Journal of Medical Physics最新文献

{"title":"A Digital Imaging and Communications in Medicine-based Collision Prediction System with an Angle Optimization Algorithm for Electron Beam Therapy.","authors":"Hideharu Miura, Toshiya Okazue","doi":"10.4103/jmp.jmp_29_26","DOIUrl":"10.4103/jmp.jmp_29_26","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to develop and validate a Digital Imaging and Communications in Medicine (DICOM)-based collision prediction system for electron beam therapy. This system enables a quantitative pretreatment assessment of mechanical interference between the electron applicator and the patient's body surface. It also automatically proposes safe alternative beam angle combinations.</p><p><strong>Materials and methods: </strong>We geometrically approximated a 20 cm × 20 cm electron applicator mounted on a TrueBeam STx linear accelerator as a 190-mm cube. We extracted body and table contours, as well as isocenter coordinates, from the RT Structure Set. For each combination of gantry, table, and collimator angles, the minimum clearance distance between the applicator model and the body contour was calculated. The discretized angle space was visualized using heatmaps of the minimum clearance distance. An optimization algorithm was implemented to calculate a score for each candidate angle set using a weighted sum of angular deviations from the original plan and to identify the best alternative angle configurations.</p><p><strong>Results: </strong>The system was evaluated using an adult female anthropomorphic phantom and four intentionally selected electron regions (superior, inferior, medial, and lateral around the left nipple). For the superior, inferior, and medial regions, the initial planned angles produced geometric intersections. However, the system-suggested alternative angle sets eliminated these intersections in three-dimensional visualization and were confirmed to be collision-free on the linear accelerator.</p><p><strong>Conclusions: </strong>The proposed system requires no additional hardware, integrates with current workflows, and provides a practical tool for collision risk assessment and angle optimization in nearsurface electron therapy.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 2","pages":"307-313"},"PeriodicalIF":1.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13344924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413719","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":"MI_VMAT: A New Comprehensive Modulation Index for Volumetric Modulated Arc Therapy Based on Three Key Variables.","authors":"Seeja Joseph, Saju Bhasi, K Anilkumar, R Swathi, Nicy Maria Thankachan, P Raghu Kumar","doi":"10.4103/jmp.jmp_21_26","DOIUrl":"10.4103/jmp.jmp_21_26","url":null,"abstract":"<p><strong>Objective: </strong>This study introduces a novel modulation index for volumetric modulated arc therapy (MI_VMAT), which simultaneously accounts for the combined effects of three key delivery variables. The index is validated for its ability to predict gamma pass rates (GPRs) across two different linear accelerator platforms.</p><p><strong>Materials and methods: </strong>MI_VMAT is derived from beam modulation at the control points (CPs) of VMAT plans. Modulation due to variations in gantry speed and dose rate is represented by changes in meter set weight across CPs and combined with the corresponding aperture area at each CP. In addition, the Field Weight (FW=MU/cGy), obtained from plan optimization and inherently dependent on these variables, is incorporated into the MI_VMAT formulation. The predictive performance of MI_VMAT was evaluated by analyzing its correlation with measured GPRs for VMAT plans delivered on two linear accelerators with different characteristics. Its performance was also compared with previously published and commonly used plan complexity metrics (PCMs). Patient plans were prospectively analyzed on both machines.</p><p><strong>Results: </strong>MI_VMAT demonstrated strong predictive capability. For the Unique linac, deviations were within 3% for GPR criteria of 3%/3 mm and 2%/3 mm. For the TrueBeam linac, deviations were within 1% for GPR criteria of 3%/3 mm, 3%/2 mm, and 3%/1 mm. Compared with a multivariate prediction model incorporating multiple PCMs, the univariate MI_VMAT model showed comparable or slightly superior performance, with lower mean deviations and reduced variability. MI_VMAT also exhibited strong to moderate correlations with most established metrics.</p><p><strong>Conclusion: </strong>The univariate MI_VMAT model shows substantial predictive power for VMAT delivery accuracy and performs comparably or better than multivariate PCM-based models, supporting its clinical utility.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 2","pages":"237-248"},"PeriodicalIF":1.