Biomedical Physics & Engineering Express最新文献

{"title":"RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.","authors":"Xiaowen Tang, Yinsu Zhu","doi":"10.1088/2057-1976/adb494","DOIUrl":"10.1088/2057-1976/adb494","url":null,"abstract":"<p><p><i>Objectives</i>Accurate identification of molecular subtypes in breast cancer is critical for personalized treatment. This study introduces a novel neural network model, RAE-Net, based on Multimodal Feature Fusion (MFF) and the Evidential Deep Learning Algorithm (EDLA) to improve breast cancer subtype prediction using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).<i>Methods</i>A dataset of 344 patients with histologically confirmed breast cancer was divided into training (n = 200), validation (n = 60), and testing (n = 62) cohorts. RAE-Net, built on ResNet-50 with Multi-Head Attention (MHA) fusion and Multi-Layer Perceptron (MLP) mechanisms, combines radiomic and deep learning features for subtype prediction. The EDLA module adds uncertainty estimation to enhance classification reliability.<i>Results</i>The RAE-Net model incorporating the MFF module demonstrated superior performance, achieving a mean accuracy of 0.83 and a Macro-F1 score of 0.78, surpassing traditional radiomics models (accuracy: 0.79, Macro-F1: 0.75) and standalone deep learning models (accuracy: 0.80, Macro-F1: 0.76). When an EDLA uncertainty threshold of 0.2 was applied, the performance significantly improved, with accuracy reaching 0.97 and Macro-F1 increasing to 0.92. Additionally, RAE-Net outperformed two recent deep learning networks, ResGANet and HIFUSE. Specifically, RAE-Net showed a 0.5% improvement in accuracy and a higher AUC compared to ResGANet. In comparison to HIFUSE, RAE-Net reduced both the number of parameters and computational cost by 90% while only increasing computation time by 5.7%.<i>Conclusions</i>RAE-Net integrates feature fusion and uncertainty estimation to predict breast cancer subtypes from DCE-MRI. The model achieves high accuracy while maintaining computational efficiency, demonstrating its potential for clinical use as a reliable and resource-efficient diagnostic tool.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143397803","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":"Platform for detecting, managing, and manipulating characteristic points of the ECG waves through continuous wavelet transform implementation.","authors":"Frank Martínez-Suárez, Carlos Alvarado-Serrano, Oscar Casas","doi":"10.1088/2057-1976/adb589","DOIUrl":"10.1088/2057-1976/adb589","url":null,"abstract":"<p><p>This work presents open-source software that incorporates detection and delineation algorithms of characteristic points of QRS complexes and P and T waves in ECG recordings. The tool facilitates the identification of significant points in the ECG waves, allowing manual correction of the results based on user criteria, exporting the detected points, and a simultaneous visualization of the recordings and the obtained points. The main objective is to improve the management of long- and short-term recordings by reducing detection errors caused by noise, interference, and artifacts, while also providing the capability for manual results correction. To achieve these objectives, the software uses an SQL Server database, which efficiently manages the data, and detection and delineation algorithms based on the continuous wavelet transform with splines, along with alternatives to optimize processing time. The QRS complex detection algorithm was validated in a previous work with the manually annotated ECG databases: MIT-BIH Arrhythmia, European ST-T, and QT. The QRS detector obtained a Se = 99.91% and a P⁺= 99.62% on the first channel of the MIT-BIH, ST-T and QT databases over the 986,930 QRS complexes analyzed. To evaluate the delineation algorithms of the characteristic points of QRS, P and T waves, the QT and PTB databases were used. The mean and standard deviations of the differences between the automatic and manual annotations by CSE experts were calculated. The mean errors range obtained was smaller than one sample (4 ms) to around two samples (8 ms); and the mean standard deviations range was around of two samples (8 ms) to six samples (24 ms).</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143413304","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":"EffNet: an efficient one-dimensional convolutional neural networks for efficient classification of long-term ECG fragments.","authors":"Bilal Ashraf, Husan Ali, Muhammad Aseer Khan, Fahad R Albogamy","doi":"10.1088/2057-1976/adb58a","DOIUrl":"10.1088/2057-1976/adb58a","url":null,"abstract":"<p><p>Early Diagnosis of Cardiovascular disease (CVD) is essential to prevent a person from death in case of a cardiac arrhythmia. Automated ECG classification is required because manual classification by cardiologists is laborious, time-consuming, and prone to errors. Efficient ECG classification has been an active research problem over the past few decades. Earlier ECG classification techniques didn't perform satisfactorily with greater accuracy and efficiency. An efficient 12-layer deep One-Dimensional Convolutional Neural Network (1D-CNN) titled EffNet is proposed in this research paper to automatically classify five distinct categories of heartbeats present in ECG signals. A unique collection of five different PhysioNet databases with