F Mri Research Papers - Academia.edu (original) (raw)

In this paper we present an approach to detect whether an MRI scan of a brain contains a tumor or not using machine learning. Once detected, it will then classify the type of tumor as either benign or malignant. In any medical field, the most important resource used by doctors is Medical Images which is a tool with high accuracy. In this work, the system correctly classifies MRI images into images with tumor and images without tumor. This has to be done with no human intervention. In order to apply several types of classifiers, there is a need to pre-process several aspects of the images such as the color, area of interest, image file extension, and contrast level.

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- F Mri, Magnetic Resonance Imaging, MRI, Functional MRI

MRI is the most important technique, in detecting the tumors in various body parts. In this paper survey of various data mining methods are used for classification of MRI images. A new hybrid technique based on the support vector machine (SVM) and fuzzy c-means for brain tumor classification is studied in this paper. The algorithm is a combination of support vector machine (SVM) and fuzzy c-means, a hybrid technique for prediction of brain tumor. In this algorithm, the image is enhanced using enhancement techniques such as contrast improvement, and mid-range stretch. Fuzzy c-means (FCM) clustering is used for the segmentation of the image to detect the suspicious region in brain MRI image.

This paper presents an efficient architecture for various image filtering algorithms and tumor characterization using Xilinx System Generator (XSG). This architecture offers an alternative through a graphical user interface that combines MATLAB, Simulink and XSG and explores important aspects concerned to hardware implementation. Performance of this architecture implemented in SPARTAN-3E Starter kit (XC3S500E-FG320) exceeds those of similar or greater resources architectures. The proposed architecture reduces the resources available on target device by 50%.

We conducted a systematic review to assess the evidence for specific effects of cannabis on impulsivity, disinhibition and motor control. The review had a specific focus on neuroimaging findings associated with acute and chronic use of the drug and covers literature published up until May 2012. Seventeen studies were identified, of which 13 met the inclusion criteria; three studies investigated acute effects of cannabis (1 fMRI, 2 PET), while six studies investigated non-acute functional effects (4 fMRI, 2 PET), and four studies investigated structural alterations. Functional imaging studies of impulsivity studies suggest that prefrontal blood flow is lower in chronic cannabis users than in controls. Studies of acute administration of THC or marijuana report increased brain metabolism in several brain regions during impulsivity tasks. Structural imaging studies of cannabis users found differences in reduced prefrontal volumes and white matter integrity that might mediate the abnormal impulsivity and mood observed in marijuana users. To address the question whether impulsivity as a trait precedes cannabis consumption or whether cannabis aggravates impulsivity and discontinuation of usage more longitudinal study designs are warranted.

- by Current Pharmaceutical Design
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- F Mri, Neuroimaging, Magnetic Resonance Imaging, MRI

Attention Deficit Hyperactivity Disorder (ADHD) is a common psychiatric disorder characterised by persistent patterns of inattention, hyperactivity, and impulsivity in children. The risk factor is that these children are frequently entangled in learning difficulties, which lead to frustration when they reach adulthood. This study uses functional Magnetic Resonance Imaging data for the resting-state brain to present an effective approach for ADHD identification at an early stage. The proposed method is based on seed correlation, which calculates the functional connectivity between seeds and all other voxels in the brain.This paper gives a walk through of the steps for using machine learning to analyse rs-fMRI data and, more specifically, to distinguish Attention Deficit Hyperactivity Disorder (ADHD) from healthy controls. I discuss (1) feature extraction with masks, (2) the advantages and disadvantages of long short term memory networks (LSTM) for classifying fMRI data, and (3) hypothesis testing and its application in model evaluation.

- by IJRASET Publication
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- Neuroscience, F Mri, ADHD, Anxiety

- by Vignesh Mohan
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- F Mri, FPGA, Functional MRI, Digital FPGA implementation
- by Sophia Frangou
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- Cognitive Science, Neuropsychology, F Mri, Magnetic Resonance Imaging

Multiband echo planar imaging (EPI) offers increased temporospatial resolution and statistical power for functional magnetic resonance imaging (fMRI) but the higher spatial resolution comes at the cost of higher susceptibility-related spatial distortions. In diffusion MRI (dMRI), studies have shown that distortion correction is better when using blip-reversed EPI data (known under the term blip-up/down images) as compared to the standard fieldmap approach. This has motivated fMRI studies to acquire their data with blip-up/down directions and to use these to reduce susceptibility distortion. Here, we qualitatively illustrate why this can lead to erroneous results and quantify this error across 10 subjects.

- by Gina Joue
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- F Mri, Functional MRI, Image Preprocessing
- by Sophia Frangou
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- Cognitive Science, Neuropsychology, F Mri, Sex

The functional catechol-O-methyltransferase (COMT Val108/158Met) polymorphism has been shown to have an impact on tasks of executive function, memory and attention and recently, tasks with an affective component. As oestrogen reduces COMT activity, we focused on the interaction between gender and COMT genotype on brain activations during an affective processing task. We used functional MRI (fMRI) to record brain activations from 74 healthy subjects who engaged in a facial affect recognition task; subjects viewed and identified fearful compared to neutral faces. There was no main effect of the COMT polymorphism, gender or genotypexgender interaction on task performance. We found a significant effect of gender on brain activations in the left amygdala and right temporal pole, where females demonstrated increased activations over males. Within these regions, Val/Val carriers showed greater signal magnitude compared to Met/Met carriers, particularly in females. The COMT Val108/158Met polymorphism impacts on gender-related patterns of activation in limbic and paralimbic regions but the functional significance of any oestrogen-related COMT inhibition appears modest.

- by Sophia Frangou
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- F Mri, Fear, Gender, Neuropsychopharmacology

Strong bidentate ligation between a fluorinated isophthalate and binuclear lanthanide-DO3A species yields a new class of 19F NMR agent with very high nuclear relaxation rates at physiologically-relevant pH.

- by Gemma-Louise Davies
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- F Mri, MRI

- by Dr Tessa Christodoulou
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- F Mri, Fear, Gender, Facial Affect