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13345035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413753","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":"Comparative Study of Spinal Cord Toxicity from Dose Per Fraction Delivered to Cervical Spinal Cord in Head-and-neck Cancers by Volumetric-modulated Arc Therapy with Simultaneous Integrated Boost versus Three-dimensional Conformal Radiotherapy in a Murine Model.","authors":"Swati Verma, Manoj Kumar Gupta, Shailendra Handu, Shalinee Rao","doi":"10.4103/jmp.jmp_296_25","DOIUrl":"10.4103/jmp.jmp_296_25","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Aim: &lt;/strong&gt;To assess and compare the radiation-induced effects on the spinal cord of rats following fractionated irradiation with doses of 1.36 Gy and 2.0 Gy per fraction.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Materials and methods: &lt;/strong&gt;The risk of radiation-induced myelopathy and spinal cord tolerance represent the major dose-limiting factors during re-irradiation of head-and-neck cancers since spinal cord is exposed to maximum tolerance dose of 45 Gy in 1.8-2 Gy per fraction during first radiation treatment. However, modern radiotherapy techniques such as Volumetric-modulated Arc Therapy (VMAT) with simultaneous integrated boost (SIB) usually irradiate spinal cord with much less dose per fraction than 2 Gy per fraction of prescription dose. In this experimental study, the cervical spinal cord of male Wistar rats was irradiated to a total biologically effective dose of 120 Gy&lt;sub&gt;2&lt;/sub&gt; using different dose-per-fraction, control group was irradiated with 2 Gy per fraction for a total dose of 60 Gy/30F in 6 weeks while experimental group was given 1.36 Gy per fraction for a total dose of 70.72 Gy/52F in 10.5 weeks. The motor function including motor co-ordination and balance, locomotor activity was observed at 1, 3, and 5 months after the completion of radiation and was compared with baseline values.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Motor co-ordination and balance were preserved until the 3&lt;sup&gt;rd&lt;/sup&gt; month in both control and experimental groups, with no significant difference from baseline (0 ± 0). Gradual functional deterioration became evident after 3 months and a significant decline was observed at 5 months in both groups, with a greater reduction in the control group (72 ± 2.4) compared to the experimental group (105 ± 2.8) (&lt;i&gt;F&lt;/i&gt; = 367.9, &lt;i&gt;P&lt;/i&gt; &lt; 0.001). The decline was more pronounced in rats treated with 2 Gy per fraction than in those receiving 1.36 Gy per fraction. Locomotor activity declined progressively after irradiation in both groups. Baseline activity was 388.8 (control group) and 369.7 (experimental group). At 1 month, the control group showed a significantly greater reduction (180.2 ± 8.2) than the experimental group (272.5 ± 12.1), with a significant group and time interaction (&lt;i&gt;F&lt;/i&gt; = 110.4, &lt;i&gt;P&lt;/i&gt; &lt; 0.001). This difference persisted at 3 months (control group: 130.2 ± 3.6; experimental group: 185.4 ± 4.8; &lt;i&gt;F&lt;/i&gt; = 208.7, &lt;i&gt;P&lt;/i&gt; &lt; 0.001) and was most pronounced at 5 months (control group: 99.5 ± 1.7; experimental group: 147.4 ± 4.0; &lt;i&gt;F&lt;/i&gt; = 610.9, &lt;i&gt;P&lt;/i&gt; &lt; 0.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrates that fractionated irradiation of the spinal cord at 1.36 Gy per fraction, as commonly delivered during VMAT SIB, is less damaging in rats compared to the conventional 2 Gy per fraction used in three-dimensional conformal radiotherapy. Motor co-ordination and locomotor activity deterioration were more pronounced in the control group than in the experimental group. These findings suggest that","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 2","pages":"387-393"},"PeriodicalIF":1.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13345021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413708","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":"Magnetic Resonance Imaging-based Prostate Cancer Diagnosis Using Principal Component Analysis and Machine Learning Algorithms.","authors":"N Samta, C S Sureka, Sivananthan Sarasanandarajah, R K Jeevanram","doi":"10.4103/jmp.jmp_32_26","DOIUrl":"10.4103/jmp.jmp_32_26","url":null,"abstract":"<p><strong>Purpose: </strong>Prostate cancer is a leading cause of cancer-related mortality among men, necessitating accurate and early diagnostic methods. This study evaluates the effectiveness of principal component analysis (PCA) combined with machine learning (ML) for classifying benign and malignant prostate lesions using magnetic resonance imaging (MRI).</p><p><strong>Materials and methods: </strong>MRI data from 26 biopsy-confirmed prostate cancer patients were obtained from The Cancer Imaging Archive. Preprocessing included resizing and intensity normalization of dynamic contrast-enhanced MRI images. PCA was applied to extract dominant features capturing key tissue variations. A comparative analysis was performed using three feature representation strategies: raw pixel-based features, PCA-transformed features, and radiomics-based features. The extracted features were classified using Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) classifiers.