Journal of Medical Physics最新文献

{"title":"Investigating the Dosimetric Leaf Gap Correction Factor of Mobius3D Dose Calculation for Volumetric-modulated Arc Radiotherapy Plans.","authors":"Thitipong Sawapabmongkon, Pimolpun Changkaew, Chanon Puttanawarut, Puangpen Tangboonduangjit, Suphalak Khachonkham","doi":"10.4103/jmp.jmp_11_24","DOIUrl":"10.4103/jmp.jmp_11_24","url":null,"abstract":"<p><strong>Aims: </strong>The dosimetric leaf gap (DLG) is a parameter for correcting radiation transmission through the round leaf end of multileaf collimators. The purpose of this study was to determine and investigate the optimal DLG correction factor for 6 MV volumetric-modulated arc radiotherapy (VMAT) plan dose calculations in Mobius3D.</p><p><strong>Materials and methods: </strong>Seventeen VMAT plans were selected for the DLG correction factor optimization process. The optimal DLG correction factor was defined as the minimum difference between the measured dose and the Mobius3D-calculated dose on the Mobius Verification Phantom™ with different DLG correction factors. Subsequently, the optimal DLG correction factor was applied for Mobius3D dose calculation, and accuracy was assessed by comparing the measured and calculated doses. For verification and validation, the 17 previous plans and 10 newly selected plans underwent Mobius3D calculations with the optimal DLG correction factor, and gamma analysis was performed to compare them to the treatment planning system (TPS). Gamma analysis was also performed between the electronic portal imaging device (EPID) and the TPS for cross-comparison between systems.</p><p><strong>Results: </strong>The DLG correction factor was optimized to -1.252, which reduced the average percentage differences between measured and Mobius3D-calculated doses from 2.23% ±1.21% to 0.03% ±1.82%. The cross-comparison between Mobius3D/TPS and EPID/TPS revealed a similar trend in gamma passing rate (>95%) in both the verification and validation plans.</p><p><strong>Conclusion: </strong>The DLG correction factor strongly influences the accuracy of Mobius3D-calculated doses. Applying the optimal DLG correction factor can increase dose agreement and gamma passing rate between calculation and delivered doses of VMAT plans, which emphasizes the importance of optimizing this factor during the commissioning process.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"49 2","pages":"261-269"},"PeriodicalIF":0.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11309132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918081","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":"Performance Evaluation of Deformable Image Registration Systems - SmartAdapt^® and Velocity™.","authors":"M Anil Kumar, Raghavendra Hajare, Bhakti Dev Nath, K K Sree Lakshmi, Umesh M Mahantshetty","doi":"10.4103/jmp.jmp_167_23","DOIUrl":"10.4103/jmp.jmp_167_23","url":null,"abstract":"<p><strong>Aim: </strong>To commission and validate commercial deformable image registration (DIR) systems (SmartAdapt^® and Velocity™) using task group 132 (TG-132) digital phantom datasets. Additionally, the study compares and verifies the DIR algorithms of the two systems.</p><p><strong>Materials and methods: </strong>TG-132 digital phantoms were obtained from the American Association of Physicists in Medicine website and imported into SmartAdapt^® and Velocity™ systems for commissioning and validation. The registration results were compared with known shifts using rigid registrations and deformable registrations. Virtual head and neck phantoms obtained online (DIR Evaluation Project) and some selected clinical data sets from the department were imported into the two DIR systems. For both of these datasets, DIR was carried out between the source and target images, and the contours were then propagated from the source to the target image data set. The dice similarity coefficient (DSC), mean distance to agreement (MDA), and Jacobian determinant measures were utilised to evaluate the registration results.</p><p><strong>Results: </strong>The recommended criteria for commissioning and validation of DIR system from TG-132 was error <0.5*voxel dimension (vd). Translation only registration: Both systems met TG-132 recommendations except computed tomography (CT)-positron emission tomography registration in both systems (Velocity ~1.1*vd, SmartAdapt ~1.6*vd). Translational and rotational registration: Both systems failed the criteria for all modalities (For velocity, error ranged from 0.6*vd [CT-CT registration] to 3.4*vd [CT-cone-beam CT (CBCT) registration]. For SmartAdapt^® the range was 0.6*vd [CT-CBCT] to 3.6*vd [CT-CT]). Mean ± standard deviation for DSC, MDA and Jacobian metrics were used to compare the DIR results between SmartAdapt^® and Velocity™.</p><p><strong>Conclusion: </strong>The DIR algorithms of SmartAdapt^® and Velocity™ were commissioned and their deformation results were compared. Both systems can be used for clinical purpose. While there were only minimal differences between the two systems, Velocity™ provided lower values for parotids, bladder, rectum, and prostate (soft tissue) compared to SmartAdapt. However, for mandible, spinal cord, and femoral heads (rigid structures), both systems showed nearly identical results.