Browsing by Author "Haberal, K. Murat"

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Bulut tabanlı digital doz takip yazılımıyla kurumsal diyagnostik referans seviyelerinin belirlenmesi ve bilgisayarlı tomografide radyasyon doz optimizasyonu
(Başkent Üniversitesi Tıp Fakültesi, 2022) Kahraman, Gökhan; Haberal, K. Murat
Çalışmamızın amacı bilgisayarlı tomografi (BT) incelemelerinde radyasyon dozunun standart bir hale getirilmesi ve optimizasyonu amacıyla referans bir değer olarak kullanılan diyagnostik referans seviyelerinin (DRS) belirlenmesidir. BT görüntüleme yöntemleri içerisinde en önemli iyonizan radyasyon kaynaklarından biridir. Son yıllarda BT tetkik sayısı oldukça artmış, artan radyasyon maruziyeti ile doz optimizasyonu oldukça önemli bir konu haline gelmiştir. DRS radyasyon doz miktarının düşürülmesinde oldukça etkili, iyi oluşturulmuş ve uzun yıllardır geniş çapta kabul görmüş bir kavramdır. Birçok ülkede belirli aralıklarla DRS düzeyleri yayınlanmaktadır. Çalışmamızda Türkiye’de ilk defa bulut tabanlı dijital doz takip yazılımı kullanılarak kurumsal DRS değerleri belirlenmiştir. Çalışmamızda kurumumuzdaki 5 radyoloji ünitesindeki 8 adet BT cihazı kullanılmıştır. İlk aşamada radyasyon doz ölçümlerindeki olası hataları ortadan kaldırmak için BT cihazlarının kalibrasyonları ve validasyonları yapılmıştır. İkinci aşamada BT cihazlarında BT doz değerlerini içeren veriler dijital bulut tabanlı bilgisayar yazılımı kullanılarak toplanmıştır. 12 ay boyunca toplanan bu verilerden tanımlayıcı istatistikler yapılmış ve her bir tetkik ve cihaz için ayrı ayrı 25., 50. ve 75. persentil (yüzdelik) DLP (Dose Length Product), CTDIvol (Computed Tomography Dose Index volume) değerleri hesaplanmıştır. Her bir merkezdeki 75. persentil DLP, CTDIvol değerleri her bir tetkik için merkeze özgü/lokal DRS değeri olarak, ayrıca tüm merkezlerdeki veriler birleştirilerek kurumsal DRS değerleri belirlenmiştir. Çalışmamızın oldukça sık kullanılan BT’de, radyasyon dozuyla ilişkili riskin belirlenmesi, tanıya yönelik karar destek sistemlerinin daha güvenli kullanımına büyük katkı sağlayacağına ve toplum sağlığını korumaya yönelik önemli bir adım olacağına inanıyoruz. The aim of this study is to identify the diagnostic reference levels (DRL) that are used as the reference value for the standardization and optimization of the radiation dose on computed tomography (CT) examinations. CT is one of the most important sources of ionizing radiation. The number of CT scans has increased dramatically in recent years. Dose optimization has become a critical issue with increasing radiation exposure. The DRL is well-established and has been widely accepted for many years. It is highly effective in decreasing radiation dose. DRL levels are published periodically in many countries. In our study, institutional DRL for CT were established for the first time in Turkey with cloud-based digital dose-tracking software. 8 CT scanners in 5 different radiology units in our institute were used. At the initial stage, CT scanners were calibrated and validated with phantom studies to eliminate the possible errors in the radiation dose measurements. In the second stage, data were collected by using a digital cloud-based software. Descriptive statistical analyses were performed. 25th, 50th and 75th percentiles of DLP, CTDIvol values were calculated according to examinations and CT scanners. At the same time, 75th percentile of the DLP and CTDIvol values obtained from each center were determined as the DRL value. Determination of the risk associated with radiation dose in CT, which is frequently used in medical imaging, will greatly contribute to the safer use of diagnostic decision support systems and will be an important step towards protecting the public health.
