PURPOSE: We aim to leverage the power of deep-learning with high-fidelity training data to improve the reliability and processing speed of hemodynamic mapping with MR fingerprinting (MRF) arterial spin labeling (ASL).
Based on whole-brain gray matter volume (GMV), we used relevance vector regression to predict the Rey's Auditory Verbal Learning Test Delayed Recall (AVLT-DR) scores of individual amnestic mild cognitive impairment (aMCI) patient. The whole-brain GMV...
Deep learning methods hold strong promise for identifying biomarkers for clinical application. However, current approaches for psychiatric classification or prediction do not allow direct interpretation of original features. In the present study, we ...
PURPOSE: Traditional registration of functional magnetic resonance images (fMRI) is typically achieved through registering their coregistered structural MRI. However, it cannot achieve accurate performance in that functional units which are not neces...
INTRODUCTION: Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM) region only. Reducing the spatial coverage to the DGM vicinity can sub...
Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or ...
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...
Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more wide...
PURPOSE: To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET).