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Öğe Fokal Asimetrik Meme Dansitelerinin Değerlendirilmesinde Tomosentezin Tanıya Katkısı(2023) Güçlü, Derya; Naldemir, İbrahim Feyyaz; Unlu, Elif Nisa; Onbas, OmerAmaç: Bu çalışma ile mamografik incelemelerde fokal asimetrik dansite saptanan ve ek tetkik gerektiren olgularda tomosentezin tanıya katkısını araştırmak amaçlanmıştır. Gereç ve Yöntemler: Hastanemiz Radyoloji Anabilim Dalı Meme Görüntüleme Ünitesine Şubat 2020-Haziran 2022 tarihleri arasında tarama ya da tanısal amaçlı dijital mamografi tetkiki yapılan ve fokal asimetrik meme dansitesi saptanan 56 olguya, ek olarak tomosentez inceleme yapıldı ve bulgular BI-RADS kullanılarak sınıflandırıldı. Hasta yaşı, meme paterni, fokal asimetik dansite bulunan meme ve kadranı kaydedildi. Sonrasında tamamlayıcı ve altın standart tetkik olarak bilateral meme ultrasonografi incelemesi yapılarak bulgular kaydedildi. Bulgular: Dijital mamografi görüntüleme sonucunda fokal asimetrik dansite nedeniyle BI-RADS 0 olarak değerlendirilen 56 kadın olgunun ortalama yaşları 51,5 ± 8,1’dir. %12,5’i (n=7) A tipi, %42,9’u (n=24) B tipi, %41,1’i (n=23) C tipi, %3,6’sı (n=2) D tipi meme paternine sahiptir. Fokal asimetrik dansitelerin % 44,6’sı (n=25) sağ, %55,4’ü (n=31) sol memede saptanmıştır. Dijital tomosentez incelemelerinin değerlendirilmesinde, olguların % 41,1’i (n=23) BI-RADS 1, % 16,1’i (n=9) BI-RADS 2, % 21,4’ü (n=12) BI-RADS 3, 21,4’ü (n=12) BI-RADS 4 olarak sınıflanmıştır. Ultrasonografik incelemede hastaların %42,9’u (n=24) asimetrik fibroglandüler doku, %17,9’u (n=10) olası benign lezyon, % 19,6’sı (n=11) ise malign lezyon olarak değerlendirilmiştir. Histopatolojik inceleme önerilen 12 hastanın ikisi stromal fibrozis, diğer 10’u ise biri lobüler olmak üzere invaziv meme karsinomu olarak değerlendirilmiştir. Sonuç: Bu çalışma mamografik incelemeler ile morfolojisi değerlendirilemeyen ve ek tetkik gerektiren fokal asimetrik dansitelerde tomosentezin tanısal önemini vurgulamaktadır. Tomosentez, son dekatta kullanımı giderek artan ve parankime süperpoze lezyonlarda yaşanabilecek tanı güçlüklerini çözebilecek görece yeni bir tekniktir.Öğe Gender Classification Using Parameters Obtained from the Dens Axis with Machine Learning Algorithms and Multilayer Perceptron Classifier(Dubai Iranian Hosp, 2025) Harmandaoglu, Oguzhan; Secgin, Yusuf; Kaya, Seren; Ozturk, Oguzhan; Senol, Deniz; Onbas, OmerBackground and Objectives: Due to the difficulties associated with the separation, damage, cremation, and commingling of skeletal remains, it is of great importance in forensic medicine to assess the accuracy and reliability of sex estimates derived from different skeletal components. For this purpose, this study aimed to classify gender using machine learning (ML) algorithms and a multilayer perceptron classifier (MLPC) based on morphometric data of the dens axis obtained from computed tomography (CT) images. Methods: Retrospectively, measurements were taken from CT images of 300 male and 300 female individuals aged between 18-65 years, including dens axis height (DAH), anteroposterior (APDDA) and anterosuperior lengths (ASDDA), dens axis angle (DAA), clivodental angle (CDA), and Boogard angle (BOO). Machine learning models such as Extra Tree Classifier (ETC), Random Forest (RF), Decision Tree (DT), Gaussian Naive Bayes (GaussianNB), k-Nearest Neighbors (k-NN), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Logistic Regression (LR) were used. MLPC was chosen as artificial neural networks (ANN) model. Results: Significant differences were found between genders in all dens axis parameters except BOO (p<0.05). The highest accuracy rate in ML algorithm modeling was found to be 0.80 with LDA, RF, k-NN algorithms, and MLPC. The parameter with the highest impact on gender classification was the dens axis anterosuperior length. Conclusion: It was found that the parameters obtained from the dens axis using MLCP and ML algorithms have sufficient accuracy rates the classification of sex. It was concluded that in forensic medicine, in cases of deterioration, loss, and deficiencies in bone sources for biological identity determination, the morphometric features of the dens axis can be considered for gender prediction.