance</i>. These results indicate two methods by which MRAS systems could be designed to deliver higher frequency magnetic forces without the need for experimental trial and error. Either the number of turns within each solenoid could be reduced while permanent magnets are added at precise locations, or a larger number of smaller solenoids could be used. These findings overcome a key challenge toward the goal of MMUS thrombosis elastometry, and simulation files are provided online for broader experimentation.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472106","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":"Simultaneous reduction of radiation dose and scatter-to-primary ratio using a truncated detector and advanced algorithms for dedicated cone-beam breast CT.","authors":"Hsin Wu Tseng, Zhiyang Fu, Srinivasan Vedantham","doi":"10.1088/2057-1976/adb8f1","DOIUrl":"10.1088/2057-1976/adb8f1","url":null,"abstract":"<p><p><i>Objective</i>. To determine the minimum detector width along the fan-angle direction in offset-detector cone-beam breast CT for multiple advanced reconstruction algorithms and to investigate the effect on radiation dose, scatter, and image quality.<i>Approach.</i>Complete sinograms (<i>m</i>×<i>n</i>= 1024 × 768 pixels) of 30 clinical breast CT datasets previously acquired on a clinical-prototype cone-beam breast CT system were reconstructed using Feldkamp-Davis-Kress (FDK) algorithm and served as the reference. Complete sinograms were retrospectively truncated to varying widths to understand the limits of four image reconstruction algorithms-FDK with redundancy weighting (FDK-W), compressed-sensing based FRIST, fully-supervised MS-RDN, and self-supervised AFN. Upon determining the truncation limits, numerical phantoms generated by segmenting the reference reconstructions into skin, adipose, and fibroglandular tissues were used to determine the radiation dose and scatter-to-primary ratio (SPR) using Monte Carlo simulations.<i>Main results.</i>FDK-W, FRIST, and MS-RDN showed artifacts when<i>m</i>< 596, whereas AFN reconstructed images without artifacts for<i>m</i>> = 536. Reducing the detector width reduced signal-difference to noise ratio (SDNR) for FDK-W, whereas FRIST, MS-RDN and AFN maintained or improved SDNR. Reference reconstruction and AFN with<i>m</i>= 536 had similar quantitative measures of image quality.<i>Significance.</i>For the 30 cases, AFN with<i>m</i>= 536 reduced the radiation dose and SPR by 37.85% and 33.46%, respectively, compared to the reference. Qualitative and quantitative image quality indicate the feasibility of AFN for offset-detector cone-beam breast CT. Radiation dose and SPR were simultaneously reduced with a 536 × 768 detector and when used in conjunction with AFN algorithm had similar image quality as the reference reconstruction.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12047646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472108","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":"Determination of calibration reference values <1cGy MU^-1as detailed in the IPEM 2020 high-energy photon code of practice.","authors":"Niamh L Clarke, Glenn Whitten, Raymond B King, Alan R Hounsell, Denise M Irvine, Christina E Agnew","doi":"10.1088/2057-1976/adb435","DOIUrl":"10.1088/2057-1976/adb435","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;i&gt;Background&lt;/i&gt;. In 2020, the Institute of Physics and Engineering in Medicine (IPEM) published an updated code of practice (COP) for high-energy photon therapy dosimetry [Eaton DJ&lt;i&gt;et al&lt;/i&gt;2020 IPEM code of practice for high-energy photon therapy dosimetry based on the NPL absorbed dose calibration service&lt;i&gt;Physics in Medicine & Biology&lt;/i&gt;&lt;b&gt;65&lt;/b&gt;195006], with a further update published in 2023 [IPEM 2023&lt;i&gt;Policy Statement IPEM Recommendations on the Implementation of Codes of Practice&lt;/i&gt;Institue of Physics and Engineering in Medicine]. The 2020 COP provided an option to calibrate isocentrically at 10 cm deep to a reference value for each energy that is less than 1 cGy M&lt;sup&gt;-1&lt;/sup&gt;U&lt;sup&gt;-1&lt;/sup&gt;, to give approximately 1 cGy/MU at the depth of dose maximum (d&lt;sub&gt;max&lt;/sub&gt;). These reference values can relate the machine calibration at d&lt;sub&gt;max&lt;/sub&gt;in a fixed source-to-surface distance (SSD) setup, to the machine calibration in an isocentric setup at 10 cm deep. This option was provided to give consistency in the number of monitor units (MU) with dose in cGy in the updated reference conditions, to those typical of fixed SSD conditions, with minimal adjustment to linac output.