AIMC Topic: Diffusion Tensor Imaging

Clear Filters Showing 101 to 110 of 186 articles

Machine learning-based segmentation of ischemic penumbra by using diffusion tensor metrics in a rat model.

Journal of biomedical science
BACKGROUND: Recent trials have shown promise in intra-arterial thrombectomy after the first 6-24 h of stroke onset. Quick and precise identification of the salvageable tissue is essential for successful stroke management. In this study, we examined t...

Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes.

Scientific reports
Fronto-temporal dementia (FTD) is a common type of presenile dementia, characterized by a heterogeneous clinical presentation that includes three main subtypes: behavioural-variant FTD, non-fluent/agrammatic variant primary progressive aphasia and se...

DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning.

NeuroImage
Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and pos...

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging.

NeuroImage
Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized fr...

Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions.

Annals of clinical and translational neurology
OBJECTIVE: Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterog...

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets.

Communications biology
Understanding how cognitive functions emerge from brain structure depends on quantifying how discrete regions are integrated within the broader cortical landscape. Recent work established that macroscale brain organization and function can be describ...

Novel Deep Learning Network Analysis of Electrical Stimulation Mapping-Driven Diffusion MRI Tractography to Improve Preoperative Evaluation of Pediatric Epilepsy.

IEEE transactions on bio-medical engineering
OBJECTIVE: To investigate the clinical utility of deep convolutional neural network (DCNN) tract classification as a new imaging tool in the preoperative evaluation of children with focal epilepsy (FE).

Diagnosing schizophrenia with network analysis and a machine learning method.

International journal of methods in psychiatric research
OBJECTIVE: Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizo...

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Human brain mapping
Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the d...