ECG recordings of five different classes is created to enhance the dataset. These databases are segmented into ECG Fragments (long-term ECG signals of length 10 s) to capture the ECG features between successive beats effectively. These ECG fragments are then concatenated to form a merged dataset. Initially, sampling of the merged dataset is done. The Synthetic Minority Oversampling Technique (SMOTE) is used to balance the dataset. Afterwards, 1D-CNN is employed with different sets of hyperparameters for the efficient classification of the ECG dataset. Classification of ECG of five different classes is also done through two deep Convolutional Neural Networks (CNNs), namely GoogLeNet and SqueezeNet, and Support Vector Machines (SVM). The statistical results obtained proved the dominance of EffNet over the transfer learning techniques (SqueezeNet and GoogLeNet) and SVM. Furthermore, a comparison is also made with the existing literature work carried out for ECG classification, and the statistical results dominated over all others in terms of performance metrics.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143413303","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 synchronously increasing dose of multi-shell structure to improve stereotactic ablation radiotherapy central dose of large volume locally advanced gastrointestinal stromal tumors using cyberKnife.","authors":"Hui Xu, Zhen Jia, Xiongfei Li, Mingzhu Li, Hongyu Lin, Yunfei Bian, Wei Wang, Lian Zhang, Ying Li","doi":"10.1088/2057-1976/adb434","DOIUrl":"10.1088/2057-1976/adb434","url":null,"abstract":"<p><p><i><b>Purpose</b></i>. Increasing the central dose for large, locally advanced, drug-resistant gastrointestinal stromal tumors (LADR-GISTs) has consistently been a significant challenge. This study explores the feasibility of using multiple shell structures within the tumor to enhance the central ablation dose of large LADR-GIST by increasing the shell doses.<i><b>Methods and Materials</b></i>. This study involved five patients with large LADR-GIST who were treated with CyberKnife. The gross tumor volume (GTV) was delineated as a multi-shell structure. Five dose escalation plans (SIB-SBRT) were created for each patient, varying the dose escalation ratios. The radiation doses for the center of the GTV (GTV center) in these plans ranged from 49 Gy to 70 Gy. Parameter evaluations were conducted comparing the SIB-SBRT plans with conventional SBRT plans (Con-SBRT), focusing on equivalent uniform dose (EUD), relative equivalent uniform dose (rEUD), dose volume parameters, conformal index (CI), new conformal index (nCI), gradient index (GI), and monitor unit (MU). The Friedman Test was employed to determine statistical differences (<i>P</i>< 0.05), followed by pairwise comparisons.<i><b>Results</b></i>. When the dose escalation ratios reached 25% of the prescribed dose, the average rEUD increased to 6.92, and the proportion of the GTV volume with Biologically Equivalent Dose (BED)> 100 Gy increased to 30.69%. At dose escalation ratios of 30% of the prescribed dose, the rEUD stabilized, but the radiation dose received by the bladder, colon, and duodenum significantly increased. Except for the SIB₂₅-SBRT and SIB₃₀-SBRT groups, no statistically significant differences were observed between the other SIB-SBRT groups and the Con-SBRT group across various evaluation metrics.<i><b>Conclusions</b></i>. The method of synchronously increasing the dose using a multi-shell structure is feasible for stereotactic ablation in the treatment of LADR-GISTs using CyberKnife. The results indicate that dose escalation ratios of 25% of the prescribed dose can provide a satisfactory ablation dose (BED > 100 Gy), covering 31% of the large tumor volume.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143390055","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-based video-level view classification of two-dimensional transthoracic echocardiography.","authors":"Hanlin Cheng, Zhongqing Shi, Zhanru Qi, Xiaoxian Wang, Guanjun Guo, Aijuan Fang, Zhibin Jin, Chunjie Shan, Ruiyang Chen, Yue Du, Sunnan Qian, Shouhua Luo, Jing Yao","doi":"10.1088/2057-1976/adb493","DOIUrl":"10.1088/2057-1976/adb493","url":null,"abstract":"<p><p>In recent years, deep learning (DL)-based automatic view classification of 2D transthoracic echocardiography (TTE) has demonstrated strong performance, but has not fully addressed key clinical requirements such as view coverage, classification accuracy, inference delay, and the need for thorough exploration of performance in real-world clinical settings. We proposed a clinical requirement-driven DL framework, TTESlowFast, for accurate and efficient video-level TTE view classification. This framework is based on the SlowFast architecture and incorporates both a sampling balance strategy and a data augmentation strategy to address class imbalance and the limited availability of labeled TTE videos, respectively. TTESlowFast achieved an overall accuracy of 0.9881, precision of 0.9870, recall of 0.9867, and F1 score of 0.9867 on the test set. After field deployment, the model's overall accuracy, precision, recall, and F1 score for view classification were 0.9607, 0.9586, 0.9499, and 0.9530, respectively. The inference time for processing a single TTE video was 105.0 ± 50.1 ms on a desktop GPU (NVIDIA RTX 3060) and 186.0 ± 5.2 ms on an edge computing device (Jetson Orin Nano), which basically meets the clinical demand for immediate processing following image acquisition. The TTESlowFast framework proposed in this study demonstrates effective performance in TTE view classification with low inference delay, making it well-suited for various medical scenarios and showing significant potential for practical application.