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"49 2","pages":"240-249"},"PeriodicalIF":0.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11309134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918084","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":"Validation and Efficiency Evaluation of Automated Quality Assurance Software SunCHECK™ Machine for Mechanical and Dosimetric Quality Assurance.","authors":"Mayank Dhoundiyal, Sachin Rasal, Ajinkya Gupte, Prasad Raj Dandekar, Ananda Jadhav, Omkar Awate","doi":"10.4103/jmp.jmp_158_23","DOIUrl":"10.4103/jmp.jmp_158_23","url":null,"abstract":"<p><p>Recent decades have witnessed transformative advances in radiation physics and computer technology, revolutionizing the precision of radiation therapy. The adoption of intricate treatment techniques such as three-dimensional conformal radiotherapy, intensity-modulated radiotherapy, volumetric-modulated arc therapy, and image-guided radiotherapy necessitates robust quality assurance (QA) programs. This study introduces the SunCHECK™ Machine (SCM), a web-based QA platform, presenting early results from its integration into a comprehensive QA program. linear accelerators (LINAC) demand QA programs to uphold machine characteristics within accepted tolerances. The increasing treatment complexity underscores the need for streamlined procedures. The selection of QA tools is vital, requiring efficiency, accuracy, and alignment with clinic needs, as per recommendations such as the AAPM task group 142 report. The materials and methods section details SCM implementation in various QA aspects, encompassing daily QA (DQA), imaging QA with Catphan, conventional output assessment with a water phantom, and LINAC isocenter verification through the Winston-Lutz test. Challenges in QA processes, such as manual data transcription and limited device integration, are highlighted. Early results demonstrate SCM's significant reduction in QA time, ensuring accuracy and efficiency. Its automation eliminates interobserver variation and human errors, contributing to time savings and near-immediate result publication. SCM's role in consolidating and storing DQA data within a single platform is emphasized, offering potential in resource optimization, especially in resource-limited settings. In conclusion, SCM shows promise for efficient and accurate mechanical and dosimetric QA in radiation therapy. The study underscores SCM's potential to address contemporary QA challenges, contributing to improved resource utilization without compromising quality and safety standards.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"49 2","pages":"311-315"},"PeriodicalIF":0.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11309146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918086","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":"Analysis of Hybrid Feature Optimization Techniques Based on the Classification Accuracy of Brain Tumor Regions Using Machine Learning and Further Evaluation Based on the Institute Test Data.","authors":"Soniya Pal, Raj Pal Singh, Anuj Kumar","doi":"10.4103/jmp.jmp_77_23","DOIUrl":"10.4103/jmp.jmp_77_23","url":null,"abstract":"<p><strong>Aim: </strong>The goal of this study was to get optimal brain tumor features from magnetic resonance imaging (MRI) images and classify them based on the three groups of the tumor region: Peritumoral edema, enhancing-core, and necrotic tumor core, using machine learning classification models.</p><p><strong>Materials and methods: </strong>This study's dataset was obtained from the multimodal brain tumor segmentation challenge. A total of 599 brain MRI studies were employed, all in neuroimaging informatics technology initiative format. The dataset was divided into training, validation, and testing subsets online test dataset (OTD). The dataset includes four types of MRI series, which were combined together and processed for intensity normalization using contrast limited adaptive histogram equalization methodology. To extract radiomics features, a python-based library called pyRadiomics was employed. Particle-swarm optimization (PSO) with varying inertia weights was used for feature optimization. Inertia weight with a linearly decreasing strategy (W1), inertia weight with a nonlinear coefficient decreasing strategy (W2), and inertia weight with a logarithmic strategy (W3) were different strategies used to vary the inertia weight for feature optimization in PSO. These selected features were further optimized using the principal component analysis (PCA) method to further reducing the dimensionality and removing the noise and improve the performance and efficiency of subsequent algorithms. Support vector machine (SVM), light gradient boosting (LGB), and extreme gradient boosting (XGB) machine learning classification algorithms were utilized for the classification of images into different tumor regions using optimized features. The proposed method was also tested on institute test data (ITD) for a total of 30 patient images.