Thorsenlunde0664

A significant positive relationship between cyst/tumor size and number of impaired hormones was observed in both groups, but smaller cysts could cause hormone secretion impairment in RCC. Stimulation tests suggested that most hormone secretion impairment was attributable to the interrupted hypothalamic-pituitary axis in both groups. Therefore, RCC, even small ones, can cause pituitary dysfunction. Different mechanisms may underlie hypothalamic-pituitary interruption in RCC and NFA.

Virtual radiographic simulation has been found educationally effective for students to practice their clinical examinations remotely or online. A free available virtual simulator-ImaSim has received particular attention for radiographic science education because of its portability, free of charge and no constrain of location and physical facility. However, it lacks evidence to validate this virtual simulation software to faithfully reproduce radiographs comparable to that taken from a real X-ray machine to date.

To evaluate image quality of the virtual radiographs produced by the ImaSim. Thus, the deployment of this radiographic simulation software for teaching and experimental studying of radiography can be justified.

A real medical X-ray examination machine is employed to scan three standard QC phantoms to produce radiographs for comparing to the corresponding virtual radiographs generated by ImaSim software. The high and low range of radiographic contrast and comprehensive contrast-detail performancedesign in radiography.

To demonstrate the ability of achieving low dose and high-quality head CT images for children with acute head trauma using 100 kVp and adaptive statistical iterative reconstruction (ASIR-V) algorithm in single rotation on a 16 cm wide-detector system.

We retrospectively analyzed the CT dose index (CTDI) and image quality of 104 children aged 0-6 years with acute head trauma (1 hour -3 days) in two groups Group 1(n = 50) on a 256-row CT with single rotation at a reduced-dose of 100 kVp/240 mA and reconstructed using ASIR-V at 70%level; Group 2(n = 54) on a 64-row CT with multiple rotations at a standard dose of 120 kVp/ 180mA and reconstructed using a conventional filtered back-projection (FBP). Both groups used the 0.5 s/r axial scan mode. CT dose index (CTDI) and quantitative image quality measurements were compared using the Student t test; qualitative image quality comparison was carried out using Mann-Whitney rank test and the inter-reviewer agreement was evaluated using Kappa test.

The exposure timred with standard dose protocol. Thus, it provides a novel imaging method in management of pediatric acute head trauma.Multi-modal image fusion techniques aid the medical experts in better disease diagnosis by providing adequate complementary information from multi-modal medical images. These techniques enhance the effectiveness of medical disorder analysis and classification of results. This study aims at proposing a novel technique using deep learning for the fusion of multi-modal medical images. The modified 2D Adaptive Bilateral Filters (M-2D-ABF) algorithm is used in the image pre-processing for filtering various types of noises. The contrast and brightness are improved by applying the proposed Energy-based CLAHE algorithm in order to preserve the high energy regions of the multimodal images. Images from two different modalities are first registered using mutual information and then registered images are fused to form a single image. In the proposed fusion scheme, images are fused using Siamese Neural Network and Entropy (SNNE)-based image fusion algorithm. Particularly, the medical images are fused by using Siamese convolutional neural network structure and the entropy of the images. selleck inhibitor Fusion is done on the basis of score of the SoftMax layer and the entropy of the image. The fused image is segmented using Fast Fuzzy C Means Clustering Algorithm (FFCMC) and Otsu Thresholding. Finally, various features are extracted from the segmented regions. Using the extracted features, classification is done using Logistic Regression classifier. Evaluation is performed using publicly available benchmark dataset. Experimental results using various pairs of multi-modal medical images reveal that the proposed multi-modal image fusion and classification techniques compete the existing state-of-the-art techniques reported in the literature.

To investigate whether the baseline apparent diffusion coefficient (ADC) can predict survival in the hepatocellular carcinoma (HCC) patients receiving chemoembolization.

Diffusion-weighted MR imaging of HCC patients is performed within 2 weeks before chemoembolization. The ADC of the largest index lesion is recorded. Responses are assessed by mRECIST after the start of the second course of chemoembolization. Receiver operating characteristic (ROC) curve analysis is performed to evaluate the diagnostic performance and determine optimal cut-off values. Cox regression and Kaplan-Meier survival analyses are used to explore the differences in overall survival (OS) between the responders and non-responders.

The difference is statistically significant in the baseline ADC between the responders and non-responders (P < 0.001). ROC analyses indicate that the baseline ADC value is a good predictor of response to treatment with an area under the ROC curve (AUC) of 0.744 and the optimal cut-off value of 1.22×10-3 mm2/s. The Cox regression model shows that the baseline ADC is an independent predictor of OS, with a 57.2% reduction in risk.

An optimal baseline ADC value is a functional imaging response biomarker that has higher discriminatory power to predict tumor response and prolonged survival following chemoembolization in HCC patients.

An optimal baseline ADC value is a functional imaging response biomarker that has higher discriminatory power to predict tumor response and prolonged survival following chemoembolization in HCC patients.

This cross-sectional study aims to describe the features of the suppression head impulse paradigm (SHIMP) in acute unilateral vestibulopathy (AUV) and to define its role in predicting the recovery of patients.

Thirty patients diagnosed with AUV were retrospectively analyzed. The dizziness handicap inventory score and video head impulse test parameters performed 4-8 weeks from the AUV onset constituted the main outcome measures. Patients with a worse recovery (Group 1) and patients who recovered spontaneously (Group 2) were compared.

The SHIMP vestibulo-ocular reflex (VOR) gain was statistically significantly lower than the conventional head impulse paradigm (HIMP) VOR gain (P <  0.001). The SHIMP VOR gain was negatively correlated with the DHI (P <  0.001) and was positively correlated with the HIMP VOR gain (P <  0.001) and the SHIMP overt saccades (%) (P <  0.001). Patients with a worse recovery exhibited the following higher DHI (P <  0.001), lower SHIMP and HIMP VOR gain (P <  0.001 and P = 0.