</p><p><strong>Results: </strong>PCA-based features demonstrated separation between benign and malignant tissue regions. Statistical analysis using an independent two-sample <i>t</i>-test (Welch's correction) revealed a highly significant difference in mean PC1 values between the two groups (<i>P</i> < 2.2 × 10^⁻16). Among the classifiers, RF achieved the highest performance, followed by SVM and KNN, as confirmed by receiver operating characteristic analysis (area under the curve [AUC] = 0.909, 0.805, and 0.785, respectively), where higher AUC values indicate the model's ability to distinguish between benign and malignant lesions. Comparative evaluation indicated that PCA-based models outperformed raw pixel-based approaches, while radiomics-based features yielded additional gains.</p><p><strong>Conclusion: </strong>PCA-based feature extraction may enhance classification performance by reducing dimensionality and noise in MRI data. The proposed PCA-ML framework offers an interpretable, efficient, and clinically relevant approach for prostate cancer diagnosis.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 2","pages":"314-325"},"PeriodicalIF":1.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13345050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413777","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":"Impact of Grid Size on Dose Calculations for Cranial Tumors.","authors":"Nayron Medeiros Soares, Maria José Almada, José Alejandro Rojas López, Carlos Daniel Venencia","doi":"10.4103/jmp.jmp_6_26","DOIUrl":"10.4103/jmp.jmp_6_26","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to evaluate the impact of calculation grid size on dose calculations for multiple cranial tumor types using Monte Carlo (MC) calculation, comparing different grid settings.</p><p><strong>Methods: </strong>This cross-sectional study optimized 110 treatment plans using Elements Cranial and included arteriovenous malformation (AVM), glioblastoma (GBM), meningioma (MGM), brain metastasis (BM), pituitary adenoma (PA), and vestibular schwannoma (VS). Dose coverage at D98%, D95%, and D2%, along with mean dose, were assessed, and other relevant planning and anatomical parameters were analyzed.</p><p><strong>Results: </strong>GBM had the largest gross tumor volume and planning target volume (PTV) volumes, while VS had the smallest volumes and the highest PTV-bone intersection. For MC, grid size variations (1%/1 mm vs. 2%2 mm) did not significantly affect AVM, GBM, or MGM. However, statistically significant dose reductions were observed for BM, PA, and VS. When adjusted for PTV volume and bone intersection, median dose values decreased with the 2%/2 mm grid compared to the 1%/1 mm grid: D98% (25.60 Gy vs. 25.27 Gy, adjusted <i>P</i> = 0.026), D95% (27.05 Gy vs. 26.14 Gy, adjusted <i>P</i> = 0.005), and D2% (30.48 Gy vs. 30.00 Gy, adjusted <i>P</i> < 0.001). Application of the 2%/2 mm grid resulted in increased conformity index (CI) and gradient index (GI) values, with the effect being more pronounced in smaller or anatomically irregular targets, such as VS and PA.</p><p><strong>Conclusions: </strong>Grid size impacted dose calculations for small or irregular targets such as BM, PA, and VS. The 2%/2 mm grid led to significant dose reductions and increased CI and GI values.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 2","pages":"277-284"},"PeriodicalIF":1.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13344925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413810","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":"Three-dimensional Hierarchically Dense U-net Architecture for Knowledge-based Dose Prediction in Head and Neck Radiotherapy.","authors":"Naser Mahdavi, Mojtaba Shamsaei Zafarghandi, Saeed Setayeshi, Hassan Ali Nedaie","doi":"10.4103/jmp.jmp_311_25","DOIUrl":"10.4103/jmp.jmp_311_25","url":null,"abstract":"<p><strong>Purpose: </strong>Our objective is to investigate a novel knowledge-based planning model that utilizes the newly developed dense U-Net architecture to predict three-dimensional (3D) dose distributions in patients with head and neck cancer.</p><p><strong>Materials and methods: </strong>We utilized data published by the American Association of Physicists in Medicine Institute, which includes treatment plans for 340 patients with head and neck cancer who were treated using intensity modulated radiotherapy treatment with a prescribed dose of 70 Gy. The data were divided into three subsets, and the proposed dense U-Net was applied to predict full volumetric dose distributions. Model performance was evaluated using the mean absolute error (MAE) between predicted and clinical doses across all subsets.</p><p><strong>Results: </strong>The MAE between the reference and predicted dose distributions for the training, validation, and testing datasets was 1.60 Gy, 3.09 Gy, and 3.14 Gy, respectively. The mean absolute dose error was measured at 1.53 Gy in the brainstem, 3.43 Gy in the left parotid, 3.29 Gy in the right parotid, 2.05 Gy in the spinal cord, 2.40 Gy in the esophagus, 3.81 Gy in the mandible, 3.35 Gy in the larynx, 1.56 Gy in planning target volume (PTV70), 1.82 Gy in PTV63, 1.91 Gy in PTV56, and 3.14 Gy in the body contour for the testing data. The proposed framework enabled rapid generation of complete 3D dose distributions for each patient's plan in a few seconds, demonstrating strong potential to accelerate clinical workflows.