AIMC Topic: Magnetic Resonance Imaging

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Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease.

NeuroImage. Clinical
Parkinson's disease is the second most common neurodegenerative disease in the elderly after Alzheimer's disease. The aetiology and pathogenesis of Parkinson's disease (PD) are still unclear, but the loss of dopaminergic cells and the excessive iron ...

Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation.

NeuroImage. Clinical
PURPOSE: Accurate lesion segmentation is important for measurements of lesion load and atrophy in subjects with multiple sclerosis (MS). International MS lesion challenges show a preference of convolutional neural networks (CNN) strategies, such as n...

Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification.

Computational and mathematical methods in medicine
In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect...

High-resolution 3D MR Fingerprinting using parallel imaging and deep learning.

NeuroImage
MR Fingerprinting (MRF) is a relatively new imaging framework capable of providing accurate and simultaneous quantification of multiple tissue properties for improved tissue characterization and disease diagnosis. While 2D MRF has been widely availab...

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported.

A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.

NeuroImage
Predicting the progression of Alzheimer's Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the development of novel machine learning algorithms. This study proposes a novel machine learning algo...

Deep Learning for Quantitative Cardiac MRI.

AJR. American journal of roentgenology
The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep learning, review its current applications on quantitative cardi...

Single-slice Alzheimer's disease classification and disease regional analysis with Supervised Switching Autoencoders.

Computers in biology and medicine
BACKGROUND: Alzheimer's disease (AD) is a difficult to diagnose pathology of the brain that progressively impairs cognitive functions. Computer-assisted diagnosis of AD based on image analysis is an emerging tool to support AD diagnosis. In this arti...

Synthesizing diffusion tensor imaging from functional MRI using fully convolutional networks.

Computers in biology and medicine
PURPOSE: Medical image synthesis can simulate a target modality of interest based on existing modalities and has the potential to save scanning time while contributing to efficient data collection. This study proposed a three-dimensional (3D) deep le...