A5066945263- Advanced Neuroimaging Techniques and Applications
- MRI in cancer diagnosis
- Artificial Intelligence in Healthcare
- Acute Ischemic Stroke Management
- Model Reduction and Neural Networks
- Brain Tumor Detection and Classification
- Dementia and Cognitive Impairment Research
- NMR spectroscopy and applications
- Stroke Rehabilitation and Recovery
- Tensor decomposition and applications
- Fetal and Pediatric Neurological Disorders
- Cerebrovascular and Carotid Artery Diseases
University of Antwerp
2021-2022
Icometrix (Belgium)
2019-2021
ZeptoMetrix (United States)
2020
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES'22 provided 400 patient scans ischemic various medical centers, facilitating wide range cutting-edge segmentation research community. Through...
Background Diffusion tensor imaging (DTI) parameters, such as fractional anisotropy (FA), allow examining the structural integrity of brain. However, true value these parameters may be confounded by variability in MR hardware, acquisition and image quality. Purpose To examine effects confounding factors on FA to evaluate feasibility statistical methods model reduce multicenter variability. Study Type Longitudinal study. Phantom DTI single strand phantom (HQ imaging). Field Strength/Sequence...
The free water elimination (FWE) model and its kurtosis variant (DKI-FWE) can separate tissue signal contributions, thus providing tissue-specific diffusional information. However, a downside of these models is that the associated parameter estimation problem ill-conditioned, necessitating use advanced techniques potentially bias estimates. In this work, we propose T2-DKI-FWE exploits T2 relaxation properties both compartments, thereby better conditioning providing, at same time, an...
Fitting the diffusion kurtosis imaging free water elimination model (DKI-FWE) to MRI data represents an ill-conditioned problem. Fortunately, conditioning of fitting can be improved by explicitly modeling T2 relaxation dependency signal. As a benefit, and metrics robust partial volume effects estimated with conventional techniques. In this work, we use Cramér-Rao lower bound (CRLB) theory identify optimal acquisition settings that maximize precision parameter estimates.
Abstract Background Macro‐ and micro‐structural degeneration in cortical grey matter (CGM) accompany the progression of frontotemporal lobar (FTLD). The former can be measured by brain volumetry from magnetic resonance imaging (MRI) latter mean diffusivity (MD) diffusion tensor (DTI). While disease‐modifying treatment is expected to slow both metrics, unexpected effects such as inflammation may alter relationship between them. Here, we evaluate cross‐sectional longitudinal relationships...