</p><p><strong>Conclusion: </strong>The proposed dense U-Net model demonstrated proficiency in accurately predicting dose distributions for the head and neck region, ensuring consistent quality.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 2","pages":"255-264"},"PeriodicalIF":1.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13345029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413836","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":"Influence of Setup Uncertainties: Optimizing Clinical Target Volume-to-planning Target Volume Margins in Total Marrow and Lymphoid Irradiation.","authors":"Henry Finlay Godson, Swathi Bapani, Tarun Jose, Retna Ponmalar, Ebenezer Suman Babu, Patricia Sebastian, Selvamani Backianathan, Rajesh Balakrishnan","doi":"10.4103/jmp.jmp_48_26","DOIUrl":"10.4103/jmp.jmp_48_26","url":null,"abstract":"<p><strong>Background: </strong>Total Marrow and Lymphoid Irradiation (TMLI) is an advanced alternative to traditional Total Body Irradiation, aiming to reduce toxicities while maintaining treatment efficacy.</p><p><strong>Aim: </strong>This study evaluates setup uncertainties and their impact on clinical target volume (CTV) to planning target volume (PTV) in patients undergoing Total Marrow and Lymphoid Irradiation using a linear accelerator-based volumetric modulated arc therapy (VMAT).</p><p><strong>Materials and methods: </strong>Setup deviations were analyzed for 15 patients using cone-beam CT image guidance across multiple isocenters. Systematic and random errors were computed for each anatomical region, and CTV-to-PTV margins were estimated using ICRU 62, Stroom's, and van Herk's formulas. Average shifts and 3D displacement vectors were quantified across the skull, thorax, abdomen, pelvis, and extremities.</p><p><strong>Results: </strong>Setup uncertainties were site dependent, with the thorax showing the largest systematic error (3.9 mm) and the leg the largest random error (2.6 mm). The mean 3D displacement vector ranged from 3.3 mm (pelvis) to 6.8 mm (leg). Based on systematic and random error analysis, differential PTV margins of 3 mm, for skull, 5 mm for thorax, abdomen and pelvis, and 10 mm for extremities were determined to maintain adequate dose coverage.</p><p><strong>Conclusion: </strong>VMAT-based TMLI requires region-specific margins to account for setup uncertainties, particularly in extremities. Image-guided adaptive strategies can optimize target coverage while minimizing normal tissue exposure. Precise image guidance and tailored margins are crucial for maximizing treatment efficacy and minimizing side effects in TMLI.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 2","pages":"285-294"},"PeriodicalIF":1.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13345114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413813","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":"Design and Implementation of a No-code Graphical User Interface Application for Radiomics Analysis.","authors":"Koshi Hasegawa, Hayato Saito, Naiki Sato, Ryohei Fukui","doi":"10.4103/jmp.jmp_204_25","DOIUrl":"10.4103/jmp.jmp_204_25","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to develop an integrated application that enables the intuitive operation of each radiomics analysis process (image input, feature extraction, dimensionality reduction, model construction, and evaluation) with a graphical user interface (GUI) and no code.</p><p><strong>Methods: </strong>Tkinter, a Python standard library, was used to design the GUI. Libraries such as PyRadiomics, scikit-learn, and pydicom were integrated into the GUI for image loading and feature analysis. Feature extraction and selection using the least absolute shrinkage and selection operator, principal component analysis, multiple regression analysis, and linear discriminant analysis-receiver operating characteristic (ROC) analysis were available. Visualization and comma-separated values format outputs were also supported. The two datasets from The Cancer Imaging Archive were used for validation. Additionally, we compared the analysis time with that of conventional methods (e.g., PyRadiomics, RStudio, and Excel).</p><p><strong>Results: </strong>Using the developed application, we were able to reduce the analysis time from approximately 20 min to <1/3 of the 6 min required by the conventional method. Additionally, diagnostic support information was obtained through accuracy evaluation, correlation visualization of features, and ROC analysis.</p><p><strong>Conclusion: </strong>This application improves the efficiency and availability of radiomics analysis and can be easily used by researchers and clinicians with little programming experience. Further enhancements are planned in the future, including the integration of segmentation functions, automatic parameter optimization, and application to other modalities and diseases.