AIMC Topic: Magnetic Resonance Imaging

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Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease.

PloS one
Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing literature, support vector machine (SVM) has always been applied to distinguish between AD and health...

Model-Based Feature Augmentation for Cardiac Ablation Target Learning From Images.

IEEE transactions on bio-medical engineering
GOAL: We present a model-based feature augmentation scheme to improve the performance of a learning algorithm for the detection of cardiac radio-frequency ablation (RFA) targets with respect to learning from images alone.

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

Journal of neuroscience methods
Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i...

Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels.

IEEE transactions on medical imaging
This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) parameter estimation via regression with kernels (PERK). PERK first uses prior distributions and the nonlinear MR ...

Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network.

Journal of healthcare engineering
Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic ...

Dynamic functional network connectivity discriminates mild traumatic brain injury through machine learning.

NeuroImage. Clinical
Mild traumatic brain injury (mTBI) can result in symptoms that affect a person's cognitive and social abilities. Improvements in diagnostic methodologies are necessary given that current clinical techniques have limited accuracy and are solely based ...

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

Journal of vascular and interventional radiology : JVIR
PURPOSE: To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques.

Medical Image Synthesis with Deep Convolutional Adversarial Networks.

IEEE transactions on bio-medical engineering
Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great...

A convolutional neural network to filter artifacts in spectroscopic MRI.

Magnetic resonance in medicine
PURPOSE: Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viab...

Age-related changes in the ease of dynamical transitions in human brain activity.

Human brain mapping
Executive functions, a set of cognitive processes that enable flexible behavioral control, are known to decay with aging. Because such complex mental functions are considered to rely on the dynamic coordination of functionally different neural system...