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143397671","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":"Magnetically shielded high-resolution visual stimulation for OPM-MEG applications.","authors":"P Anders, M Brickwedde, J Voigt, T Grent-'t-Jong, P Krüger, J Haueisen, P J Uhlhaas, T Sander","doi":"10.1088/2057-1976/adb1eb","DOIUrl":"10.1088/2057-1976/adb1eb","url":null,"abstract":"<p><p>Many magnetoencephalography (MEG) experiments require visual stimulation (VS) inside a magnetically shielded room (MSR). For conventional MEG utilizing superconducting quantum interference devices (SQUIDs), the participant's head must stay within the semi-spherical surface of a cryogenic storage Dewar. This design allows to have many SQUID sensors as close as possible to the head in order to achieve good signal quality. Because Dewars have very restricted mobility, VS is usually realized using a projector outside of the MSR, some optical elements and a back-projection screen in the line of sight of the participant.Recently, the feasibility of MEG using optically pumped magnetometers (OPMs) was demonstrated. These sensors can be attached directly to the head because they operate near room temperature. OPM-MEG therefore offers more experimental freedom including different postures, movements or hyperscanning, creating the need for a more flexible kind of VS setup.In this paper, we present a compact, high-resolution VS setup which is enclosed by a portable magnetic shield with an opening for the projection. The VS setup is based on a single-board computer which acts as experiment control device to create visual stimuli, process inputs, log participant activity and set off trigger signals. This setup supports the new possibilities of OPM-MEG and can be easily installed into any MSR. We investigate if the shielded VS inside the MSR generates distortion signals above the noise floor of the OPMs. We also show that visual cortex activity can be evoked with our setup and recorded with a custom-made OPM-MEG cap. By applying two well-established visual stimulation paradigms, we demonstrate the ability of our setup to elicit brain activity in different frequency ranges.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187977","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":"Simulation of fluid flow with Cuprophan and AN69ST membranes in the dialyzer during hemodialysis.","authors":"José Luis Velázquez Ortega, Aldo Gómez López, Esteban Adrian Romero López","doi":"10.1088/2057-1976/adaec4","DOIUrl":"10.1088/2057-1976/adaec4","url":null,"abstract":"<p><p>Hemodialysis is a crucial procedure for removing toxins and waste from the body when kidneys fail to perform this function effectively. This study addresses the need to improve the efficiency and biocompatibility of membranes used in dialyzers. We simulate fluid flow through two types of membranes, Cuprophan (cellulosic) and AN69ST (synthetic), to understand the complex mechanisms involved and quantify key variables such as pressure, concentration, and flow. This study presents a detailed model that applies mass conservation equations and Navier-Stokes principles adapted for porous media, along with heat and mass transfer considerations. The results revealed significant differences in the flow behavior and filtration efficiency between the two membranes, highlighting the superiority of the AN69ST membrane in terms of flow rate and toxin removal. This model serves as a valuable tool for characterizing new porous membranes in dialysis applications, enabling the prediction of the temperature, pressure, and concentration profiles. By providing this information without requiring extensive experimentation, the model complements the design and evaluation of new membranes and, optimizes their development. The ability to predict these profiles is crucial because they directly influence the parameters that determine treatment effectiveness. Moreover, this study underscores the importance of continued innovation in membrane materials and designs, contributing to improved clinical outcomes and treatment efficiency, representing a significant advancement in healthcare.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051481","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":"Investigation on the heating effects of intra-tumoral injectable magnetic hydrogels (IT-MG) for cancer hyperthermia.","authors":"Hema Brindha Masanam, Janani Muthuraman, Bharath Chandra, Venkata Naga Sundara Mahesh Kottapalli, Sai Sarath Chandra, Piyush Kumar Gupta, Ashwin Kumar Narasimhan","doi":"10.1088/2057-1976/adaec6","DOIUrl":"10.1088/2057-1976/adaec6","url":null,"abstract":"<p><p>Capacitive-based radiofrequency (Rf) radiation at 27 MHz offers a non-invasive approach for inducing hyperthermia, making it a promising technique for thermal cancer therapy applications. To achieve focused and site-specific hyperthermia, Rf-responsive materials is required to convert Rf radiation into localized heat efficiently. Nanoparticles capable of absorbing Rf energy and convert into heat for targeted ablation are of critical importance. In this study, we developed and evaluated an Intra-tumoral injectable magnetic hydrogel (IT-MG) composed of Superparamagnetic Iron Oxide Nanoparticles (SPIONs) impregnated in low molecular weight Hyaluronic Acid (HA) forming HA-SPIONs. Our systematic investigation revealed that HA-SPIONs exposed to Rf radiation significantly increased temperature, reaching up to 50 °C. Further testing in tissue-mimicking phantom models also showed consistent heating, with temperatures stabilizing at 43 °C, ideal for localized hyperthermia. The ability of HA-SPIONs to act as an effective localized heating agent when exposed to 27 MHz Rf radiation, reaching apoptosis-inducing temperature, has not been previously reported. In conclusion, synergistic effects of IT-MG in both<i>in-vitro</i>and tumor-mimicking phantom models demonstrate improved and localized hyperthermia, facilitating adjuvant cancer treatment.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051471","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":"Quantifying radiotherapy beam quality: an analysis using gamma passing rates.","authors":"Xiang Gao, Yipeng He, Yanjuan Yu, Sijia Chen, Guanglu Gao, Lirong Fu, Liwan Shi, Zheng Kang","doi":"10.1088/2057-1976/adb291","DOIUrl":"10.1088/2057-1976/adb291","url":null,"abstract":"<p><p><i>Purpose</i>. PDD and profile curves play a crucial role in analyzing the beam quality and energy stability of accelerators. The aim of this study was to assess the efficacy of GPR in machine QA and compare it with traditional methods for analyzing dose outputs.<i>Methods</i>. GPRs were employed to assess the quality of radiation beams by comparing 1D and 2D Profile metrics and PDD data against commissioning data. The data used were obtained from the ASCII data files derived from the water tank. GPRs were calculated for all plots with a lower percentage dose cutoff of 10%. The local GPRs and dose influence for the 2D PDD metrics and dose influence were calculated for an open field 10 × 10 cm²photon beam at SSD = 100 cm. In both 1D and 2D GPRs analyses, criterion of 1%/1 mm was adopted, as this approach allows for the capture of more subtle variations in the data. To substantiate the viability of the study, a comparative analysis was conducted by comparing the outcomes of the gamma analysis with those derived from traditional methods, such as manual machine quality assurance checks.<i>Results</i>. GPRs demonstrated a superior capability for comprehensive data analysis compared to traditional methods. For the 1D curves, the passing rates (<i>γ</i>≤ 1) are 96.19%, 100%, and 93.46%, respectively. With respect to the 2D dose influence, the PDD image passing rate was 99.57%, and significant dose differences were observed at the four corners of the open field, indicating areas that require further investigation.<i>Conclusions</i>. Compared to traditional methods, GPRs are more sensitive to subtle changes in the data, providing valuable insights into the accelerator beam status.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254551","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":"Validation of a 3D printed bolus for radiotherapy: a proof-of-concept study.","authors":"A C Ciobanu, L C Petcu, F Járai-Szabó, Z Bálint","doi":"10.1088/2057-1976/adb15d","DOIUrl":"10.1088/2057-1976/adb15d","url":null,"abstract":"<p><p>3D-printed boluses in radiation therapy are of increasing interest for enhancing treatment precision and patient comfort. A comprehensive clinical validation of these boluses remains to be established. This study aims to confirm the effectiveness of a 3D-printed bolus through a proof-of-concept comparative validation, by implementing in a clinical setting a bolus made of PLA and designed to ensure uniform dose coverage for a case in the eye region. In this study the 3D-printed bolus was compared to two commercially available boluses (one thermoplastic and one skin type) by using a refecence where no bolus was present (with the optimal dose distribution scenario). All boluses were placed on an anthropomorphic head phantom and BeOSL detectors were used to measure dose values to determine the level of their effectiveness on delivery. During the scanning process, a thermoplastic mask was used to prevent bolus movement and to accurately reproduce clinical scenarios. Differences in dose values at D_maxand D_50%revealed the performance of each bolus. The treatment planning system (TPS) and BeOSL readings for the 3D printed bolus were within 2% (the clinical tolerance), with 0.66% dose difference for the customized 3D-printed bolus. Although the thermoplastic bolus had the closest value to the detector reading, with a score of 0.30%, this result was influenced by improper shaping of the bolus on the phantom and the presence of a wide air gap, which caused lack of eye covering. Whereas, the skin bolus, due to higher volume of air between phantom surface and bolus, showed a 1.29% dose difference between the TPS values and the OSL detector readings. We provide a comparative validation for the use of 3D printed boluses and highlight that proper bolus fitting is essential in clinical settings to avoid air gaps and to maintain dose distribution over multiple treatment sessions.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121978","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}