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 2","pages":"394-402"},"PeriodicalIF":1.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13345025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413819","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":"Bridging Precision and Practice: A Systematic Approach to Total Body Irradiation in Routine Clinical Settings.","authors":"Karthikeyan Nithiyanantham, Sambasivaselli Raju, Saurabha Kumar","doi":"10.4103/jmp.jmp_313_25","DOIUrl":"10.4103/jmp.jmp_313_25","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the feasibility of delivering total body irradiation (TBI) in a standard linear accelerator bunker using a treatment planning system (TPS) and a conventional multileaf collimator assembly. The aim was to address dose homogeneity and lung shielding within practical clinical constraints.</p><p><strong>Materials and methods: </strong>TBI was performed using conventional anterior-posterior beam delivery, dividing the whole-body irradiation into three to four sections with patients positioned on the treatment couch. Whole-body computed tomography images, acquired with Vac-Loc immobilization, were used for planning in the TPS. The source-to-surface distance was maintained between 120 and 135 cm. The moving-junction technique provided uniformity at section junctions, and the multileaf collimator (MLC) was employed for customized lung shielding. Patient setup and shield positioning were verified with an electronic portal imaging device.</p><p><strong>Results: </strong>The plan resulted in ≥ 92% of the target volume received ≥ 90% of the prescribed dose while V110 remained <7%. The mean lung dose was kept below 8 Gy. The prescribed dose rate at the mid-plane was approximately 1.8 Gy/min. Point dose measurements showed agreement with the TPS calculations within 2%, indicating high dosimetric accuracy and reliable dose delivery.</p><p><strong>Conclusions: </strong>Conventional linac setups, combined with TPS-guided planning, MLC-based lung shielding, and moving-junction delivery approaches, provide a practical solution for TBI with acceptable dose uniformity and lung sparing, supporting implementation in standard radiation oncology practice.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 2","pages":"341-347"},"PeriodicalIF":1.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13345105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413740","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":"Comparative Study on Effective Dose and Cancer Risk in Dual-tracer Hybrid Imaging - International Commission on Radiological Protection and Personalized Models.","authors":"Mehrnoosh Karimipourfard, Hojjat Mahani, Sedigheh Sina, Masood Shobeiry, Chai Hong Yeong","doi":"10.4103/jmp.jmp_306_25","DOIUrl":"10.4103/jmp.jmp_306_25","url":null,"abstract":"<p><strong>Purpose: </strong>Dual-tracer hybrid imaging is used for concurrent evaluation of two distinct molecular biomarkers, enhancing diagnostic capabilities. However, concerns about increased radiation exposure to patients arise with this technique. This study investigates the representative radiation doses and cancer risks associated with dual-tracer imaging and explores personalized dosimetry approaches.</p><p><strong>Materials and methods: </strong>A literature-based protocol identification was performed to obtain the required dosimetric inputs. Dose coefficients were applied to convert tracer activities into effective dose estimates. Internal dose assessments using reference phantom models were compared with personalized dosimetry results obtained through GATE-based Monte Carlo simulations specific to dual-tracer positron emission tomography. In addition, complementary computed tomography (CT) imaging was performed, and cancer risks were quantified using the BEIR VII risk model.</p><p><strong>Results: </strong>Whole-body CT dose estimates closely matched those from the MIRDct tool, with a maximum discrepancy of 9.2%. Among the tracers evaluated, ²⁰¹Tl resulted in the highest effective dose and cancer risk, while ^81mKr and ¹³N-ammonia exhibited the lowest doses. Personalized dosimetry for dual-tracer imaging with ^¹⁸F-fludeoxyglucose (FDG) and ⁶⁸Ga-PSMA showed a maximum deviation of 5.6% from phantom-based models. The total effective dose reached up to 34 mSv.</p><p><strong>Conclusions: </strong>The study emphasizes the importance of addressing increased radiation doses and harmonizing dual-tracer hybrid imaging protocols. Personalized dosimetry enhances patient-specific risk assessments and safety by providing more accurate estimates of radiation exposure and associated cancer risks.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"51 2","pages":"430-439"},"PeriodicalIF":1.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13345051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413700","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}