Öğe Gender estimation using machine learning algorithms and artificial neural networks based on parameters obtained from the sphenoid sinus(Mexican Acad Surgery, 2026) Harmandaoglu, Oguzhan; Secgin, Yusuf; Kaya, Seren; Senol, Deniz; Oner, Zulal; Onbas, OmerObjective: The aim of this study is to estimate gender using parameters obtained from the sphenoid sinus in computed tomography (CT) images, utilizing Machine Learning (ML) algorithms and Artificial Neural Networks (ANNs). Method: In this study, length, width, and volume measurements of the sphenoid sinus were evaluated from CT images of 300 individuals (150 males and 150 females) aged 18-65 years. Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis, Logistic Regression (k-NN) algorithms, and ANN model were used for gender prediction. Results: The length, width, and volume of the sphenoid sinus on both the left and right sides were found to be significantly higher in males compared to females (p < 0.05). The performance values of the ML algorithms were found as follows: LDA 0.82; k-NN 0.80; LR 0.84; GaussianNB 0.80; DT 0.82; and ANN 0.82. Conclusions: Morphometric measurements of the sphenoid sinus, when analyzed with the LDA, LR, DT, and ANN algorithms, showed high accuracy and provided reliable data for sex estimation.Öğe Investigation of relationship between neck anteroposterior-transvers diameters and trachea anteroposterior-transvers diameters based on CT scans(2026) Unlu, İlhan; Subaşı, Buğra; Belada, Abdullah; Onbas, Omer; Ozel, Mehmet Ali; Güçlü, Ender; Belada, Ayşe NurAims: The tracheal diameter may be influenced by external anatomical characteristics, and accurate estimation of airway dimensions is important for procedural planning. This study aimed to evaluate the anatomical relationship between neck and tracheal dimensions using radiological measurements in the anteroposterior (AP) and transverse (TR) planes. Methods: A total of 180 adult patients were retrospectively included in the study. The AP and TR diameters of the first tracheal ring and the AP and TR diameters of the neck at the level of the cricoid cartilage were measured on computed tomography (CT) images. Body mass index was calculated from recorded height and weight data. Correlation analyses and multiple linear regression models adjusted for age, sex, and body mass index were performed. Results: Significant sex-related differences were observed in tracheal AP and TR diameters (trachea AP: 19.97±2.10 mm vs. 16.32±1.87 mm; trachea TR: 20.00±1.67 mm vs. 16.14±1.69 mm; both p<0.001). Significant differences were also found in neck dimensions between males and females (neck AP: 141.56±14.64 mm vs. 123.79±20.41 mm; p<0.001; neck TR: 126.24±12.97 mm vs. 120.12±12.99 mm; p=0.002). Correlation analysis demonstrated statistically significant associations between tracheal and neck measurements. In multivariable regression analysis, neck AP diameter and sex were significantly associated with both tracheal AP and TR diameters. Conclusion: Tracheal and neck dimensions measured on CT were statistically associated, with stronger relationships observed in AP measurements. However, the magnitude of these associations was limited, indicating that external neck measurements alone are insufficient for precise prediction of tracheal size. Neck measurements may serve as complementary anatomical indicators within a multifactorial airway assessment approach rather than as standalone predictors.Öğe Long-term Outcomes of Children with Myelomeningocele and the Quality of Life in Survivors(Duzce Univ, Fac Medicine, 2024) Cakmak, Hatice Mine; Onbas, Omer; Tuncer, Cengiz; Kocabay, Kenan; Kilic, Guven; Zamur, Cagatay; Sav, Nadide MelikeObjective: Myelomeningocele, a condition that causes chronic health conditions and diminished quality of life, affects not just the children but also their families. Therefore, we comprehensively evaluated the data of 101 children with MMC (myelomeningocele) and aimed to compare the quality of life between children with MMC and their siblings. It is crucial to understand that children with MMS have a diminished quality of life with social and behavioral aspects and health issues, which can be emotionally challenging for them and their families. Methods: In this retrospective study, we collected data from electronic files, ensuring a comprehensive and accurate representation of the participants' medical history. To measure the quality of life, we used the KIDSCREEN 10 instrument, a widely recognized and validated tool in pediatric research. Results: Of the 101 children, 93 were survivors. Comparing the survivors (n=93) with their siblings, survivors had lower HRQoL (health-related quality of life) scores in subdimensions of physical well-being (p<0.001), relationships with family (p<0.001) Aand friends (p<0.001), Aschool performance and attention (p<0.001). On the other hand, the psychological wellness score was higher in survivors than in siblings (p<0.001). Most 44 (43.5%) had average mental capacity. The HRQoL score, a measure of the impact of health conditions on a person's overall well-being, was lower in the Chiari type 2 group than in the other survivors (p=0.035). Serum and folic acid levels did not correlate with HRQoL measures. Conclusions: This study illuminates the quality of life measures in MMC survivors and the Chiari type 2 group and utilizes new MRI findings, which provide groundbreaking insights into the health conditions and well-being of these populations. These findings are of utmost importance for medical professionals, researchers, and healthcare providers specializing in pediatric care and neurology, as they can significantly impact the treatment and care of these patients.Öğe Morphometric and topographic analysis of the flexor carpi radialis tendon, its tunnel, and the median nerve: an MRI-based anatomical guide for wrist interventions(Springer France, 2026) Kaya, Seren; Secgin, Yusuf; Senol, Deniz; Harmandaoglu, Oguzhan; Ozturk, Oguzhan; Onbas, OmerPurpose This study aimed to evaluate the morphometric features of the flexor carpi radialis(FCR) tendon and its tunnel, and their topographical relationships, using wrist magnetic resonance(MR) images. Methods Axial MR images of 190 individuals aged 18-45 years (95 women and 95 men) were retrospectively analysed. Measurements included distances of the FCR tendon to the radial artery (RA), ulnar artery (UA), median nerve (MN), and scaphoid; trapezium tubercle dimensions; FCR tunnel angle, area, and retinacular thicknesses; retinaculum bending ratio; FCR tendon-to-MN area ratio; and FCR tunnel-to-carpal tunnel ratios. The topographic relationship between the FCR tendon and MN was assessed at the levels of the FCR tunnel and pisiform. Positions of the MN and other tendons in the carpal tunnel were also recorded. Results The mean FCR tunnel area was 8990 +/- 2238 mm(2) in males and 7979 +/- 1792 mm(2) in females. Significant sex-related differences were identified in the distances of the FCR to the RA, UA, and MN, as well as in tunnel areas (p < 0.05). Right-left comparisons revealed significant differences in the distances of the FCR to the scaphoid, UA, and MN, in the FCR tunnel area, and in retinacular thickness above and below the tunnel (p < 0.05). Variations were observed in the structures between the FCR tendon and MN, and in MN positioning relative to other tendons. Conclusion The findings may provide an anatomical guide to how FCR-region variations influence invasive procedures, particularly in conditions such as carpal tunnel syndrome and FCR tendinopathy, considering sex and laterality.Öğe Nasal dermoid sinus cyst with intracranial extension and atretic sinus tract in an adult patient(Scientific Scholar LLC, 2026) Gökçe, Ayşe; Onbas, OmerNasal dermoid sinus cyst (NDSC) is a rare congenital lesion due to cephalic neural tube defect. It may also have been seen in other parts of the body. NDSC constitutes 1–3% of all dermoid cysts and 11–12% of those in the head and neck. A NDSC is located in the median line of the nasofrontal region from the glabella to the columella. The differential diagnosis based on location includes epidermal inclusion cyst, glioma, meningomyelocele, meningoencephalocele, teratoma, teratoid cyst, thyroglossal duct cyst, branchial cleft cyst, and benign lymphadenopathy. Intracranial extension causes serious complications such as meningitis, osteomyelitis, abscess formation, hydrocephalus, seizures, and personality changes. All patients with NDSC should be considered to have a potential intracranial extension and should, therefore, undergo pre-operative radiological evaluation. Herein, the authors report an unusual adult patient with NDSC with intracranial extension. © 2026 Published by Scientific Scholar on behalf of Journal of Cutaneous and Aesthetic Surgery.