Magnetic resonance imaging based classification of anatomic relationship between the cochleovestibular nerve and anterior inferior cerebellar artery in patients with non-specific neuro-otologic symptoms

2005 ◽  
Vol 27 (6) ◽  
pp. 531-535 ◽  
Author(s):  
Akif Sirikci ◽  
Yildirim Bayazit ◽  
Enver Ozer ◽  
Ayhan Ozkur ◽  
İbrahim Adaletli ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Anterior Inferior Cerebellar Artery ◽  
Resonance Imaging ◽  
Cerebellar Artery ◽  
Anatomic Relationship

scholarly journals Evaluation of the Relationship Between the Internal Acoustic Canal Anatomy and Loops of the Anterior Inferior Cerebellar Artery Using 3-Tesla Magnetic Resonance Imaging

2021 ◽  
Vol 18 (4) ◽  
Author(s):  
Murat Bayav ◽  
Murat Sahin
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Anatomical Variations ◽  
3 Tesla ◽  
Anterior Inferior Cerebellar Artery ◽  
Resonance Imaging ◽  
Cerebellar Artery ◽  
The Relationship ◽  
Internal Acoustic Canal ◽  
Vascular Loop

Background: Anatomical variations in vascular structures are frequently encountered. In the cerebellopontine region, anatomical variations, especially variations in the anterior inferior cerebellar artery (AICA) in relation to cranial nerves passing through the internal acoustic canal (IAC), are common. Magnetic resonance imaging (MRI) provides detailed information for the evaluation of the cerebellopontine region and the IAC anatomy. Objectives: This study aimed to examine the relationship between the IAC anatomy and AICA loop variations and to investigate the frequency of anatomical variations and its correlation with demographic variables. Patients and Methods: In this study, 253 patients (age > 18 years), who underwent 3-Tesla MRI of the temporal bone between July 2019 and December 2019, were retrospectively evaluated. The exclusion criteria were images of poor diagnostic quality due to technical or patient-related reasons and the presence of a localized tumor that could disrupt the IAC. Four patients were excluded from the study because they had tumors related to IAC. The AICA variations were categorized according to the Chavda classification. The IAC size, canal diameter, canal length, and meatus width were measured, and the canals were characterized as cylindrical, funnel-shaped, and bud-shaped. Results: A total of 249 patients were included in this study. The Chavda type I vascular loop was the most common type on both sides. The mean size measurements of IACs were significantly higher in the group of vascular loops compared to the group without vascular loops. The presence of a vascular loop was significantly more common in funnel- and bud-shaped IACs compared to cylindrical IAC. Also, there was a negative correlation between the patient’s age and all IAC dimensions. Conclusion: The results of the present study showed that the AICA loop variations were closely related to the size and shape of the IAC.

scholarly journals Case report: the etiology of anterior inferior cerebellar artery infarction: what does basi-parallel anatomic scanning magnetic resonance imaging tell us?

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhi-yong Zhang ◽  
Zhi Zhou ◽  
Hai-bo Zhang ◽  
Jin-song Jiao
Keyword(s):  
Magnetic Resonance Imaging ◽  
Case Report ◽  
Magnetic Resonance ◽  
Diagnostic Value ◽  
Anterior Inferior Cerebellar Artery ◽  
Resonance Imaging ◽  
Outer Contour ◽  
Case Presentation ◽  
Cerebellar Artery

Abstract Background The precise etiology of anterior inferior cerebellar artery (AICA) infarction is difficult to identify because of the high anatomic variability of vertebrobasilar arteries and the limitations of conventional vascular examinations. Basi-parallel anatomic scanning magnetic resonance imaging (BPAS-MRI) can reveal the outer contour of the intracranial vertebrobasilar arteries, which may be helpful to distinguish the arteriosclerosis from congenital dysplasia and dissection. Case presentation In this study, we reported 3 cases of AICA infarction and discussed the diagnostic value of BPAS-MRI in the evaluation of vascular etiology. Conclusions The BPAS-MRI could be considered as an important supplementary in the diagnosis of vascular etiology of infarction in AICA territory.

scholarly journals Vascular loops in the anterior inferior cerebellar artery, as identified by magnetic resonance imaging, and their relationship with otologic symptoms

2016 ◽  
Vol 49 (5) ◽  
pp. 300-304 ◽  
Author(s):  
Luiz de Abreu Junior ◽  
Cristina Hiromi Kuniyoshi ◽  
Angela Borri Wolosker ◽  
Maria Lúcia Borri ◽  
Augusto Antunes ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Hearing Loss ◽  
Magnetic Resonance ◽  
Interobserver Agreement ◽  
Anterior Inferior Cerebellar Artery ◽  
Internal Auditory Meatus ◽  
Resonance Imaging ◽  
Chi Square ◽  
Cerebellar Artery ◽  
Chi Square Tests

Abstract Objective: To use magnetic resonance imaging to identify vascular loops in the anterior inferior cerebellar artery and to evaluate their relationship with otologic symptoms. Materials and Methods: We selected 33 adults with otologic complaints who underwent magnetic resonance imaging at our institution between June and November 2013. Three experienced independent observers evaluated the trajectory of the anterior inferior cerebellar artery in relation to the internal auditory meatus and graded the anterior inferior cerebellar artery vascular loops according to the Chavda classification. Kappa and chi-square tests were used. Values of p < 0.05 were considered significant. Results: The interobserver agreement was moderate. Comparing ears that presented vascular loops with those that did not, we found no association with tinnitus, hearing loss, or vertigo. Similarly, we found no association between the Chavda grade and any otological symptom. Conclusion: Vascular loops do not appear to be associated with otoneurological manifestations.

Survey of denoising, segmentation and classification of magnetic resonance imaging for prostate cancer

2021 ◽  
Author(s):  
Mamta Juneja ◽  
Sumindar Kaur Saini ◽  
Jatin Gupta ◽  
Poojita Garg ◽  
Niharika Thakur ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Resonance Imaging

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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