</p><p><strong>Results: </strong>For OTD test dataset, the classification accuracy of SVM was 0.989, for the LGB model (LGBM) was 0.992, and for the XGB model (XGBM) was 0.994, using the varying inertia weight-PSO optimization method and the classification accuracy of SVM was 0.996 for the LGBM was 0.998, and for the XGBM was 0.994, using PSO and PCA-a hybrid optimization technique. For ITD test dataset, the classification accuracy of SVM was 0.994 for the LGBM was 0.993, and for the XGBM was 0.997, using the hybrid optimization technique.</p><p><strong>Conclusion: </strong>The results suggest that the proposed method can be used to classify a brain tumor as used in this study to classify the tumor region into three groups: Peritumoral edema, enhancing-core, and necrotic tumor core. This was done by extracting the different features of the tumor, such as its shape, grey level, gray-level co-occurrence matrix, etc., and then choosing the best features using hybrid optimal feature selection techniques. This was done without much human expertise and in much less time than it would take a person.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"49 1","pages":"22-32"},"PeriodicalIF":0.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200803","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":"Improvement of Whole-body Bone Planar Images on a Bone-dedicated Single-photon Emission Computed Tomography Scanner by Blind Deconvolution Algorithm.","authors":"Zhexin Wang, Hui Liu, Li Cheng, Zhenlei Lyu, Lilei Gao, Nianming Jiang, Zuoxiang He, Yaqiang Liu","doi":"10.4103/jmp.jmp_127_23","DOIUrl":"10.4103/jmp.jmp_127_23","url":null,"abstract":"<p><strong>Purpose: </strong>We have developed a bone-dedicated collimator with higher sensitivity but slightly degraded resolution on single-photon emission computed tomography (SPECT) for planar bone scintigraphy, compared with conventional low-energy high-resolution collimator. In this work, we investigated the feasibility of using the blind deconvolution algorithm to improve the resolution of planar images on bone scintigraphy.</p><p><strong>Materials and methods: </strong>Monte Carlo simulation was performed with the NCAT phantom for modeling bone scintigraphy on the clinical dual-head SPECT scanner (Imagine NET 632, Beijing Novel Medical Equipment Ltd.) equipped with the bone-dedicated collimator. Maximum likelihood estimation method was used for the blind deconvolution algorithm. The initial estimation of point spread function (PSF) and iteration number for the method were determined by comparing the deblurred images obtained from different input parameters. We simulated different tumors in five different locations and with five different diameters to evaluate the robustness of the initial inputs. Furthermore, we performed chest phantom studies on the clinical SPECT scanner. The quantified increased contrast ratio (CR) between the tumor and the background was evaluated.</p><p><strong>Results: </strong>The 2 mm PSF kernel and 10 iterations provided a practical and robust deblurred image on our system. Those two inputs can generate robust deblurred images in terms of the tumor location and size with an average increased CR of 21.6%. The phantom studies also demonstrated the ability of blind deconvolution, using those two inputs, with increased CRs of 17%, 17%, 22%, 20%, and 13% for lesions with diameters of 1 cm, 2 cm, 3 cm, 4 cm, and 5 cm, respectively.</p><p><strong>Conclusions: </strong>It is feasible to use the blind deconvolution algorithm to deblur the planar images for SPECT bone scintigraphy. The appropriate values of the PSF kernel and the iteration number for the blind deconvolution can be determined using simulation studies.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"49 1","pages":"110-119"},"PeriodicalIF":0.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201147","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":"Novel Artificial Intelligence Tool for Real-time Patient Identification to Prevent Misidentification in Health Care.","authors":"Shriram Rajurkar, Teerthraj Verma, S P Mishra, Mlb Bhatt","doi":"10.4103/jmp.jmp_106_23","DOIUrl":"10.4103/jmp.jmp_106_23","url":null,"abstract":"<p><strong>Purpose: </strong>Errors in the identification of true patients in a health-care facility may result in the wrong dose or dosage being given to the wrong patient at the wrong site during radiotherapy sessions, radiopharmaceutical administration, radiological scans, etc. The aim of this article is to reduce the error in the identification of correct patients by implementation of the Python deep learning-based real-time patient identification program.</p><p><strong>Materials and methods: </strong>The authors utilized and installed Anaconda Prompt (miniconda 3), Python (version 3.9.12), and Visual Studio Code (version 1.71.0) for the design of the patient identification program. In the field of view, the area of interest is merely face detection. The overall performance of the developed program is accomplished over three steps, namely image data collection, data transfer, and data analysis, respectively. The patient identification tool was developed using the OpenCV library for face recognition.