Öğe Sex estimation based on frontal sinus computed tomography images using machine learning and artificial neural networks(Taylor & Francis Ltd, 2025) Kaya, Seren; Harmandaoglu, Oguzhan; Ozturk, Oguzhan; Secgin, Yusuf; Senol, Deniz; Onbas, OmerDue to its anatomical uniqueness, the frontal sinus (FS) shows significant inter-individual differences by ancestry, age, and sex, making it useful for preliminary identification processes. This study aims to estimate sex using machine learning (ML) algorithms and artificial neural networks (ANN) applied to morphometric data from FS computed tomography (CT) images. This retrospective study analysed CT scans of 338 females and 338 males aged 18-65. FS measurements comprised sinus floor anteroposterior length, volume, area, height, depth, width, and anterior wall thickness (AWT). Sex estimation was performed using several ML algorithms, including Linear Discriminant Analysis, Quadratic Discriminant Analysis, Logistic Regression, Extra Trees Classifier, Decision Tree, Random Forest, k-Nearest Neighbours, and Gaussian Naive Bayes. Additionally, a multilayer perceptron classifier, representing ANN models, was utilized. The highest classification accuracy (94%) was achieved by the Logistic Regression model. According to the SHapley Additive exPlanations analysis, the two most influential parameters were identified as the right and left AWT, respectively. This study, with a comparatively large sample size, found that all morphometric FS parameters - especially AWT - hold significant potential in forensic identification. ML- and ANN-based models showed high classification accuracy, surpassing previous studies. These findings may guide future research involving diverse populations and regions.Öğe Sex Estimation Based on Optical Channel Parameters from Computed Tomography Images with Machine Learning Algorithms(Soc Chilena Anatomia, 2025) Ozturk, Oguzhan; Harmandaoglu, Oguzhan; Kaya, Seren; Secgin, Yusuf; Senol, Deniz; Colakoglu, Serdar; Onbas, OmerThe skull is one of the most dimorphic and anatomically informative bones for sex estimation and shows resistance to taphonomic processes. This study aims to estimate sex using machine learning (ML) algorithms based on morphometric measurementof the optic canal (OC)-a clinically significant canal within the sphenoid bone that transmits the optic nerve and ophthalmicrtery. aThis retrospective study was conducted on CT from 260 adults (130 females and 130 males, aged 18-65). The images were obtained from the PACS archive of the Department of Radiology, Faculty of Medicine, D & uuml;zce University, covering the years 2019 to 2025.Sixteen bilateral morphometric parameters of the temporal bone were measured in axial and coronal planes. Data were analysed oususing vari ML algorithms, and classification performance was compared. On the 20 % test set, ML models achieved over 81 % accuracy;icLogist Regression performed best with 90 %. In 10-fold cross-validation, all algorithms exceeded 74 %, with LR again reaching theest highat 89 %. Decision Tree yielded the lowest accuracy. SHapley Additive exPlanations (SHAP), which facilitates interpretable machinelearning, revealed that the right-sided OC-midsagittal distance had the greatest predictive impact. Morphometric data from the OChighprovide accuracy and strong potential for sex estimation. The study also highlights sex-and population-based variation in OC Theseposition. findings may be relevant in clinical and forensic contexts, particularly in forensic anthropology, ophthalmology, and legalmedicine.Öğe Sex estimation with parameters of the facial canal by computed tomography using machine learning algorithms and artificial neural networks(Bmc, 2025) Secgin, Yusuf; Kaya, Seren; Harmandaoglu, Oguzhan; Ozturk, Oguzhan; Senol, Deniz; Onbas, Omer; Yilmaz, NihatBackgroundThe skull is highly durable and plays a significant role in sex determination as one of the most dimorphic bones. The facial canal (FC), a clinically significant canal within the temporal bone, houses the facial nerve. This study aims to estimate sex using morphometric measurements from the FC through machine learning (ML) and artificial neural networks (ANNs).Materials and methodsThe study utilized Computed Tomography (CT) images of 200 individuals (100 females, 100 males) aged 19-65 years. These images were retrospectively retrieved from the Picture Archiving and Communication Systems (PACS) at D & uuml;zce University Faculty of Medicine, Department of Radiology, covering 2021-2024. Bilateral measurements of nine temporal bone parameters were performed in axial, coronal, and sagittal planes. ML algorithms including Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), Decision Tree (DT), Extra Tree Classifier (ETC), Random Forest (RF), Logistic Regression (LR), Gaussian Naive Bayes (GaussianNB), and k-Nearest Neighbors (k-NN) were used, alongside a multilayer perceptron classifier (MLPC) from ANN algorithms.ResultsExcept for QDA (Acc 0.93), all algorithms achieved an accuracy rate of 0.97. SHapley Additive exPlanations (SHAP) analysis revealed the five most impactful parameters: right SGAs, left SGAs, right TSWs, left TSWs and, the inner mouth width of the left FN, respectively.ConclusionsFN-centered morphometric measurements show high accuracy in sex determination and may aid in understanding FN positioning across sexes and populations. These findings may support rapid and reliable sex estimation in forensic investigations-especially in cases with fragmented craniofacial remains-and provide auxiliary diagnostic data for preoperative planning in otologic and skull base surgeries. They are thus relevant for surgeons, anthropologists, and forensic experts.Clinical trial numberNot applicable.