AIMC Topic: Magnetic Resonance Imaging

Clear Filters Showing 5291 to 5300 of 6780 articles

Technical Note: A deep learning-based autosegmentation of rectal tumors in MR images.

Medical physics
PURPOSE: Manual contouring of gross tumor volumes (GTV) is a crucial and time-consuming process in rectum cancer radiotherapy. This study aims to develop a simple deep learning-based autosegmentation algorithm to segment rectal tumors on T2-weighted ...

Localized instance fusion of MRI data of Alzheimer's disease for classification based on instance transfer ensemble learning.

Biomedical engineering online
BACKGROUND: Diagnosis of Alzheimer's disease (AD) is very important, and MRI is an effective imaging mode of Alzheimer's disease. There are many existing studies on the diagnosis of Alzheimer's disease based on MRI data. However, there are no studies...

Reducing Navigators in Free-Breathing Abdominal MRI via Temporal Interpolation Using Convolutional Neural Networks.

IEEE transactions on medical imaging
Navigated 2-D multi-slice dynamic magnetic resonance imaging (MRI) acquisitions are essential for MR guided therapies. This technique yields time-resolved volumetric images during free-breathing, which are ideal for visualizing and quantifying breath...

Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data.

NeuroImage
Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brai...

Transferring and generalizing deep-learning-based neural encoding models across subjects.

NeuroImage
Recent studies have shown the value of using deep learning models for mapping and characterizing how the brain represents and organizes information for natural vision. However, modeling the relationship between deep learning models and the brain (or ...

3D Randomized Connection Network with Graph-based Label Inference.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, a novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity. The convolutional LSTM and 3D convolution are ...

Computational mechanisms underlying cortical responses to the affordance properties of visual scenes.

PLoS computational biology
Biologically inspired deep convolutional neural networks (CNNs), trained for computer vision tasks, have been found to predict cortical responses with remarkable accuracy. However, the internal operations of these models remain poorly understood, and...

A morphometric signature of depressive symptoms in unmedicated patients with mood disorders.

Acta psychiatrica Scandinavica
OBJECTIVE: A growing literature indicates that unipolar depression and bipolar depression are associated with alterations in grey matter volume. However, it is unclear to what degree these patterns of morphometric change reflect symptom dimensions. H...

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as...

Super-resolution reconstruction of MR image with a novel residual learning network algorithm.

Physics in medicine and biology
Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural network...