&lt;i&gt;Purpose.&lt;/i&gt;The aim of this study was to determine such reference values for beam energies 6MV, 15MV, 6FFF and 10FFF and their respective tissue phantom ratios (TPR&lt;sub&gt;20,10&lt;/sub&gt;) 0.630-0.763.&lt;i&gt;Methods.&lt;/i&gt;Reference values for clinical energies 6MV, 15MV, 6FFF and 10FFF were determined over multiple Varian TrueBeam linacs, field ionisation chambers and electrometers and from historical quality control (QC) records over 11 years.&lt;i&gt;Results&lt;/i&gt;. Reference values were determined as 0.799, 0.929, 0.761 and 0.855 for 6MV, 15MV, 6FFF and 10FFF respectively. The variation in these data (1 standard deviation, [SD]) was &lt;0.004, demonstrating the stability of these reference values in a single centre study. Modelling of reference values using treatment planning system (TPS) data also demonstrated small differences compared to measured data of 0.06 ± 0.29 %. The relative standard uncertainty in these reference values was determined as 0.38 %. Adding this uncertainty in quadrature to the published uncertainties for isocentric calibration published in the 2020 COP contributes an additional 0.06 %.&lt;i&gt;Conclusions&lt;/i&gt;. Published reference values for clinical range of TPRs may aid centres implementing this option of the 2020 COP to confirm their data, with the shift away from a reference value of unity. Understanding the magnitude of the uncertainty in these reference values shows the impact of dose calibration at a reference position e.g. d&lt;sub&gt;max&lt;/sub&gt;, different to a dose planned and delivered isocentrically and can help ascertain the risk/benefit of changing machine calibration and measurement conditions. Similarly, given the magnitude of the uncertainty in these reference values, incorporating such a factor to convert from one calibration setup condition to anoth","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143390044","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 dose calculation method with a combination of Monte Carlo method and removal-diffusion equation in heterogeneous geometry for boron neutron capture therapy.","authors":"Mai Nojiri, Takushi Takata, Akinori Sasaki, Yuki Tamari, Nishiki Matsubayashi, Naonori Hu, Yoshinori Sakurai, Minoru Suzuki, Hiroki Tanaka","doi":"10.1088/2057-1976/ada7fe","DOIUrl":"10.1088/2057-1976/ada7fe","url":null,"abstract":"<p><p>Clinical research in boron neutron capture therapy (BNCT) has been conducted worldwide. Currently, the Monte Carlo (MC) method is the only dose calculation algorithm implemented in the treatment planning system for the clinical treatment of BNCT. We previously developed the MC-RD calculation method, which combines the MC method and the removal-diffusion (RD) equation, for fast dose calculation in BNCT. This study aimed to verify the partial-MC-RD calculation method, which utilizes the MC-RD calculation method for a portion of the entire neutron energy range, in terms of calculation accuracy and time as the dose calculation method. We applied the partial-MC-RD calculation method to calculate the total dose for head phantom, comprising soft tissue, brain tissue, and bone. The calculation time and accuracy were evaluated based on the full-MC method. Our accuracy verifications indicated that the partial-MC-RD calculation was mostly comparable with full-MC calculation in the accuracy. However, the assumptions and approximation used in the RD calculation mainly occurred the discrepancy from the full-MC calculation result. Additionally, the partial-MC-RD calculation reduced the time required to approximately 45% for the irradiation to the top and cheek region of head phantom, compared to the full-MC calculation. In conclusion, the MC-RD calculation method can be the basis of a fast dose calculation method in BNCT.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142943745","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}