RESUMEN
Background: Volumetric-modulated arc therapy (VMAT) is an efficient method of administering intensity-modulated radiotherapy beams. The Delta4 device was employed to examine patient data. Aims and Objectives: The utility of the Delta4 device in identifying errors for patient-specific quality assurance of VMAT plans was studied in this research. Materials and Methods: Intentional errors were purposely created in the collimator rotation, gantry rotation, multileaf collimator (MLC) position displacement, and increase in the number of monitor units (MU). Results: The results show that when the characteristics of the treatment plans were changed, the gamma passing rate (GPR) decreased. The largest percentage of erroneous detection was seen in the increasing number of MU, with a GPR ranging from 41 to 92. Gamma analysis was used to compare the dose distributions of the original and intentional error designs using the 2%/2 mm criteria. The percentage of dose errors (DEs) in the dose-volume histogram (DVH) was also analyzed, and the statistical association was assessed using logistic regression. A modest association (Pearson's R-values: 0.12-0.67) was seen between the DE and GPR in all intentional plans. The findings indicated a moderate association between DVH and GPR. The data reveal that Delta4 is effective in detecting mistakes in treatment regimens for head-and-neck cancer as well as lung cancer. Conclusion: The study results also imply that Delta4 can detect errors in VMAT plans, depending on the details of the defects and the treatment plans employed.
RESUMEN
Metal artifacts often appear in the images of computed tomography (CT) imaging. In the case of lumbar spine CT images, artifacts disturb the images of critical organs. These artifacts can affect the diagnosis, treatment, and follow up care of the patient. One approach to metal artifact reduction is the sinogram completion method. A mixed-variable thresholding (MixVT) technique to identify the suitable metal sinogram is proposed. This technique consists of four steps: 1) identify the metal objects in the image by using k-mean clustering with the soft cluster assignment, 2) transform the image by separating it into two sinograms, one of which is the sinogram of the metal object, with the surrounding tissue shown in the second sinogram. The boundary of the metal sinogram is then found by the MixVT technique, 3) estimate the new value of the missing data in the metal sinogram by linear interpolation from the surrounding tissue sinogram, 4) reconstruct a modified sinogram by using filtered back-projection and complete the image by adding back the image of the metal object into the reconstructed image to form the complete image. The quantitative and clinical image quality evaluation of our proposed technique demonstrated a significant improvement in image clarity and detail, which enhances the effectiveness of diagnosis and treatment.