</p><p><strong>Results: </strong>This program provides real-time patient identification information, together with the other preset parameters such as disease site, with a precision of 0.92%, recall rate of 0.80%, and specificity of 0.90%. Furthermore, the accuracy of the program was found to be 0.84%. The output of the in-house developed program as \"Unknown\" is provided if a patient's relative or an unknown person is found in restricted region.</p><p><strong>Interpretation and conclusions: </strong>This Python-based program is beneficial for confirming the patient's identity, without manual interventions, just before therapy, administering medications, and starting other medical procedures, among other things, to prevent unintended medical and health-related complications that may arise as a result of misidentification.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"49 1","pages":"41-48"},"PeriodicalIF":0.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201159","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":"Dosimetric Evaluation of Semiflex Three-dimensional Chamber under Unflatten Beam in Comparison among Different Detectors.","authors":"Kanakavel Kandasamy, E James Jebaseelan Samuel","doi":"10.4103/jmp.jmp_115_23","DOIUrl":"10.4103/jmp.jmp_115_23","url":null,"abstract":"<p><strong>Purpose: </strong>The goal of this study is to investigate the dosimetric properties of a Semiflex three-dimensional (3D) chamber in an unflatten beam and compare its data from a small to a large field flattening filter-free (FFF) beam with different radiation detectors.</p><p><strong>Methods: </strong>The sensitivity, linearity, reproducibility, dose rate dependency, and energy dependence of a Semiflex 3D detector in flattening filter and filter-free beam were fully investigated. The minimum radiation observed field widths for all detectors were calculated using lateral electronic charged particle equilibrium to investigate dosimetric characteristics such as percentage depth doses (PDDs), profiles, and output factors (OPFs) for Semiflex 3D detector under 6FFF Beam. The Semiflex 3D measured data were compared to that of other detectors employed in this study.</p><p><strong>Results: </strong>The ion chamber has a dosage linearity deviation of +1.2% for <10 MU, a dose-rate dependency deviation of +0.5%, and significantly poorer sensitivity due to its small volume. There is a difference in field sizes between manufacturer specs and derived field sizes. The measured PDD, profiles, and OPFs of the Semiflex 3D chamber were within 1% of each other for all square field sizes set under linac for the 6FFF beam.</p><p><strong>Conclusion: </strong>It was discovered to be an appropriate detector for relative dose measurements for 6 FFF beams with higher dose rates for field sizes more than or equal to 3 cm × 3 cm.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"49 1","pages":"84-94"},"PeriodicalIF":0.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201707","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":"Deep Learning-based Lung dose Prediction Using Chest X-ray Images in Non-small Cell Lung Cancer Radiotherapy.","authors":"Takahiro Aoyama, Hidetoshi Shimizu, Yutaro Koide, Hidemi Kamezawa, Jun-Ichi Fukunaga, Tomoki Kitagawa, Hiroyuki Tachibana, Kojiro Suzuki, Takeshi Kodaira","doi":"10.4103/jmp.jmp_122_23","DOIUrl":"10.4103/jmp.jmp_122_23","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to develop a deep learning model for the prediction of V₂₀ (the volume of the lung parenchyma that received ≥20 Gy) during intensity-modulated radiation therapy using chest X-ray images.</p><p><strong>Methods: </strong>The study utilized 91 chest X-ray images of patients with lung cancer acquired routinely during the admission workup. The prescription dose for the planning target volume was 60 Gy in 30 fractions. A convolutional neural network-based regression model was developed to predict V₂₀. To evaluate model performance, the coefficient of determination <i>(R</i>²), root mean square error (RMSE), and mean absolute error (MAE) were calculated with conducting a four-fold cross-validation method. The patient characteristics of the eligible data were treatment period (2018-2022) and V₂₀ (19.3%; 4.9%-30.7%).</p><p><strong>Results: </strong>The predictive results of the developed model for V₂₀ were 0.16, 5.4%, and 4.5% for the <i>R</i>², RMSE, and MAE, respectively. The median error was -1.8% (range, -13.0% to 9.2%). The Pearson correlation coefficient between the calculated and predicted V₂₀ values was 0.40. As a binary classifier with V₂₀ <20%, the model showed a sensitivity of 75.0%, specificity of 82.6%, diagnostic accuracy of 80.6%, and area under the receiver operator characteristic curve of 0.79.</p><p><strong>Conclusions: </strong>The proposed deep learning chest X-ray model can predict V₂₀ and play an important role in the early determination of patient treatment strategies.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"49 1","pages":"33-40"},"PeriodicalIF":0.