AIMC Topic: Magnetic Resonance Imaging
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DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra.
Nature communications
Sep 1, 2021
The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Her...
Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.
PloS one
Sep 1, 2021
Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in multiple sclerosis (MS). In this work, we have developed the first fully automated method to segment and count the gadolinium-enhancing lesions from rou...
A combination of support vector machine and voxel-based morphometry in adult male alcohol use disorder patients with cognitive deficits.
Brain research
Aug 31, 2021
Cognitive performance deteriorates with drinking. However, the neural basis of cognitive deficits in alcohol use disorder (AUD) is still incompletely understood. Here we examined the relationship between overall drinking, brain structural alterations...
A multiphase texture-based model of active contours assisted by a convolutional neural network for automatic CT and MRI heart ventricle segmentation.
Computer methods and programs in biomedicine
Aug 31, 2021
BACKGROUND: Left and right ventricle automatic segmentation remains one of the more important tasks in computed aided diagnosis. Active contours have shown to be efficient for this task, however they often require user interaction to provide the init...
A machine-learning-based method to predict adverse events in patients with dilated cardiomyopathy and severely reduced ejection fractions.
The British journal of radiology
Aug 31, 2021
OBJECTIVE: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of experiencing adverse cardiac events. A machine learning (ML) method could enable more effective risk stra...
A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data.
Computer methods and programs in biomedicine
Aug 31, 2021
BACKGROUND AND OBJECTIVE: Synthetic magnetic resonance imaging (MRI) is a low cost procedure that serves as a bridge between qualitative and quantitative MRI. However, the proposed methods require very specific sequences or private protocols which ha...
Dual Attention Multi-Instance Deep Learning for Alzheimer's Disease Diagnosis With Structural MRI.
IEEE transactions on medical imaging
Aug 31, 2021
Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological disease diagnosis, which could reflect the variations of brain. However, due to the local brain atrophy, only a few regions in sMRI scans have obvious structural c...
GraphRegNet: Deep Graph Regularisation Networks on Sparse Keypoints for Dense Registration of 3D Lung CTs.
IEEE transactions on medical imaging
Aug 31, 2021
In the last two years learning-based methods have started to show encouraging results in different supervised and unsupervised medical image registration tasks. Deep neural networks enable (near) real time applications through fast inference times an...
Differentiating Benign from Malignant Renal Tumors Using T2- and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists.
Journal of magnetic resonance imaging : JMRI
Aug 30, 2021
BACKGROUND: Differentiating benign from malignant renal tumors is important for selection of the most effective treatment.
Automatic breast lesion detection in ultrafast DCE-MRI using deep learning.
Medical physics
Aug 30, 2021
PURPOSE: We propose a deep learning-based computer-aided detection (CADe) method to detect breast lesions in ultrafast DCE-MRI sequences. This method uses both the 3D spatial information and temporal information obtained from the early-phase of the d...
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