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Gliomas are the most common type of primary brain tumors and one of the highest causes of mortality worldwide. Accurate grading of gliomas is of immense importance to administer proper treatment plans. In this paper, we develop a comprehensive non-invasive multimodal magnetic resonance (MR)-based computer-aided diagnostic (CAD) system to precisely differentiate between different grades of gliomas (Grades: I, II, III, and IV). A total of 99 patients with gliomas (M = 49, F = 50, age range = 1–79 years) were included after providing their informed consent to participate in this study. The proposed imaging-based glioma grading (GG-CAD) system utilizes three different MR imaging modalities, namely; contrast-enhanced T1-MR, T2-MR known as fluid-attenuated inversion-recovery (FLAIR), and diffusion-weighted (DW-MR) to extract the following imaging features: (i) morphological features based on constructing the histogram of oriented gradients (HOG) and estimating the glioma volume, (ii) first and second orders textural features by constructing histogram, gray-level run length matrix (GLRLM), and gray-level co-occurrence matrix (GLCM), (iii) functional features by estimating voxel-wise apparent diffusion coefficients (ADC) and contrast-enhancement slope. These features are then integrated together and processed using a Gini impurity-based selection approach to find the optimal set of significant features. The reduced significant features are then fed to a multi-layer perceptron artificial neural networks (MLP-ANN) classification model to obtain the final diagnosis of a glioma tumor as Grade I, II, III, or IV. The GG-CAD system was evaluated on the enrolled 99 gliomas (Grade I = 13, Grade II = 22, Grade III = 22, and Grade IV = 42) using a leave-one-subject-out (LOSO) and k-fold stratified (with k = 5 and 10) cross-validation approach. The GG-CAD achieved 0.96 ± 0.02 quadratic-weighted Cohen’s kappa and 95.8% ± 1.9% overall diagnostic accuracy at LOSO and an outstanding diagnostic performance at k = 10 and 5. Alternative classifiers, including RFs and SVMlin produced inferior results compared to the proposed MLP-ANN GG-CAD system. These findings demonstrate the feasibility of the proposed CAD system as a novel tool to objectively characterize gliomas using the comprehensive extracted and selected imaging features. The developed GG-CAD system holds promise to be used as a non-invasive diagnostic tool for Precise Grading of Glioma.

Background Olfactory dysfunction has been reported in 47.85% of COVID patients. It can be broadly categorized into conductive or sensorineural olfactory loss. Conductive loss occurs due to impaired nasal air flow, while sensorineural loss implies dysfunction of the olfactory epithelium or central olfactory pathways. Objectives The aim of this study was to analyze the clinical and imaging findings in patients with COVID-related olfactory dysfunction. Additionally, the study aimed to investigate the possible mechanisms of COVID-related olfactory dysfunction. Methods The study included 110 patients with post-COVID-19 olfactory dysfunction, and a control group of 50 COVID-negative subjects with normal olfactory function. Endoscopic nasal examination was performed for all participants with special focus on the olfactory cleft. Smell testing was performed for all participants by using a smell diskettes test. Olfactory pathway magnetic resonance imaging (MRI) was done to assess the condition of the olfactory cleft and the dimensions and volume of the olfactory bulb. Results Olfactory dysfunction was not associated with nasal symptoms in 51.8% of patients. MRI showed significantly increased olfactory bulb dimensions and volume competed to controls. Additionally, it revealed olfactory cleft edema in 57.3% of patients. On the other hand, radiological evidence of sinusitis was detected in only 15.5% of patients. Conclusion The average olfactory bulb volumes were significantly higher in the patients’ group compared to the control group, indicating significant edema and swelling in the olfactory bulb in patients with COVID-related olfactory dysfunction. Furthermore, in most patients, no sinonasal symptoms such as nasal congestion or rhinorrhea were reported, and similarly, no radiological evidence of sinusitis was detected. Consequently, the most probable mechanism of COVID-related olfactory dysfunction is sensorineural loss through virus spread and damage to the olfactory epithelium and pathways.

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This study aimed to assess the utility of DTI in the detection of olfactory bulb dysfunction in COVID-19-related anosmia. It was performed in 62 patients with COVID-19-related anosmia and 23 controls. The mean diffusivity and fractional anisotropy were calculated by 2 readers. The difference between the fractional anisotropy and mean diffusivity values of anosmic and control olfactory bulbs was statistically significant (P ¼ .001). The threshold of fractional anisotropy and mean diffusivity to differentiate a diseased from normal olfactory bulb were 0.22 and 1.5, with sensitivities of 84.4% and 96.8%, respectively, and a specificity of 100%.

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Background: Cerebral venous thrombosis is uncommon disease with vague manifestations. Magnetic resonance venography (MRV) plays an important role in its diagnosis. The cardinal MRV sign of venous sinus thrombosis is sinus drop of signal. Using time of flight MR venography (TOF MRV) -the commonly used technique there are multiple causes of signal drop simulating dural sinus thrombosis. Aim of Study: To diagnose different causes of signal drop on TOF MRV using contrast enhanced MRV to avoid misinterpretation of dural sinus thrombosis.Conclusion: Drop of signal in dural venous sinuses using TOF MRV has many causes other than thrombosis. To avoid misinterpretation it is important to use contrast enhanced MRV either TRICKS or elliptic centric techniques.

Background: With widespread use of pediatric head CT, it is critically important to protect patients from radiation hazards, using reduced dose CT techniques. In this regard, adaptive statistical iterative reconstruction-V (ASIR-V) algorithm can decrease image noise, generating CT images of reasonable diagnostic quality with less radiation. The objective of this study was radiation dose assessment, quantitative and qualitative evaluation of reduced dose pediatric head CT using ASIR-V 60% and 80% reconstruction. Results: Retrospective analysis was performed on two groups of pediatric head CT examinations, a reduced dose CT examination group with ASIR-V reconstruction (ASIR group) (n = 27) and a standard dose CT examination group without ASIR reconstruction (non-ASIR group) (n = 14). The average effective dose (ED) of ASIR group was significantly lower than that of the non-ASIR group (1.04 ± 0.1 mS vs 3.48 ± 0.45 mS; p = 0.001). Quantitative analysis revealed comparable results of signal to noise ratio (SNR) and contrast to noise ratio (CNR) of ASIR and non-ASIR groups (p > 0.05). Qualitative evaluation of resulting images by two readers revealed comparable results of both ASIR and non-ASIR groups (p > 0.05) with excellent inter-reader agreement (κ = 0.97). Both quantitative and qualitative assessment demonstrated better ASIR-V 80% than ASIR-V 60% reconstructed images. Conclusion: ASIR-V algorithm is a promising technology for effective dose reduction of pediatric head CT with preservation of diagnostic image quality.