AIMC Topic: Diffusion Tensor Imaging

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Mitigating transmit-B artifacts by predicting parallel transmission images with deep learning: A feasibility study using high-resolution whole-brain diffusion at 7 Tesla.

Magnetic resonance in medicine
PURPOSE: To propose a novel deep learning (DL) approach to transmit-B (B )-artifact mitigation without direct use of parallel transmission (pTx), by predicting pTx images from single-channel transmission (sTx) images.

Deep learning based diagnosis of Parkinson's Disease using diffusion magnetic resonance imaging.

Brain imaging and behavior
The diagnostic performance of a combined architecture on Parkinson's disease using diffusion tensor imaging was evaluated. A convolutional neural network was trained from multiple parcellated brain regions. A greedy algorithm was proposed to combine ...

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI.

NeuroImage
Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the acc...

DIFFnet: Diffusion Parameter Mapping Network Generalized for Input Diffusion Gradient Schemes and b-Value.

IEEE transactions on medical imaging
In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-v...

Jointly estimating parametric maps of multiple diffusion models from undersampled q-space data: A comparison of three deep learning approaches.

Magnetic resonance in medicine
PURPOSE: While advanced diffusion techniques have been found valuable in many studies, their clinical availability has been hampered partly due to their long scan times. Moreover, each diffusion technique can only extract a few relevant microstructur...

Automated grading of enlarged perivascular spaces in clinical imaging data of an acute stroke cohort using an interpretable, 3D deep learning framework.

Scientific reports
Enlarged perivascular spaces (EPVS), specifically in stroke patients, has been shown to strongly correlate with other measures of small vessel disease and cognitive impairment at 1 year follow-up. Typical grading of EPVS is often challenging and time...

Effects of a Brain-Computer Interface-Operated Lower Limb Rehabilitation Robot on Motor Function Recovery in Patients with Stroke.

Journal of healthcare engineering
PURPOSE: To observe the effect of a brain-computer interface-operated lower limb rehabilitation robot (BCI-LLRR) on functional recovery from stroke and to explore mechanisms.

Can we predict anti-seizure medication response in focal epilepsy using machine learning?

Clinical neurology and neurosurgery
OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning approach based on clinical factors and diffusion tensor imaging (DTI) to predict anti-seizure medication (ASM) response in focal epilepsy. We hypothesized that ASM r...

Skilled reach training enhances robotic gait training to restore overground locomotion following spinal cord injury in rats.

Behavioural brain research
Rehabilitative training has been shown to improve motor function following spinal cord injury (SCI). Unfortunately, these gains are primarily task specific; where reach training only improves reaching, step training only improves stepping and stand t...

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification.

Human brain mapping
Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level...