A5098957912- Advanced Neuroimaging Techniques and Applications
- Advanced MRI Techniques and Applications
- Advanced Memory and Neural Computing
- Functional Brain Connectivity Studies
- Neonatal and fetal brain pathology
- Advancements in Semiconductor Devices and Circuit Design
- Tensor decomposition and applications
- Quantum and electron transport phenomena
- Child Nutrition and Water Access
- IoT and Edge/Fog Computing
- Magnetic properties of thin films
- Infant Development and Preterm Care
- Interconnection Networks and Systems
Cardiff University
2024
Measures of physical growth, such as weight and height have long been the predominant outcomes for monitoring child health evaluating interventional in public studies, including those that may impact neurodevelopment. While growth generally reflects overall nutritional status, it lacks sensitivity specificity to brain developing cognitive skills abilities. Psychometric tools, e.g., Bayley Scales Infant Toddler Development, afford more direct assessment development but they require language...
Abstract Purpose This study aims to reduce Diffusion Tensor MRI (DT-MRI) scan time by minimizing diffusion-weighted measurements. Using machine learning, DT-MRI parameters are accurately estimated with just four tetrahedrally-arranged diffusion-encoded measurements, instead of the usual six or more. significantly shortens duration and is particularly useful in ultra-low field (ULF) studies for non-compliant populations (e.g., children, elderly, those movement disorders) where long times...
Motivation: Facilitating white matter mapping in traditionally inaccessible low income settings. Goal(s): Demonstration of a viable protocol to resolve complex architecture at field. Approach: A multi-modality was devised allow feasible acquisition multi-direction diffusion weighted imaging (DWI) datasets 64mT. modified DWI is shown, employing voxel-wise encoding non-uniformity calibration, extended readout, and optimal sampling density. Machine learning based denoising employed...
Motivation: The project aimed to tackle extended DTI scan durations, worsened by low SNR at fields, striving boost efficiency while preserving results' accuracy lower levels. Goal(s): study sought create an ML-based approach shorten DT-MRI scans ensuring reliable tensor estimation despite challenges ULF. Approach: ML models, trained on synthetic data, predicted diffusivities and principal eigenvectors from four diffusion-weighted images, factoring in simulated noise gradient rotations for...
Motivation: Neuroscience MRI research, including assessment of structural connectomics, has been largely limited to high-resource settings. Goal(s): To democratise brain connectivity by demonstrating the first ever diffusion-weighted imaging (DWI)-based connectomics at 64 mT. Approach: 15-direction DWI data were acquired Whole-brain tractograms recovered after deep learning based denoising and constrained spherical deconvolution. adjacency matrices graph-theory parameters extracted, their...