Monte Carlo simulation and performance assessment of GE Discovery 690 VCT positron emission tomography/computed tomography scanner.

The aim of this study is to simulate GE Discovery 690 VCT positron emission tomography/computed tomography (PET/CT) scanner using Geant4 Application for Tomographic Emission (GATE) simulation package (version 8). Then, we assess the performance of scanner by comparing measured and simulated parameter results. Detection system and geometry of PET scanner that consists of 13,824 LYSO crystals designed in 256 blocks and 24 ring detectors were modeled. In order to achieve a precise model, we verified scanner model. Validation was based on a comparison between simulation data and experimental results obtained with this scanner in the same situation. Parameters used for validation were sensitivity, spatial resolution, and contrast. Image quality assessment was done based on comparing the contrast recovery coefficient (CRC) of simulated and measured images. The findings demonstrate that the mean difference between simulated and measured sensitivity is <7%. The simulated spatial resolution agreed to within <5.5% of the measured values. Contrast results had a slight divergence within the range below 4%. The image quality validation study demonstrated an acceptable agreement in CRC for 8:1 and 2:1 source-to-background activity ratio. Validated performance parameters showed good agreement between experimental data and simulated results and demonstrated that GATE is a valid simulation tool for simulating this scanner model. The simulated model of this scanner can be used for future studies regarding optimization of image reconstruction algorithms and emission acquisition protocols.

Automatic Segmentation, Detection, and Diagnosis of Abdominal Aortic Aneurysm (AAA) Using Convolutional Neural Networks and Hough Circles Algorithm.

PURPOSE: An abdominal aortic aneurysm (AAA) is known as a cardiovascular disease involving localized deformation (swelling or enlargement) of aorta occurring between the renal and iliac arteries. AAA would jeopardize patients' lives due to its rupturing risk, so prompt recognition and diagnosis of this disorder is vital. Although computed tomography angiography (CTA) is the preferred imaging modality used by radiologist for diagnosing AAA, computed tomography (CT) images can be used too. In the recent decade, there has been several methods suggested by experts in order to find a precise automated way to diagnose AAA without human intervention base on CT and CTA images. Despite great approaches in some methods, most of them need human intervention and they are not fully automated. Also, the error rate needs to decrease in other methods. Therefore, finding a novel fully automated with lower error rate algorithm using CTA and CT images for Abdominal region segmentation, AAA detection, and disease severity classification is the main goal of this paper. METHODS: The proposed method in this article will be performed in three steps: (1) designing a classifier based on Convolutional Neural Network (CNN) for classifying different parts of abdominal into four different classes such as: abdominal inside region, aorta, body border, and bone. (2) After correct aorta detection, defining its edge and measuring its diameter with the use of Hough Circle Algorithm (which is an algorithm for finding an arbitrary shape in images and measuring its diameter in pixel) is the second step. (3) Ultimately, the detected aorta, depending on its diameter, will be categorized in one of these groups: (a) there is no risk of AAA, (b) there is a medium risk of AAA, and (c) there is a high risk of AAA. RESULTS: The designed CNN classifier classifies different parts of abdominal into four different classes such as: abdominal inside region, aorta, body border, and bone with the accuracy, precision, and sensitivity of 97.93, 97.94, and 97.93% respectively. The accuracy of the proposed classifier for aorta region detection is 98.62% and Hough Circles algorithm can classify 120 aorta patches according to their diameter with accuracy of 98.33%. CONCLUSIONS: As a whole, a classifier using Convolutional Neural Network is designed and applied in order to detect AAA region among other abdominal regions. Then Hough Circles algorithm is applied to aorta patches for finding aorta border and measuring its diameter. Ultimately, the detected aortas will be categorized according to their diameters. All steps meet the expected results.

Prediction of the thickness of the compensator filter in radiation therapy using computational intelligence.

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are investigated to predict the thickness of the compensator filter in radiation therapy. In the proposed models, the input parameters are field size (S), off-axis distance, and relative dose (D/D0), and the output is the thickness of the compensator. The obtained results show that the proposed ANN and ANFIS models are useful, reliable, and cheap tools to predict the thickness of the compensator filter in intensity-modulated radiation therapy.

Right ventricle functional parameters estimation in arrhythmogenic right ventricular dysplasia using a robust shape based deformable model.

Assessment of cardiac right-ventricle functions plays an essential role in diagnosis of arrhythmogenic right ventricular dysplasia (ARVD). Among clinical tests, cardiac magnetic resonance imaging (MRI) is now becoming the most valid imaging technique to diagnose ARVD. Fatty infiltration of the right ventricular free wall can be visible on cardiac MRI. Finding right-ventricle functional parameters from cardiac MRI images contains segmentation of right-ventricle in each slice of end diastole and end systole phases of cardiac cycle and calculation of end diastolic and end systolic volume and furthermore other functional parameters. The main problem of this task is the segmentation part. We used a robust method based on deformable model that uses shape information for segmentation of right-ventricle in short axis MRI images. After segmentation of right-ventricle from base to apex in end diastole and end systole phases of cardiac cycle, volume of right-ventricle in these phases calculated and then, ejection fraction calculated. We performed a quantitative evaluation of clinical cardiac parameters derived from the automatic segmentation by comparison against a manual delineation of the ventricles. The manually and automatically determined quantitative clinical parameters were statistically compared by means of linear regression. This fits a line to the data such that the root-mean-square error (RMSE) of the residuals is minimized. The results show low RMSE for Right Ventricle Ejection Fraction and Volume (≤ 0.06 for RV EF, and ≤ 10 mL for RV volume). Evaluation of segmentation results is also done by means of four statistical measures including sensitivity, specificity, similarity index and Jaccard index. The average value of similarity index is 86.87%. The Jaccard index mean value is 83.85% which shows a good accuracy of segmentation. The average of sensitivity is 93.9% and mean value of the specificity is 89.45%. These results show the reliability of proposed method in these cases that manual segmentation is inapplicable. Huge shape variety of right-ventricle led us to use a shape prior based method and this work can develop by four-dimensional processing for determining the first ventricular slices.