Hojjatollah Azadbakht
A5035467288- Advanced Neuroimaging Techniques and Applications
- Advanced MRI Techniques and Applications
- Fetal and Pediatric Neurological Disorders
- MRI in cancer diagnosis
- Functional Brain Connectivity Studies
- Radiomics and Machine Learning in Medical Imaging
- Botulinum Toxin and Related Neurological Disorders
- Lattice Boltzmann Simulation Studies
- Bone and Joint Diseases
- Artificial Intelligence in Healthcare and Education
- COVID-19 diagnosis using AI
- Medical Image Segmentation Techniques
- Tensor decomposition and applications
- Visual perception and processing mechanisms
- Neural dynamics and brain function
- Advanced Neural Network Applications
University of Manchester
2013-2017
Manchester Academic Health Science Centre
2015-2017
Machine learning methods offer great promise for fast and accurate detection prognostication of COVID-19 from standard-of-care chest radiographs (CXR) computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models both these tasks, but it is unclear which are potential clinical utility. In this systematic review, we search EMBASE via OVID, MEDLINE PubMed, bioRxiv, medRxiv arXiv papers preprints uploaded January 1, to October 3,...
White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white pathways in vivo human brains. However, like other analyses complex data, there is considerable variability protocols and techniques. This can result different reconstructions same intended pathways, which directly affects results, quantification, interpretation. In this study, we aim evaluate quantify that arises from for segmentation. Through an open call users...
Diffusion magnetic resonance imaging (MRI) allows for the noninvasive in vivo examination of anatomical connections human brain, which has an important role understanding brain function. Validation this technique is vital, but proved difficult due to lack adequate gold standard. In work, macaque visual system was used as a model extensive body literature and postmortem tracer studies established detailed underlying connections. We performed probabilistic tractography on high angular...
The temporal lobe has been implicated in multiple cognitive domains through lesion studies as well neuroimaging research. There a recent increased interest the structural and connective architecture that underlies these functions. However there not yet comprehensive exploration of patterns connectivity appear across lobe. This article uses data driven, spectral reordering approach order to understand general axes within Two important findings emerge from study. Firstly, lobe's overarching...
Temporal lobe networks are associated with multiple cognitive domains. Despite an upsurge of interest in connectional neuroanatomy, the terminations main fibre tracts human brain yet to be mapped. This information is essential given that neurological, neuroanatomical and computational accounts expect neural functions strongly shaped by pattern white-matter connections. paper uses a probabilistic tractography approach identify cortical areas contribute major temporal tracts. In order...
Abstract White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white pathways in vivo human brains. However, like other analyses complex data, there is considerable variability protocols and techniques. This can result different reconstructions same intended pathways, which directly affects results, quantification, interpretation. In this study, we aim evaluate quantify that arises from for segmentation. Through an open call users...
This work demonstrates the feasibility of using deep learning (DL) to accelerate revised-NODDI parameter estimation with data acquired tensor-valued diffusion encoding (TVDE). Revised-NODDI is a recently proposed version NODDI which showed improved compatibility TVDE. Thanks this model has an extra free be estimated which, conventional fitting methods, further slowdown NODDI’s time-demanding estimation. DL methods can vastly process. We show that accurate parameters obtained via...
Quantitative MRI (qMRI) aims to map tissue properties non-invasively via models that relate these unknown quantities measured signals. Estimating unknowns, which has traditionally required model fitting - an often iterative procedure, can now be done with one-shot machine learning (ML) approaches. Such parameter estimation may complicated by intrinsic qMRI signal degeneracy: different combinations of produce the same signal. Despite their many advantages, it remains unclear whether ML...