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Item Dual-Energy Computed Tomography Pulmonary Angiography With Ultra-Low Dose Contrast Administration: Comparison Of Image Quality With Standard Computed Tomography Pulmonary Angiography(2022) Celtikci, Pinar; Hekimoglu, Koray; Kahraman, Gokhan; Haberal, Kemal Murat; Kilic, DalokayBackground: This study aims to compare quantitative and qualitative image quality between standard computed tomography pulmonary angiography and dual-energy computed tomography pulmonary angiography protocols. Methods: Between September 2017 and August 2018, a total of 91 consecutive patients (34 males, 57 females; mean age: 65.9 +/- 15 years; range, 37 to 91 years) who were referred for computed tomography pulmonary angiography were randomly imaged with either a standard or dual-energy protocol. Standard protocol (n=49) was acquired with a 64-slice multidetector computed tomography scanner using 60 mL contrast media (18 g iodine). A third-generation dual-energy computed tomography scanner was utilized to acquire dual-energy computed tomography pulmonary angiography and simultaneous lung perfusion imaging (n=42), which required 40 mL contrast media (12 g iodine). Two radiologists reviewed images separately to determine interobserver variability. Attenuation and noise in three central and two segmental pulmonary arteries were measured; signal-to-noise ratio and contrast-to-noise ratio were calculated. A five-point scale was utilized to evaluate image quality and image noise qualitatively. Results: The standard protocol required a significantly higher amount of iodine. Comparison of two groups employing quantitative measurements (attenuation value in five pulmonary arteries, mean attenuation value, mean background noise, signal-to-noise ratio, and contrast-to-noise ratio) and employing qualitative measurements (five-point scale scores of image quality and image noise) revealed no significant difference between dual-energy and standard groups (p>0.05). Qualitative and quantitative evaluations demonstrated low interobserver variability. Conclusion: Dual-energy computed tomography pulmonary angiography protocol delivers image quality equal to standard protocol, while requiring less amount of iodinated contrast medium and providing simultaneous lung perfusion imaging to contribute the diagnosis of pulmonary embolism.Item A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance I mages: A Preliminary Machine Learning Study(2020) Atici, Mehmet Ali; Sagiroglu, Seref; Celtikci, Pinar; Ucar, Murat; Borcek, Alp Ozgun; Emmez, Hakan; Celtikci, Emrah; 0000-0002-1655-6957; 31608975AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans. MATERIAL and METHODS: A total of 3580 images obtained from 179 individuals were used for training and validation. After random rotation and vertical flip, training data was augmented by factor of 10 in each iteration. In order to increase data processing time, every single image converted into a Jpeg image which has a resolution of 320x320. Accuracy, precision and recall rates were calculated after training of the algorithm. RESULTS: Following training, CNN achieved acceptable performance ratios of 0.854 to 0.944 for accuracy, 0.812 to 0.980 for precision and 0.738 to 0.907 for recall. Also, CNN was able to detect HGG cases even though there is no apparent mass lesion in the given image. CONCLUSION: Our preliminary findings demonstrate; currently proposed CNN model achieves acceptable performance results for the automatic detection of HGGs on T2-weighted images.