Öğe Sex prediction based on computed tomography images of the mandible using deep learning models(Taylor & Francis Ltd, 2026) Secgin, Yusuf; Cakmak, Muhammet; Senol, Deniz; Ozturk, Oguzhan; Kaya, Seren; Harmandaoglu, Oguzhan; Onbas, OmerThe aim of this study is to estimate sex using deep learning methods from mandibular images obtained from computed tomography (CT) scans. In this study, 2310 images were recorded in jpeg format by segmenting the superior, inferior, anterior, posterior, right side, and left side of the mandible from retrospective and randomly scanned CT images belonging to 184 women and 201 men aged 18-65 years. The obtained data were divided into an 80% training set and a 20% test set, and the performance of the deep learning methods ConvNetBase, InceptionV3, Data-Efficient Transformer (DeiT), and the proposed hybrid model were evaluated and compared. In the study, the hybrid model was found to be the most successful model with a 92.50% accuracy rate, 0.0750 lowest error rate (MAE), 92.48% F1-score value, and 0.95 AUC-ROC value. In terms of accuracy, the hybrid model was followed by InceptionV3 (92.17%), ConvNetBase (88.67%), and DeiT (86%). In the Gradient-weighted Class Activation Mapping (Grad-CAM) analysis, it was determined that the middle and lower mandibular regions contributed significantly to sex prediction. We hope that the hybrid model, which showed a high accuracy rate in our study, will guide forensic doctors and anthropologists in sex prediction.Öğe Sex Prediction From the Clavicle Using Computerized Tomography Images via Traditional and Hybrid Deep Learning Models(Wiley, 2026) Secgin, Yusuf; Cakmak, Muhammet; Senol, Deniz; Kaya, Seren; Ozturk, Oguzhan; Harmandaoglu, Oguzhan; Onbas, OmerThe aim of this study is to perform high accuracy sex prediction from clavicle images using proposed hybrid deep learning models and traditional deep learning models. The clavicle of 807 female and 805 male individuals obtained from Computed Tomography were segmented in 3D format and saved in jpeg format as superior-inferior and right-left. MobileNetV2, DenseNet201, and ResNet101 traditional deep learning models and the proposed MobileNetV2+Multilayer Perceptron (MLP) and MobileNetV2+MLP+t-distributed Stochastic Neighborhood Embedding (t-SNE) based hybrid deep learning models were trained with the training set. The training was performed both with and without right-left side discrimination. The performance of each model was evaluated and compared. The highest accuracy rate of 91% was obtained in the training with both proposed hybrid models without side discrimination. The highest success rate obtained with the proposed models was 88%. The lowest accuracy rates were achieved with ResNet101. The accuracy rate was 81% in the analysis with side discrimination and 83% in the analysis without discrimination. According to Grad-Cam, the extremitas sternalis tip contributed the most to accuracy. In this study, MobileNetV2+MLP and MobileNetV2+MLP+t-SNE provided highly accurate results in sex prediction. This approach is a potential new method that can be used in sex estimation, especially in forensic medicine, as it allows the collection of features with MobileNetV2, classification with MLP, visualization with t-SNE, and observation of errors without metric measurement, directly from the superior and inferior available images of the clavicle.