EGSnrc application for IMRT planning.

The aim of this study was to describe a detailed instruction of intensity modulated radiotherapy (IMRT) planning simulation using BEAMnrc-DOSXYZnrc code system (EGSnrc package) and present a new graphical user interface based on MATLAB code (The MathWorks) to combine more than one. 3ddose file which were obtained from the IMRT plan. This study was performed in four phases: the commissioning of Varian Clinac iX6 MV, the simulation of IMRT planning in EGSnrc, the creation of in-house VDOSE GUI, and the analysis of the isodose contour and dose volume histogram (DVH) curve from several beam angles. The plan paramaters in sequence and control point files were extracted from the planning data in Tan Tock Seng Hospital Singapore (multileaf collimator (MLC) leaf positions - bank A and bank B, gantry angles, coordinate of isocenters, and MU indexes). VDOSE GUI which was created in this study can display the distribution dose curve in each slice and beam angle. Dose distributions from various MLC settings and beam angles yield different dose distributions even though they used the same number of simulated particles. This was due to the differences in the MLC leaf openings in every field. The value of the relative dose error between the two dose ditributions for "body" was 51.23 %. The Monte Carlo (MC) data was normalized with the maximum dose but the analytical anisotropic algorithm (AAA) data was normalized by the dose in the isocenter. In this study, we have presented a Monte Carlo simulation framework for IMRT dose calculation using DOSXYZnrc source 21. Further studies are needed in conducting IMRT simulations using EGSnrc to minimize the different dose error and dose volume histogram deviation.

Dosimetry Evaluation of Treatment Planning Systems in Patient-Specific 3D Printed Anthropomorphic Phantom for Breast Cancer after Mastectomy using a Single-Beam 3D-CRT Technique for Megavoltage Electron Radiation Therapy.

Background: The patient-specific 3D printed anthropomorphic phantom is used for breast cancer after mastectomy developed by the laboratory of medical physics and biophysics, Department of Physics, Institut Teknologi Sepuluh Nopember, Indonesia. This phantom is applied to simulate and measure the radiation interactions occurring in the human body either using the treatment planning system (TPS) or direct measurement with external beam therapy (EBT) 3 film. Objective: This study aimed to provide dose measurements in the patient-specific 3D printed anthropomorphic phantom using a TPS and direct measurements using single-beam three-dimensional conformal radiation therapy (3DCRT) technique with electron energy of 6 MeV. Material and Methods: In this experimental study, the patient-specific 3D printed anthropomorphic phantom was used for post-mastectomy radiation therapy. TPS on the phantom was conducted using a 3D-CRT technique with RayPlan 9A software. The single-beam radiation was delivered to the phantom with an angle perpendicular to the breast plane at 337.3° at 6 MeV with a total prescribed dose of 5000 cGy/25 fractions with 200 cGy per fraction. Results: The doses at planning target volume (PTV) and right lung confirmed a non-significant difference both for TPS and direct measurement with P-values of 0.074 and 0.143, respectively. The dose at the spinal cord showed statistically significant differences with a P-value of 0.002. The result presented a similar skin dose value using either TPS or direct measurement. Conclusion: The patient-specific 3D printed anthropomorphic phantom for breast cancer after mastectomy on the right side has good potential as an alternative to the evaluation of dosimetry for radiation therapy.

Automated classification of urinary stones based on microcomputed tomography images using convolutional neural network.

PURPOSE: The classification of urinary stones is important prior to treatment because the treatments depend on three types of urinary stones, i.e., calcium, uric acid, and mixture stones. We have developed an automatic approach for the classification of urinary stones into the three types based on microcomputed tomography (micro-CT) images using a convolutional neural network (CNN). MATERIALS AND METHODS: Thirty urinary stones from different patients were scanned in vitro using micro-CT (pixel size: 14.96 µm; slice thickness: 15 µm); a total of 2,430 images (micro-CT slices) were produced. The slices (227 × 227 pixels) were classified into the three categories based on their energy dispersive X-ray (EDX) spectra obtained via scanning electron microscopy (SEM). The images of urinary stones from each category were divided into three parts; 66%, 17%, and 17% of the dataset were assigned to the training, validation, and test datasets, respectively. The CNN model with 15 layers was assessed based on validation accuracy for the optimization of hyperparameters such as batch size, learning rate, and number of epochs with different optimizers. Then, the model with the optimized hyperparameters was evaluated for the test dataset to obtain classification accuracy and error. RESULTS: The validation accuracy of the developed approach with CNN with optimized hyperparameters was 0.9852. The trained CNN model achieved a test accuracy of 0.9959 with a classification error of 1.2%. CONCLUSIONS: The proposed automated CNN-based approach could successfully classify urinary stones into three types, namely calcium, uric acid, and mixture stones, using micro-CT images.

Computational analysis of interfractional anisotropic shape variations of the rectum in prostate cancer radiation therapy.

PURPOSE: To analyze the uncertainties of the rectum due to anisotropic shape variations by using a statistical point distribution model (PDM). MATERIALS AND METHODS: The PDM was applied to the rectum contours that were delineated on planning computed tomography (CT) and cone-beam CT (CBCT) at 80 fractions of 11 patients. The standard deviations (SDs) of systematic and random errors of the shape variations of the whole rectum and the region in which the rectum overlapped with the PTV (ROP regions) were derived from the PDMs at all fractions of each patient. The systematic error was derived by using the PDMs of planning and average rectum surface determined from rectum surfaces at all fractions, while the random error was derived by using a PDM-based covariance matrix at all fractions of each patient. RESULTS: Regarding whole rectum, the population SDs were larger than 1.0 mm along all directions for random error, and along the anterior, superior, and inferior directions for systematic error. The deviation is largest along the superior and inferior directions for systematic and random errors, respectively. For ROP regions, the population SDs of systematic error were larger than 1.0 mm along the superior and inferior directions. The population SDs of random error for the ROP regions were larger than 1.0 mm except along the right and posterior directions. CONCLUSIONS: The anisotropic shape variations of the rectum, especially in the ROP regions, should be considered when determining a planning risk volume (PRV) margins for the rectum associated with the acute toxicities.

Multidimensional Image Analysis for High Precision Radiation Therapy.

High precision radiation therapy (HPRT) has been improved by utilizing conventional image engineering technologies. However, different frameworks are necessary for further improvement of HPRT. This review paper attempted to define the multidimensional image and what multidimensional image analysis is, which may be feasible for increasing the accuracy of HPRT. A number of researches in radiation therapy field have been introduced to understand the multidimensional image analysis. Multidimensional image analysis could greatly assist clinical staffs in radiation therapy planning, treatment, and prediction of treatment outcomes.