Analyzing Coronary Artery Disease in Patients with Low CAC Scores by 64-Slice MDCT

Purpose. Coronary artery calcification (CAC) scores are widely used to determine risk for Coronary Artery Disease (CAD). A CAC score does not have the diagnostic accuracy needed for CAD. This work uses a novel efficient approach to predict CAD in patients with low CAC scores. Materials and Methods. The study group comprised 86 subjects who underwent a screening health examination, including laboratory testing, CAC scanning, and cardiac angiography by 64-slice multidetector computed tomographic angiography. Eleven physiological variables and three personal parameters were investigated in proposed model. Logistic regression was applied to assess the sensitivity, specificity, and accuracy of when using individual variables and CAC score. Meta-analysis combined physiological and personal parameters by logistic regression. Results. The diagnostic sensitivity of the CAC score was 14.3% when the CAC score was ≤30. Sensitivity increased to 57.13% using the proposed model. The statistically significant variables, based on beta values and P values, were family history, LDL-c, blood pressure, HDL-c, age, triglyceride, and cholesterol. Conclusions. The CAC score has low negative predictive value for CAD. This work applied a novel prediction method that uses patient information, including physiological and society parameters. The proposed method increases the accuracy of CAC score for predicting CAD

Calibration of the EBT3 Gafchromic Film Using HNN Deep Learning

To achieve a dose distribution conformal to the target volume while sparing normal tissues, intensity modulation with steep dose gradient is used for treatment planning. To successfully deliver such treatment, high spatial and dosimetric accuracy are crucial and need to be verified. With high 2D dosimetry resolution and a self-development property, the Ashland Inc. product EBT3 Gafchromic film is a widely used quality assurance tool designed especially for this. However, the film should be recalibrated each quarter due to the “aging effect,” and calibration uncertainties always exist between individual films even in the same lot. Recently, artificial neural networks (ANN) are applied to many fields. If a physicist can collect the calibration data, it could be accumulated to be a substantial ANN data input used for film calibration. We therefore use the Keras functional Application Program Interface to build a hierarchical neural network (HNN), with the inputs of net optical densities, pixel values, and inverse transmittances to reveal the delivered dose and train the neural network with deep learning. For comparison, the film dose calculated using red-channel net optical density with power function fitting was performed and taken as a conventional method. The results show that the percentage error of the film dose using the HNN method is less than 4% for the aging effect verification test and less than 4.5% for the intralot variation test; in contrast, the conventional method could yield errors higher than 10% and 7%, respectively. This HNN method to calibrate the EBT film could be further improved by adding training data or adjusting the HNN structure. The model could help physicists spend less calibration time and reduce film usage

Detection of Gastroesophageal Reflux Esophagitis Using 2-fluoro-2-deoxy-d-glucose Positron Emission Tomography

Background. Gastroesophageal reflux disease (GERD) is a common disease and a major upper gastrointestinal problem. The purpose of the present study is to evaluate the use of noninvasive 2-fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) to detect gastroesophageal reflux esophagitis. Materials and Methods. This is a retrospective study reviewing 408 healthy check-up subjects (169 females and 239 men), who underwent both FDG-PET and upper gastrointestinal endoscopy during September 2008 to December 2009. Quantitative analysis of FDG uptake in the distal part of the esophagus was performed by calculating the maximum standard uptake value (SUVmax). This indicated the degree of esophagitis. FDG-PET findings were compared with endoscopic (modified version of the Los Angeles classification) diagnoses as the gold standard. Results. The SUVmax ranged from 1.30 to 3.40 in normal subjects and from 1.30 to 4.00 in subjects with gastroesophageal reflux esophagitis. In the esophagitis group, the SUVmax was 2.13±0.42 in subjects with modified LA grade M, 2.21±0.45 in subjects with LA grade A, and 2.48±0.44 in subjects with LA grade B and C gastroesophageal reflux esophagitis. One-way ANOVA and post-hoc comparison with Bonferroni correction (P value = 0.003) identified statistical differences between the three groups. Conclusion. Noninvasive FDG-PET may be useful in the detection and evaluation of various degrees of gastroesophageal reflux esophagitis