Introduction: In this study, we aimed to evaluate the possibility of assessing cardiac abnormality using the left ventricular anatomical axis (LVAA) obtained from short-axis views of myocardial perfusion imaging (MPI).Methods: To obtain LVAA, an ellipse was drawn around the outer wall of SPECT images from XCAT phantoms and patients. The best line was then drawn from the center of all the ellipses in the short-axis views called LVAA. Then, we defined two angles based on LVAA including Ɵ which is the angle created by LVAA with the x-axis, and Φ which is the angle created by LVAA with the z-axis.Results: In this study, 94 cases were enrolled including 48 males (51%) and 46 females (49%) with a mean age of 65.65±10.04. According to the results, there was a significant difference between the two obtained angles and the result of the scan (p<0.05). The ideal cut-off of Ɵ for an abnormal scan was 91.79 (AUC, 0.93; p=0.001) with the sensitivity of 98% and specificity of 80%.Conclusion: It can be concluded that LVAA as a quantitative factor is significantly different between normal and abnormal MPS and can be used for the evaluation of MPI.

Purpose: Noise in brain Single Photon Emission Computed Tomography (SPECT) images limits an early diagnosis of Parkinson's Disease (PD). To overcome the limitation, as an image processing approach, wavelet transformation was used to denoising the images also with a segmentation method to differentiate the basal ganglia in brain SPECT. Materials and Methods: The brain scans of the human XCAT phantom through the Simulating Medical Imaging Nuclear Detectors (SIMIND) simulated SPECT system were imported to the MATLAB toolkit for image processing. The reconstructed brain images by iterative reconstruction were de-noised through 9 methods of wavelet transformation at different levels, and then six segmentation methods were applied to differentiate the caudate and putamen. The Dice coefficient, Specificity, and Sensitivity evaluation criteria were calculated based on the adaptive thresholding of the selected images from segmentation. A ground truth image was manually marked by a clinical nuclear medicine specialist. Results: The dice coefficient was obtained in a range from 0. 3979 to 0. 6299, as well as the specificity criterion from 0. 7682 to 0. 8168 and the sensitivity from 0. 9049 to 0. 9871. The results from adaptive threshold segmentation and the evaluation criteria showed that the best levels of the nucleuses detectability were provided by level 7 of Biorthogonal, levels 4 and 7 of Coiflet, level 6 of Daubechies, level 5 of Haar, level 6 of Morlet and level 6 of Symlet methods. Conclusion: Parkinson’s disease may be diagnosed in the early stage by an image processing approach to improve the quality of brain SPECT images.

Introduction: Use of SPECT/CT data is the most accurate method for patient-specific internal dosimetry when isotopes emit single gamma rays. The manual or semi-automatic segmentation of organs is a major obstacle that slows down and limits the patient-specific dosimetry. Using digital phantoms that mimic patient’s anatomy can bypass the segmentation step and facilitate the dosimetry process. In this study, the results of a patient-specific dosimetry based on CT data and XCAT phantom, a flexible phantom with predefined organs, are compared.Methods: The dosimetry results (S-value and SAF) were calculated for a patient with breast cancer who received Samarium- 153 ethylenediamine-N, N, N′, N′-tetrakis (methylenephosphonic acid (153 Sm-EDTMP). Biodistribution of activity was obtained from the SPECT scan. The anatomical data and attenuation map were extracted from CT as well as the XCAT phantom with different BMIs. GATE Monte-Carlo simulator was used to calculate the dose to different organs based on the activity distribution and segmented anatomy.Results: The whole body dosimetry results are the same for both calculations based on the CT and XCAT with different BMIs; however for target organs, the differences between SAFs and S-values are high. In the spine, the clinically important target organ for Samarium therapy, the dosimetry results obtained from phantoms with unmatched BMIs between XCAT phantom and CT are substantially different.Conclusion: We showed that atlas-based dosimetry using XCAT phantom even with matched BMI may lead to considerable errors as compared to calculations based on patient’s own CT. For accurate dosimetry results, calculations should be done using CT data.

Introduction Patient set-up optimization is required in radiotherapy to fill the accuracy gap between personalized treatment planning and uncertainties in the irradiation set-up. In this study, we aimed to develop a new method based on neural network to estimate patient geometrical setup using 4-dimensional (4D) XCAT anthropomorphic phantom. Materials and Methods To access 4D modeling of motion of dynamic organs, a phantom employs non-uniform rational B-splines (NURBS)-based Cardiac-Torso method with spline-based model to generate 4D computed tomography (CT) images. First, to generate all the possible roto-translation positions, the 4D CT images were imported to Medical Image Data Examiner (AMIDE). Then, for automatic, real time verification of geometrical setup, an artificial neural network (ANN) was proposed to estimate patient displacement, using training sets. Moreover, three external motion markers were synchronized with a patient couch position as reference points. In addition, the technique was validated through simulated activities by using reference 4D CT data acquired from five patients. Results The results indicated that patient geometrical set-up is highly depended on the comprehensiveness of training set. By using ANN model, the average patient setup error in XCAT phantom was reduced from 17. 26 mm to 0. 50 mm. In addition, in the five real patients, these average errors were decreased from 18. 26 mm to 1. 48 mm various breathing phases ranging from inhalation to exhalation were taken into account for patient setup. Uncertainty error assessment and different setup errors were obtained from each respiration phase. Conclusion This study proposed a new method for alignment of patient setup error using ANN model. Additionally, our correlation model (ANN) could estimate true patient position with less error.