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Complementary role of computer assisted models using machine learning methods in medical imaging has been a center of attention in recent years. Shape analysis of the brain structures can be used to evaluate their abnormalities and deformations, specifically in patients suffering from neurological diseases like epilepsy, Alzheimer, and Parkinson. We propose an automatic diagnosis and lateralization...
For electrophysiology procedures, obtaining the information of scar within the left ventricle is very important for diagnosis, therapy planning and patient prognosis. The clinical gold standard to visualize scar is late-gadolinium-enhanced-MRI (LGE-MRI). The viability assessment of the myocardium often requires the prior segmentation of the left ventricle (LV). To overcome this problem, we propose...
A parasitic superdirective electrically small coil array based on printed loop antenna for magnetic resonance imaging (MRI) applications is presented. The proposed coil array is composed of two coils wherein one of the coils is excited and the other is loaded with a tuning capacitor acting as a director to generate directional near-field distribution. The coils are with identical diameter of 30 mm...
Sodium Magnetic Resonance Imaging (MRI) is an attractive imaging technique for early detection of osteoarthritis, as sodium concentrations correlate with proteoglycan content. However sodium yields much less signal in human tissues compared to hydrogen: it has a very rapid transverse signal decay (T2) and rapid longitudinal relaxation (T1), low gyromagnetic ratio and much smaller concentration than...
In this paper, a swarm intelligence based neural network named as Particle Swarm Optimization — Functional Link Multi Layer Perceptron (PSO-FLMLP) is proposed for suppression of Rician noise from Magnetic Resonance Imaging (MRI) images. The performance of PSO-FLMLP is also compared with three other competitive networks such as Multi Layer Perceptron (MLP), Least Mean Square based Functional Link Artificial...
We consider the application of Compressed Sensing (CS) to enhance the acquisition speed in Magnetic Resonance Imaging (MRI). For CS-based MRI, random sampling is often implemented in the k-space and depends on the uniform distribution and energy distribution of MRI images in the k-space. In contrast, we propose a new deterministic sampling method for CS-based MRI using the logistic map, which has...
Elastography is an efficient alternative to the traditional palpation method of assessing tissue stiffness. Magnetic resonance imaging (MRI) provides a three-dimensional (3D) high-resolution view of the surrounding anatomy during interventions. Therefore, the development of MRI-based elastographic strategies is desirable for multiple clinical applications. In this work, we developed a new transient...
Magnetic Resonance Imaging (MRI) has been widely used in medical diagnose because of its non-invasive manner and excellent depiction of soft-tissue changes. Recently, the compressive sensing (CS) theory has been applied to reconstruct the MR image from highly down-sampled k-space data, which can reduce the scanning duration. To obtain useful information as much as possible with the same sampling rate,...
Magnetic resonance imaging (MRI) is a technique which is used for the evaluation of the brain tumor in medical science. In this paper, a methodology to study and classify the image de-noising filters such as Median filter, Adaptive filter, Averaging filter, Un-sharp masking filter and Gaussian filter is used to remove the additive noises present in the MRI images i.e. Gaussian, Salt & pepper noise...
In this work, a novel implantable medical stent is designed to reduce the radio frequency (RF)-induced heating from implantable medical stent under magnetic resonance imaging (MRI) procedure. By using segmented structure, the induced specific absorption rate (SAR) for a 100 mm length stent is reduced from 54.4 mW/g to 13.4 mW/g. Numerical simulation demonstrates the effectiveness of the designed structure.
The count of tumor patients is increasing day by day. Brain tumor, whose main cause is the uncontrolled division of the cells, if detected at an early stage, will help a lot in curing it. Various detection techniques are available for identifying the abnormality in the brain, but, MRI is a better technique in comparison to others. This paper presents a method for distinguishing the tumor affected...
This paper presents a robust segmentation method which is the integration of Template based K-means and modified Fuzzy C-means (TKFCM) clustering algorithm that, reduces operators and equipment error. In this method, the template is selected based on convolution between gray level intensity in small portion of brain image, and brain tumor image. K-means algorithm is to emphasized initial segmentation...
Developing automatic and accurate computer-aided diagnosis (CAD) systems for detecting brain disease in magnetic resonance imaging (MRI) are of great importance in recent years. These systems help the radiologists in accurate interpretation of brain MR images and also substantially reduce the time needed for it. In this paper, a new system for abnormal brain detection is presented. The proposed method...
Local contractility evaluation is a promising area of cardiovascular research. We propose strain (local deformation measure) calculation method based on 2d non-rigid image registration on CINE magnetic resonance imaging data. First we register pairs of consecutive images in time series then we apply the results to whole study. Presented 2d approach is stable and faster then 3d or 3d+t methods. We...
We compare three anatomical sheep models to assess possible heating effects of magnetic resonance imaging. We generate i) homogenous, ii) three-layered, and iii) seven-tissue models and compute specific absorption rate (SAR) and temperature increase on those models. We conclude that the homogenous model overestimates both SAR and heating whereas the three-tissue model underestimates those (although...
Edge detection plays a vital role in medical imaging applications such as MRI segmentation. Magnetic resonance imaging (MRI) is an imaging technique used in medical science to diagnose tumors of the brain by producing high quality images of the inside of the human body, by using various edge detectors. There exists many edge detector but still, need for research is felt in order to enhance their performance...
Ultra-High Field (UHF) (4–9.4T) Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) are valuable tools in the diagnosis and monitoring of many diseases thanks to the enhanced Signal-to-Noise Ratio (SNR) and spectral/spatial resolution. However, such UHF MR applications require the development and optimization of specially designed Radio Frequency (RF) coils. In this study we report the design,...
Magnetic Resonance (MR) Imaging and Spectroscopy of the muscle is a valuable tool in the diagnosis and monitoring of Neuromuscular Disorders (NMD). New Ultra-High Field (UHF) 7 T MRI systems, with their enhanced Signal-to-Noise Ratio, may offer increased image quality in terms of spatial resolution and/or shorter scanning time compared to lower field systems. In the study of NMD the new features provided...
Cognitive decline in old age is tightly linked with brain atrophy, causing significant burden. It is critical to identify which biomarkers are most predictive of cognitive decline and brain atrophy in the elderly. In 566 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we used a novel unsupervised machine learning approach to evaluate an extensive list of more than 200 potential...
Clinical magnetic resonance imaging (MRI) protocols typically include multiple acquisitions of the same region of interest under different contrast settings. This paper presents an efficient algorithm to jointly reconstruct a set of undersampled images with different contrasts. The proposed method has faster reconstruction time and better quality as measured by the normalized root-mean-square error...
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