Browsing by Author "Celtikci, Pinar"

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Diagnostic Impact of Quantitative Dual-Energy Computed Tomography Perfusion Imaging for the Assessment of Subsegmental Pulmonary Embolism
(2021) Celtikci, Pinar; Hekimoglu, Koray; Kahraman, Gokhan; Bozbas, Serife; Gultekin, Bahadir; Akay, Hakki Tankut; 0000-0002-1655-6957; 0000-0002-0805-0841; 33186173; AAD-9097-2021; ABA-7388-2021
Objective The aim of this study was to investigate the quantitative differences of dual-energy computed tomography perfusion imaging measurements in subsegmental pulmonary embolism (SSPE), between normal lung parenchyma (NLP) and hypoperfused segments (HPS) with and without thrombus on computed tomography angiography (CTA). Methods Lung attenuation, iodine density, and normalized uptake values were measured from HPS and NLP on iodine maps of 43 patients with SSPE. Presence of pulmonary embolism (PE) on CTA was recorded. One-way repeated-measures analysis of variance and Kruskal-Wallis analyses with post hoc comparisons were conducted. Results The numbers of HPS with and without SSPE on CTA were 45 (55.6%) and 36 (44.4%), respectively. Lung attenuation of NLP was significantly different from HPS (P < 0.001). Iodine density and normalized uptake values of HPS with PE were significantly lower than those of HPS without PE, which is significantly lower than NLP (P < 0.001). Conclusions Subsegmental pulmonary embolism causes HPS on dual-energy computed tomography perfusion imaging, which demonstrates different iodine density and normalized uptake values depending on the presence of thrombus.
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, Dalokay
Background: 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.
Microsurgical and Tractographic Anatomical Study of Transtemporal-Transchoroidal Fissure Approaches to the Ambient Cistern
(2021) Egemen, Emrah; Celtikci, Pinar; Dogruel, Yucel; Yakar, Fatih; Sahinoglu, Defne; Farouk, Mohamed; Adiguzel, Esat; Ugur, Hasan Caglar; Coskun, Erdal; Gungor, Abuzer; Microsurgical and Tractographic Anatomical Study of Transtemporal-Transchoroidal Fissure Approaches to the Ambient Cistern; 33313862
BACKGROUND: Approaching ambient cistern lesions is still a challenge because of deep location and related white matter tracts (WMTs) and neural structures. OBJECTIVE: To investigate the white matter anatomy in the course of 3 types of transtemporal-transchoroidal fissure approaches (TTcFA) to ambient cistern by using fiber dissection technique with translumination and magnetic resonance imaging fiber tractography. METHODS: Eight formalin-fixed cerebral hemispheres were dissected on surgical corridor from the temporal cortex to the ambient cistern by using Klingler's method. The trans-middle temporal gyrus, trans-inferior temporal sulcus ( TITS), and trans-inferior temporal gyrus (TITG) approaches were evaluated. WMTs that were identified during dissection were then reconstructed on the Human Connectome Project 1021 individual template for validation. RESULTS: The trans-middle gyrus approach interrupted the U fibers, arcuate fasciculus (AF), the ventral segment of inferior frontoocipital fasciculus (IFOF), the temporal extensions of the anterior commissure (AC) posterior crura, the tapetum (Tp) fibers, and the anterior loop of the optic radiation (OR). The TITS approach interrupted U fibers, inferior longitudinal fasciculus (ILF), IFOF, and OR. The TITG approach interrupted the U fibers, ILF, andOR. The middle longitudinal fasciculus, ILF, and uncinate fasciculus (UF) were not interrupted in the trans-middle gyrus approach and the AF, UF, AC, and Tp fiberswere not interrupted in the TITS/gyrus approaches. CONCLUSION: Surgical planning of the ambient cistern lesions requires detailed knowledge aboutWMTs. Fiber dissection and tractography techniques improve the orientation during surgery and may help decrease surgical complications.
Microsurgical and White Matter Anatomy of the Hypothalamus: A Fiber Dissection Study Correlating With Magnetic Resonance Tractography
(2021) Celtikci, Pinar; 35006657
BACKGROUND: The hypothalamus has been shown to be a hub for the control of autonomic and endocrine functions as well as the emotional and behavioral state due to having white matter connections to both brainstem and the cerebrum. However, the white matter connectivity of the hypothalamus is not completely unraveled and there is no consensus in the literature regarding anatomical parcellation and target selection for deep brain stimulation (DBS) surgery. OBJECTIVE: To define and showcase the microsurgical and the white matter anatomy of the hypothalamic region by utilizing fiber dissection technique and fiber tractography. METHODS: A total of 24 formalin-fixed human brain hemispheres were dissected according to Klingler's fiber dissection method with the aid of a surgical microscope. Following morphometric measurements, the hypothalamus was segmented into 6 parts as anteromedial, anterolateral, posteromedial, posterolateral, superior, and inferior according to landmarks of anterior commissure- posterior commissure line and mamillary body. RESULTS: The diagonal band of Broca, the ventral amygdalohypothalamic fiber, stria terminalis, and fornix were related with the anteromedial part; cingulate bundle, supraoptic commissure, and frontopontine tracts were related with the anterolateral part; medial longitudinal fasciculus, dorsal longitudinal fasciculus were related with the posteromedial part; and fasciculus mamillaris princeps, the corticospinal tract, the temporo-parietooccipito pontine tract, medial lemniscus, spinothalamic tract, mammillotegmental fasciculus, and dentatorubrothalamic tract were related with the posterolateral part of the hypothalamus. CONCLUSION: Understanding the detailed fiber tract anatomy of the hypothalamus would provide guidance to neurosurgeons in surgical planning, target selection for DBS and intraoperative orientation.
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; 31608975
AIM: 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.
Utilizing Deep Convolutional Generative Adversarial Networks for Automatic Segmentation of Gliomas: An Artificial Intelligence Study
(2022) Aydogan Duman, Ebru; Sagiroglu, Seref; Celtikci, Pinar; Demirezen, Mustafa Umut; Borcek, Alp Ozgun; Emmez, Hakan; Celtikci, Emrah; 34542897
AIM: To describe a deep convolutional generative adversarial networks (DCGAN) model which learns normal brain MRI from normal subjects than finds distortions such as a glioma from a test subject while performing a segmentation at the same time. MATERIAL and METHODS: MRIs of 300 healthy subjects were employed as training set. Additionally, test data were consisting anonymized T2-weigted MRIs of 27 healthy subjects and 27 HGG patients. Consecutive axial T2-weigted MRI slices of every subject were extracted and resized to 364x448 pixel resolution. The generative model produced random normal synthetic images and used these images for calculating residual loss to measure visual similarity between input MRIs and generated MRIs. RESULTS: The model correctly detected anomalies on 24 of 27 HGG patients' MRIs and marked them as abnormal. Besides, 25 of 27 healthy subjects' MRIs in the test dataset detected correctly as healthy MRI. The accuracy, precision, recall, and AUC were 0.907, 0.892, 0.923, and 0.907, respectively. CONCLUSION: Our proposed model demonstrates acceptable results can be achieved only by training with normal subject MRIs via using DCGAN model. This model is unique because it learns only from normal MRIs and it is able to find any abnormality which is different than the normal pattern.