Background: Multiple sclerosis is a chronic autoimmune inflammatory demyelinating disease of the central nervous system of unknown etiology. Different techniques and magnetic resonance image sequences are widely used and compared to each other to improve the detection of multiple sclerosis lesions in the spinal cord. Objective: To evaluate the ability of MRI short tau inversion recovery sequences in improvementof multiple sclerosis spinal cord lesion detection when compared to T2 weighted image sequences. Type of the study: A retrospective study. Methods: this study conducted from 15thAugust 2013 to 30thJune 2014 at Baghdad teaching hospital. 22 clinically definite MS patients with clinical features suggestive of spinal cord involvement, patients were imaged with sagittal short tau inversion recovery sequences and sagittal T2 weighted. Results: The mean age of the patients was 32.5 ± 6.7years; female to male ratio was 2.7:1. The total number of spinal cord MS lesions was 44 of them 86.4% in the cervical spine, 68.2%of the lesions had less than one vertebra extension,79.6% of the lesions did not show changes in the spinal cord morphology. There was a significant upgrading in the lesions conspicuity at short tau inversion recovery sequence comparing to T2 weighted image, P<0.001. A significant difference had been found in artifact grading between both sequences; P<0.001. Conclusions: short tau inversion recovery magnetic resonance image sequences improve detection of MS spinal cord plaques compared with T2 weighted image and itincreasesthe conspicuity of the visualized T2weighted image lesions, but also it accentuates theartifacts more than T2weighted image.
Praise be to God, Lord of the Worlds, who has made His way for His servants who know in the depths and who are devoted to Him at all times and times, so He has delivered to Himself their pain from two exiles, and His prayers and peace are the perfect trust in the presence of the pearl of charity of existence. Possessor of great intercession and praiseworthy status until the day of meeting and eternity, and upon his family and companions, the people of tomorrow, steadfastness, generosity, and generosity, and after that
For a long period of time, I have been asking myself: Why did he attack so many contemporary books, especially Sufism? Why this unlimited financial expenditure on printing, binding, beautifying and enticing readers?
... Show MoreKE Sharquie, MM Al-Waiz, AA Al-Nuaimy, Saudi medical journal, 2002 - Cited by 11
In this paper, a Cholera epidemic model is proposed and studied analytically as well as numerically. It is assumed that the disease is transmitted by contact with Vibrio cholerae and infected person according to dose-response function. However, the saturated treatment function is used to describe the recovery process. Moreover, the vaccine against the disease is assumed to be utterly ineffective. The existence, uniqueness and boundedness of the solution of the proposed model are discussed. All possible equilibrium points and the basic reproduction number are determined. The local stability and persistence conditions are established. Lyapunov method and the second additive compound matrix are used to study the global stability of the system.
... Show MoreThe performance quality and searching speed of Block Matching (BM) algorithm are affected by shapes and sizes of the search patterns used in the algorithm. In this paper, Kite Cross Hexagonal Search (KCHS) is proposed. This algorithm uses different search patterns (kite, cross, and hexagonal) to search for the best Motion Vector (MV). In first step, KCHS uses cross search pattern. In second step, it uses one of kite search patterns (up, down, left, or right depending on the first step). In subsequent steps, it uses large/small Hexagonal Search (HS) patterns. This new algorithm is compared with several known fast block matching algorithms. Comparisons are based on search points and Peak Signal to Noise Ratio (PSNR). According to resul
... Show MoreAdvanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m
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