Öğe Sex prediction using machine learning algorithms with parameters obtained from CT images of the Canaliculi pterygoidei(Taylor & Francis Ltd, 2026) Secgin, Yusuf; Ozturk, Oguzhan; Kaya, Seren; Harmandaoglu, Oguzhan; Senol, Deniz; Onbas, OmerIn this study, our aim is to predict sex using machine learning (ML) algorithms with morphometric variables taken from the vidian canal. The study was performed on Computed Tomography (CT) images of 137 women and 137 men aged 18-65 years. Seventeen morphometric variables were determined and recorded from axial and coronal skull images. These numerical data were used for sex determination with ML algorithms. As a result of the ML algorithm analysis of the data as 80% training set and 20% test set, the highest Accuracy (Acc.) rate was 0.89 with the Extra Tree Classifier (ETC) algorithm, and the lowest was 0.78 with Quadratic Discriminant Analysis (QDA). Among the parameters, the distance between the optic canal and the vidian canal was found to have the highest effect on sex determination with the Shapley Additive Explanations analyser. As a result of 10-fold cross-validation analysis of ML algorithms, the highest Acc rate was 0.82 with Random Forest (RF) algorithm, and the lowest was 0.73 with QDA. The findings of the study reveal that morphometric variables from the vidian canal show high accuracy in sex determination.Öğe Shear wave elastography and T2*mapping in the detection of early-stage trochlear cartilage damage(Sage Publications Ltd, 2023) Kaplan, Meral; Guclu, Derya; Unlu, Elif Nisa; Ogul, Hayri; Onbas, OmerBackground The presence of degenerative changes in joint cartilage is one of the major features in osteoarthritis. Purpose To investigate the contribution of shear wave elastography and T2* mapping to the early diagnosis of femoral trochlear cartilage damage. Material and Methods A total of 30 individuals whose trochlear cartilage structure was evaluated as normal in conventional magnetic resonance imaging (MRI) sequences (control group) were prospectively compared with 30 patients who had early-stage cartilage damage findings on conventional MRI (study group), by performing B-mode ultrasonography, shear wave elastography, and T2* mapping. Cartilage thickness, shear wave, and T2* mapping measurements were recorded. Results After evaluating B-mode ultrasound and conventional MRI sequences, cartilage thickness was found to be significantly higher in the study group on both B-mode ultrasound and MRI. Shear wave velocity values of the study group (medial condyle [MC] 4.65 & PLUSMN; 1.11 m/sn, intercondylar [IC] 4.74 & PLUSMN; 1.20 m/sn, and lateral condyle [LC] 5.42 & PLUSMN; 1.48 m/sn) were observed to be significantly lower than the control group (MC 5.60 & PLUSMN; 0.77 m/sn, IC 5.85 & PLUSMN; 0.96 m/sn, and LC 5.63 & PLUSMN; 1.05 m/sn) (P < 0.05). T2* mapping values were significantly higher in the study group (MC 32.38 & PLUSMN; 4.04 ms, IC 35.78 & PLUSMN; 4.85 ms, and LC 34.04 & PLUSMN; 3.40 ms) than that of the control group (MC 28.07 & PLUSMN; 3.29 ms, IC 30.63 & PLUSMN; 3.45 ms, and LC 29.02 & PLUSMN; 3.24 ms). Conclusion Shear wave elastography and T2* mapping are reliable methods for evaluating early-stage trochlear cartilage damage.Öğe Visual assessment of cerebrospinal fluid flow dynamics using 3D T2-weighted SPACE sequence-based classification system(Sage Publications Ltd, 2024) Naldemir, Ibrahim Feyyaz; Karaman, Ahmet Kursat; Ogul, Hayri; Onbas, OmerBackground: Flow-related signal void artifacts can be visualized on the T2-weighted (T2W) three-dimensional sampling perfection with application-optimized contrast (3D-SPACE) sequence. Flow void artifacts in the cerebral aqueduct and the fourth ventricle can provide information about cerebrospinal fluid (CSF) flow dynamics. Purpose: In this study, we aimed to test the performance of the T2W 3D-SPACE sequence in assessing the CSF flow in the aqueduct and/or fourth ventricle. Material and Methods: A total of 137 patients (age range = 3-89 years) who underwent CSF flow study were included. The amount of signal loss on T2W 3D-SPACE due to flow in the aqueduct and fourth ventricle was assessed and graded using a 4-point scale of 0 (absence of flow void) to 3 (signal void filling the aqueduct and entire fourth ventricle). A correlation was then sought between the quantitative values obtained by phase-contrast magnetic resonance imaging (PC-MRI) and the amount of signal void in the 3D-SPACE sequence. Results: At the aqueduct level, there was a statistically significant difference in the forward flow velocity and the flow volume among different grades (all P < 0.001). In the grade 3 group, CSF peak systolic flow velocity and mean flow volume were found to be significantly higher than in the other grades (P < 0.001). The mean aqueduct area in the grade 0 group was found to be significantly different from that in the other classes (P < 0.001). Conclusion: The amount of signal loss in the fourth ventricle observed on T2W 3D-SPACE is correlated with the peak systolic velocity and flow volume measured quantitatively in PC-MRI.
