AIMC Topic: Magnetic Resonance Imaging

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Network abnormalities among non-manifesting Parkinson disease related LRRK2 mutation carriers.

Human brain mapping
Non-manifesting carriers (NMC) of the G2019S mutation in the LRRK2 gene represent an "at risk" group for future development of Parkinson's disease (PD) and have demonstrated task related fMRI changes. However, resting-state networks have received les...

3D convolutional neural networks for tumor segmentation using long-range 2D context.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have achieved state-of-the-art performances in a large variety of recogniti...

Robust water-fat separation for multi-echo gradient-recalled echo sequence using convolutional neural network.

Magnetic resonance in medicine
PURPOSE: To accurately separate water and fat signals for bipolar multi-echo gradient-recalled echo sequence using a convolutional neural network (CNN).

Qualitative versus quantitative lumbar spinal stenosis grading by machine learning supported texture analysis-Experience from the LSOS study cohort.

European journal of radiology
PURPOSE: To investigate and compare the reproducibility and accuracy of qualitative ratings and quantitative texture analysis (TA) in detection and grading of lumbar spinal stenosis (LSS) in magnetic resonance imaging (MR) scans of the lumbar spine.

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation.

Medical physics
PURPOSE: Reliable automated segmentation of the prostate is indispensable for image-guided prostate interventions. However, the segmentation task is challenging due to inhomogeneous intensity distributions, variation in prostate anatomy, among other ...

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder.

NeuroImage. Clinical
Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophre...

SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network.

Zeitschrift fur medizinische Physik
Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson's disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonanc...

Evaluating reinforcement learning agents for anatomical landmark detection.

Medical image analysis
Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforc...

Random forest prediction of Alzheimer's disease using pairwise selection from time series data.

PloS one
Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing...

Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting.

IEEE transactions on medical imaging
Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of human body. Although MRF has demonstrated improved scan efficiency as compared to conventional techniq...