Research Arjan den Dekker

Abstract

MRI is a key methodology in modern neuroimaging, but conventional MRI relies on visual interpretation of intensity differences in the images, which is heavily dependent on scanner settings. Quantitative MRI (qMRI) is an attractive alternative MRI method that allows quantitative measurement of physical tissue parameters, enabling objective comparison between patients and across time. Moreover, qMRI facilitates early detection of pathological changes in the brain resulting from neurological disorders such as multiple sclerosis. Unfortunately, and despite the demonstrated potential in research settings, the implementation of qMRI in routine clinical practice remains limited due to long scan and post-processing times. While recent developments in artificial intelligence have the potential to accelerate and improve medical imaging pipelines, reduced transparency about the underlying processes, the lack of training data sets and limited information about the accuracy of the results has limited its use for clinical qMRI applications so far. In IQ-BRAIN, we propose a unique research and training programme that tackles this urgent need for improved and accelerated qMRI methodology for neuroimaging applications. By integrating both physics-based models and trustworthy artificial intelligence methods along the qMRI pipeline, our innovative approach combines the best of both worlds. IQ-BRAIN will prepare the next generation of qMRI specialists trained in the different aspects of the qMRI-neuroimaging pipeline, that can bridge the gap between qMRI method development and clinical need. Through a training programme of network-wide events, international secondments, and strong interaction between partners from academia, industry and hospitals, IQ-BRAIN offers early-stage researchers a rich combination of knowledge, expertise and essential transferable skills that prepares them for a thriving career as R&D professionals in the qMRI field.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: Bertoglio Daniele
• Co-promoter: den Dekker Arjan
• Co-promoter: Jeurissen Ben
• Co-promoter: Verhoye Marleen

Research team(s)

• Vision lab

Project website

Project type(s)

• Research Project

Abstract

Magnetic resonance imaging (MRI) is one of the most useful and rapidly growing neuroimaging tools. Unfortunately, signal intensities in conventional MRI images are expressed in relative units that depend on scanner hardware and acquisition protocols. While this does not hinder visual inspection of anatomy, it hampers quantitative comparison of tissue properties within a scan, between successive scans, and between subjects. In contrast, advanced quantitative MRI (Q-MRI) methods like MR relaxometry or diffusion MRI do enable absolute quantification of biophysical tissue characteristics. Evidence is growing that Q-MRI techniques detect subtle microscopic damage, enabling more accurate and early diagnosis of neurodegenerative diseases. However, due to the long scan time required for Q-MRI, causing discomfort for patients and limiting the throughput, Q-MRI methods have not entered clinical practice yet. B-Q MINDED aims to overcome the current barriers by developing widely-applicable post-processing breakthroughs for accelerating Q-MRI. The originality of B-Q MINDED lies in its ambition to replace the conventional rigid multi-step processing pipeline with an integrated single-step parameter estimation framework. This approach will unlock a wealth of options for optimization of Q-MRI. To accomplish this goal, B-Q MINDED proposes a collaborative cross-disciplinary approach (from basic MR physics to clinical applications) with strong involvement of industry.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: den Dekker Arjan
• Co-promoter: Guns Pieter-Jan
• Co-promoter: Verhoye Marleen

Research team(s)

• Vision lab

Project website

Project type(s)

• Research Project

Abstract

Perfusion magnetic resonance imaging (MRI) is an imaging tool to assess the spatial distribution of microvascular blood flow. Many neurological disorders are accompanied by cerebral blood flow (CBF) alterations, which makes perfusion MRI indispensable in routine clinical practice. Arterial spin labeling (ASL) perfusion MRI uses magnetically labeled arterial blood water as an endogenous diffusible tracer. Tissue perfusion is measured from the signal difference between images with labeled blood and control images. Lack of ionizing radiation, complete non-invasiveness, and absolute quantification of perfusion parameters make ASL a unique perfusion imaging modality. Current ASL methods, however, suffer from problems such as noisy images and patient movement, which are inherent to the acquisition process. My project aims to develop a framework that incorporates new ASL acquisition and reconstruction methods targeting these problems simultaneously. The core of this framework revolves around super resolution reconstruction (SRR) ASL imaging which allows direct estimation of high-resolution perfusion parameters from a set of differently sampled low-resolution images. Results will yield a patient-friendly, cost-efficient and quantitative protocol that allows accurate and precise perfusion measurement at increased resolution in a clinically acceptable acquisition time, by that removing the main obstacles for ASL to become the golden standard for perfusion measurements.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: den Dekker Arjan
• Fellow: Bladt Piet

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Perfusion magnetic resonance imaging (MRI) is an imaging tool to assess the spatial distribution of microvascular blood flow. Many neurological disorders are accompanied by cerebral blood flow (CBF) alterations, which makes perfusion MRI indispensable in routine clinical practice. Arterial spin labeling (ASL) perfusion MRI uses magnetically labeled arterial blood water as an endogenous diffusible tracer. Tissue perfusion is measured from the signal difference between images with labeled blood and control images. Lack of ionizing radiation, complete non-invasiveness, and absolute quantification of perfusion parameters make ASL a unique perfusion imaging modality. Current ASL methods, however, suffer from problems such as noisy images and patient movement, which are inherent to the acquisition process. My project aims to develop a framework that incorporates new ASL acquisition and reconstruction methods targeting these problems simultaneously. The core of this framework revolves around super-resolution reconstruction (SRR) ASL imaging which allows direct estimation of high-resolution perfusion parameters from a set of differently sampled low-resolution images. Results will yield a patient-friendly, cost-efficient and quantitative protocol that allows accurate and precise perfusion measurement at increased resolution in a clinically acceptable acquisition time, by that removing the main obstacles for ASL to become the golden standard for perfusion measurements.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: den Dekker Arjan
• Fellow: Bladt Piet

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The aim of this project is to detect extremely light atoms, to determine their atom types and to measure their positions down to picometer precision. Therefore, aberration corrected scanning transmission electron microscopy will be combined with innovative quantitative measuring.

Researcher(s)

• Promoter: Van Aert Sandra
• Co-promoter: den Dekker Arjan

Research team(s)

• Electron microscopy for materials research (EMAT)

Project type(s)

• Research Project