Incidence and Immunologic Analysis of Coronavirus Disease (COVID-19) in Hemodialysis Patients: A Single-Center Experience
(2020) Arslan, Hande; Musabak, Ugur; Soy, Ebru H. Ayvazoglu; Azap, Ozlem Kurt; Sayin, Burak; Akcay, Sule; Haberal, K. Murat; Akdur, Aydincan; Yildirim, Sedat; Haberal, Mehmet; 0000-0001-8287-6572; 0000-0003-1511-7634; 0000-0002-5735-4315; 0000-0002-0993-9917; 0000-0002-8726-3369; 0000-0002-3171-8926; 0000-0002-8360-6459; 0000-0002-3462-7632; 0000-0002-8211-4065; 32519618; J-3707-2015; AAU-1810-2020; AAF-4610-2019; AAC-5566-2019; AAA-3068-2021; AAK-4089-2021; AAB-5175-2021; AAJ-8097-2021; R-9398-2019
Objectives: COVID-19 is a great threat to the modern world and significant threat to immunocompromised patients, including patients with chronic renal failure. We evaluated COVID-19 incidence among our hemodialysis patients and investigated the most probable immune mechanisms against COVID-19. Materials and Methods: Baskent University has 21 dialysis centers across Turkey, with 2420 patients on hemodialysis and 30 on peritoneal dialysis. Among these, we retrospectively evaluated 602 patients (257 female/345 male) with chronic renal failure receiving hemodialysis as renal replacement therapy; 7 patients (1.1%) were infected with SARS-CoV-2. We retrospectively collected patient demographic characteristics, clinical data, and immunological factors affecting the clinical course of the disease. We divided patients into groups and included 2 control groups ( individuals with normal renal functions): group I included COVID-19-positive patients with normal renal function, group II included COVID-19-positive hemodialysis patients, group III included COVID-19-negative hemodialysis patients, and group IV included COVID-19-negative patients with normal renal function. Lymphocyte subsets in peripheral blood and typing of human leukocyte antigens were analyzed in all groups, with killer cell immunoglobulin-like receptor genes analyzed only in COVID-19-positive patients and healthy controls. Results: No deaths occurred among the 7 COVID-19-positive hemodialysis patients. Group I patients were significantly older than patients in groups II and III ( P = .039, P = .030, respectively) but not significantly different from group IV (P = .060). Absolute counts of natural killer cells in healthy controls were higher than in other groups (but not significantly). Activated T cells were significantly increased in both COVID-19-positive groups versus COVID-19-negative groups. Groups showed significant differences in C and DQ loci with respect to distribution of alleles in both HLA classes. Conclusions: Although immunocompromised patients are at greater risk for COVID-19, we found lower COVID-19 incidence in our hemodialysis patients, which should be further investigated in in vitro and molecular studies.
Partial Splenic Torsion in situ: Revealed by Ultrasound and Computed Tomography
(2018) Haberal, K. Murat; Bayramoglu, Mert; Kahraman, Gokhan; Avci, Tevfik; 0000-0001-5225-959X; 0000-0002-8211-4065; 30065530; AAF-1698-2021; R-9398-2019
We present a case of acute abdominal pain due to partial torsion of spleen located in its own normal anatomical position in a 20-year-old woman, diagnosed by ultrasound and confirmed on computed tomography and treated laparoscopically.
Virtual contrast enhancement for CT scans of abdomen and pelvis
(2022) Liu, Jingya; Tian, Yingli; Duzgol, Cihan; Akin, Oguz; Agildere, A. Muhtesem; Haberal, K. Murat; Coskun, Mehmet; 0000-0002-8211-4065; 35914340; R-9398-2019
Contrast agents are commonly used to highlight blood vessels, organs, and other structures in magnetic resonance imaging (MRI) and computed tomography (CT) scans. However, these agents may cause allergic reactions or nephrotoxicity, limiting their use in patients with kidney dysfunctions. In this paper, we propose a generative adversarial network (GAN) based framework to automatically synthesize contrast-enhanced CTs directly from the non-contrast CTs in the abdomen and pelvis region. The respiratory and peristaltic motion can affect the pixel-level mapping of contrast-enhanced learning, which makes this task more challenging than other body parts. A perceptual loss is introduced to compare high-level semantic differences of the enhancement areas between the virtual contrast-enhanced and actual contrast-enhanced CT images. Furthermore, to accurately synthesize the intensity details as well as remain texture structures of CT images, a dual-path training schema is proposed to learn the texture and structure features simultaneously. Experiment results on three contrast phases (i.e. arterial, portal, and delayed phase) show the potential to synthesize virtual contrast-enhanced CTs directly from non-contrast CTs of the abdomen and pelvis for clinical evaluation.