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200913","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":"Laser Fragmentation of Green Tea-synthesized Silver Nanoparticles and Their Blood Toxicity: Effect of Laser Wavelength on Particle Diameters.","authors":"Ashraf M Alattar, Israa F Al-Sharuee, Jafer Fahdel Odah","doi":"10.4103/jmp.jmp_153_23","DOIUrl":"10.4103/jmp.jmp_153_23","url":null,"abstract":"<p><strong>Background: </strong>The efficacy of fractionation is significantly impacted by the colloidal particles' spontaneous absorption of laser beam radiation. The classification of silver nanoparticles during fragmentation processing is regulated through the collection of a combination of laser pulses with wavelengths of 1064 nm and 532 nm.</p><p><strong>Aims and objectives: </strong>This study presents an investigation of the efficacy of a plant extract in conjunction with the incorporation of supplementary silver nanoparticles, as well as the generation of smaller-sized silver nanoparticles using laser fragmentation.and then measure thier toxity on the blood.</p><p><strong>Results: </strong>Ag nanoparticles were synthesized using pulsed laser fragmentation on green tea AgNPs. The synthesis process involved the utilization of a Q-switch Nd:YAG laser with wavelengths of 1064 nm and 532 nm, with energy ranging from 200 to 1000 mJ. Initially, a silver nano colloid was synthesized through the process of fragmented of the Ag target using the second harmonic generation of 532 nm at various energy levels. The optimal energy within the selected wavelengths was determined in order to facilitate the ultimate comparison. Transmission electron microscopy (TEM) was used to determine surface morphology and average particle size, while a spectrophotometer was used to analyses UV light's spectrum characteristics. The measurements focused on the surface plasmon resonance (SPR) phenomenon. The absorption spectra of silver nanoparticles exhibit distinct and prominent peaks at wavelengths of 405 nm and 415 nm. The mean diameter of the silver nanoparticles was found to be 16 nm and 20 nm, corresponding to wavelengths of 1064 nm and 532 nm, respectively.</p><p><strong>Conclusion: </strong>As a consequence, there is a decrease in the range of particle sizes and a decrease in the mean size to lower magnitudes, resulting in a very stable colloid. This particular methodology has demonstrated considerable efficacy in the production of colloidal suspensions with the intended particle dimensions. Moreover, by the analysis of nanoparticles in human blood, no discernible alterations in the blood constituents were seen, indicating their non-toxic nature.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"49 1","pages":"95-102"},"PeriodicalIF":0.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201154","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":"Effect of Detector Orientation and Influence of Jaw Position in the Determination of Small-field Output Factor with Various Detectors for High-energy Photon Beams.","authors":"Henry Finlay Godson, Ravikumar Manickam, Y Retna Ponmalar, K M Ganesh, Sathiyan Saminathan, Varatharaj Chandraraj, A Sathish Kumar, Seby George, Arun Raman, Rabi Raja Singh","doi":"10.4103/jmp.jmp_148_23","DOIUrl":"10.4103/jmp.jmp_148_23","url":null,"abstract":"<p><strong>Background: </strong>Accurate dose measurements are difficult in small fields due to charge particle disequilibrium, partial source occlusion, steep dose gradient, and the finite size of the detector.</p><p><strong>Aim: </strong>The study aims to determine the output factor using various detectors oriented in parallel and perpendicular orientations for three different tertiary collimating systems using 15 MV photon beams. In addition, this study analyzes how the output factor could be affected by different configurations of X and Y jaws above the tertiary collimators.</p><p><strong>Materials and methods: </strong>Small field output factor measurements were carried out with three detectors for different tertiary collimating systems such as BrainLab stereotactic cones, BrainLab mMLC and Millennium MLC namely. To analyze the effect of jaw position on output factor, measurements have been carried out by positioning the jaws at the edge, 0.25, 0.5, and 1.0 cm away from the tertiary collimated field.</p><p><strong>Results: </strong>The data acquired with 15 MV photon beams show significant differences in output factor obtained with different detectors for all collimating systems. For smaller fields when compared to microDiamond, the SRS diode underestimates the output by up to -1.7% ± 0.8% and -2.1% ± 0.3%, and the pinpoint ion chamber underestimates the output by up to -8.1% ± 1.4% and -11.9% ± 1.9% in their parallel and perpendicular orientation respectively. A large increase in output factor was observed in the small field when the jaw was moved 0.25 cm symmetrically away from the tertiary collimated field.</p><p><strong>Conclusion: </strong>The investigated data on the effect of jaw position inferred that the position of the X and Y jaw highly influences the output factors of the small field. It also confirms that the output factor highly depends on the configuration of X and Y jaw settings, the tertiary collimating system as well as the orientation of the detectors in small fields.</p>","PeriodicalId":51719,"journal":{"name":"Journal of Medical Physics","volume":"49 1","pages":"73-83"},"PeriodicalIF":0.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201141","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}