Background: Differentiation between malignant and benign vertebral compression fracture is often problematic. This is precisely difficult in elderly who are predisposed to benign compression caused by osteoporosis .Establishing correct diagnosis is of great importance in determining the treatment andprognosis.A study was performed to determine which magnetic resonance imaging findings are useful in discrimination between metastatic and acute osteoporotic compression fractures of the spine. Recently MRI is being increasingly used for evaluation of these fractures.Objectives: The aim of this study is to establish the correct diagnosis of malignant and benign compression vertebral fracture by MRI to determine treatment and prognosis.Methods: MRI of (45 )patients with vertebral compression fracture who underwent MRI of spine during 2 years period ( 2004-2006) in Neuro surgical hospital were retrospectively reviewed by an expedient radiologist , A collapsed vertebra were considered to be acute if there was recent history of back pain of less than 3moths. All MRI sequences needed for spine examination was done( T1WI SE, T2WI GE STIR, and Mylography ) and Gadolinium contrast medium was given to all patients..Results: Of total 45 patients (31male, 14 female, their age range between 18-72years) , The criteria used to differentiate between benign andmalignant collapse spine on MRI was based on the signal intensity, morphology and contrast enhancement for the correct diagnosis. Homogenous and diffuse abnormal signal intensity, posterior convexity and involvement of pedicles are sign that are strongly suggestive of malignant collapse .Conversely, a band like area of low signal intensity adjacent to depressed end plateand preservation of signal intensity of vertebra suggest benign nature of the collapse.Conclusions: We found that MRI features is helpful in differentiation of malignant and benign compression fractures in majority of cases, and if initial MRI finding are equivocal correlation with other images technique, follow up and biopsy in selected cases helpful in arriving correct diagnosis
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This paper presents the application of a framework of fast and efficient compressive sampling based on the concept of random sampling of sparse Audio signal. It provides four important features. (i) It is universal with a variety of sparse signals. (ii) The number of measurements required for exact reconstruction is nearly optimal and much less then the sampling frequency and below the Nyquist frequency. (iii) It has very low complexity and fast computation. (iv) It is developed on the provable mathematical model from which we are able to quantify trade-offs among streaming capability, computation/memory requirement and quality of reconstruction of the audio signal. Compressed sensing CS is an attractive compression scheme due to its uni
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The brief description to the theory of propagation of electromagnetic waves in plasma was done. The cutoff and resonance regions have been showed. The principles of plasma heating at electron cyclotron resonance (ECRH) method have been mentioned. The numerical simulation to three different station: Tosca station in United Kingdom, ISX-B station in USA and T-10 station in Russia had been done. The optical depth and the friction of energy absorbed A have been calculated. The simulation results indicate that both and A are increase with size of the tokamak and it is possible to obtain full absorption in large tokamak.
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Information security is a crucial factor when communicating sensitive information between two parties. Steganography is one of the most techniques used for this purpose. This paper aims to enhance the capacity and robustness of hiding information by compressing image data to a small size while maintaining high quality so that the secret information remains invisible and only the sender and recipient can recognize the transmission. Three techniques are employed to conceal color and gray images, the Wavelet Color Process Technique (WCPT), Wavelet Gray Process Technique (WGPT), and Hybrid Gray Process Technique (HGPT). A comparison between the first and second techniques according to quality metrics, Root-Mean-Square Error (RMSE), Compression-
... Show MoreTraumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental
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