Research Ben Jeurissen

Abstract

The ability to study tissue microstructure in vivo and completely noninvasively using magnetic resonance imaging (MRI) has the potential to radically change how we detect, monitor, and treat diseases, in particular the many neurodegenerative diseases that affect our world's aging population. Unfortunately, the MRI signal is a very indirect measure of microstructure, and the variety of contributing factors complicates a one-to-one association between the MRI measurements and the biological substrate. As a result, microstructural mapping is still a poorly understood and challenging inverse problem that often yields inconsistent and contradictory outcomes. In ADAMI, I will take the next leap in microstructure imaging by approaching the problem in a completely data-driven fashion as opposed to the state of-the-art that is model-driven. This paradigm shift will enable me to turn the MRI scanner into a powerful in vivo microscope that can provide reliable information about tissue microstructure that closely matches the underlying cellular composition. Through these innovations, ADAMI will advance the field of medical imaging by introducing a groundbreaking data-driven approach to microstructure imaging which will significantly impact the understanding, diagnosis, and monitoring of brain diseases and beyond.

Researcher(s)

• Promoter: Jeurissen Ben

Research team(s)

• Vision lab

Project website

Project type(s)

• Research Project

Abstract

Surgical anterior cruciate ligament (ACL) reconstruction using tendon graft is the standard to treat ACL injuries. However, little is known about the maturation process of human ACL graft and the role of adjacent structural abnormalities herein. There currently exists a high clinical need for improved noninvasive objective measures of ACL graft properties to help inform return to high-demand activities. Next to anatomical magnetic resonance imaging (MRI), quantitative MRI (qMRI) techniques, such as T2* relaxometry and diffusion tensor imaging (DTI), have gained interest for musculoskeletal imaging. qMRI provides objective measures of biophysical tissue properties that allow for monitoring of tissue microstructure. Despite its demonstrated potential to provide biomarkers of ACL graft maturation, standard qMRI suffers from low resolution and long scan times, impeding clinical validation. To improve the trade-off between signal-to-noise ratio, resolution and scan time, we propose a super-resolution reconstruction (SRR) framework for anatomical MRI and qMRI of the knee that will overcome the current limitations for biomarker identification. In this project, we will develop SRR qMRI for T2* relaxometry and DTI of the knee and provide further insight into the condition of maturing ACL graft in patients before return to play. SRR qMRI may also improve our ability to evaluate the effectiveness of additional treatments to accelerate ACL graft maturation.

Researcher(s)

• Promoter: Van Dyck Pieter
• Co-promoter: Jeurissen Ben

Research team(s)

• Molecular Imaging and Radiology (MIRA)

Project type(s)

• Research Project

Abstract

Multi-tissue spherical deconvolution of diffusion MRI (dMRI) is a popular analysis method that provides the full white matter fiber orientation density function as well as the densities of cerebrospinal fluid and grey matter tissue in the living human brain, completely noninvasively. It can be used to track the long-range connections of the brain and provides a tract-specific biomarker for neuronal loss in the study of neurodegenerative diseases. Currently, the technique can be regarded as a macroscopic approach: it breaks up the dMRI voxels in terms of tissues rather than cellular components, the latter being potentially more relevant biomarkers. Unfortunately, recent studies have demonstrated that conventional low-dimensional dMRI scans lack the information to resolve these microstructural features. In this proposal, I will take multi-tissue spherical deconvolution to the next (microscopic) level by leveraging high-dimensional dMRI scans. These next-generation scans have shown great promise to disentangle different microstructural compartments. The new multi-compartment spherical deconvolution approach will allow simultaneous estimation of a high quality axonal orientation density function as well as the densities of cell bodies and extracellular space. This will enable high-quality fiber tracking and at the same time provide more relevant biomarkers, and will help spherical deconvolution to maintain its position as one of the go-to tools for dMRI analysis.

Researcher(s)

• Promoter: Sijbers Jan
• Fellow: Jeurissen Ben

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Diffusion Weighted (DW) MRI is a unique and noninvasive method to characterise tissue microstructure, based on the random thermal motion of water molecules. Of particular interest is its potential for inferring the orientation of the coherently oriented fiber bundles within brain white matter tissue, as this opens up the possibility of investigating brain connectivity in vivo using so-called fiber-tracking algorithms. This relatively new technique is becoming a valuable diagnostic tool for a large number of neuropathological diseases. The main goals of this research topic are: - to use clinically relevant acquisitions (< 15 minutes) - to estimate fiber orientations in each voxel as accurate as possible - to assess global brain connectivity by means of tractography

Researcher(s)

• Promoter: Jeurissen Ben

Research team(s)

• Vision lab

Project website

Project type(s)

• Research Project

Abstract

Spherical deconvolution (SD) of diffusion-weighted MRI (DW-MRI) is a popular analysis method that allows extraction of white matter (WM) fibre orientation information in the living human brain, completely noninvasively. It can be used to track the long-range connections of the brain or serve as a tract-specific biomarker for neuronal loss in the study of neurodegenerative diseases. Recently, I proposed a new analysis method based on SD that models the presence of non-WM tissue in voxels, which was previously unaccounted for, enabling unprecedented tractography and quantification of WM. However, significant challenges remain, preventing SD from realizing its full potential: * The current approach models the signal arising from the three macroscopic tissue types. With a new approach, I want to take this to the microscopic level, taking into account the presence of axons, cell bodies and extracellular water. This will improve current neuronal fibre estimates and will introduce new quantitative measures that can be used as biomarkers in the study of neurodegenerative diseases. * Clinical scans are limited in spatial resolution due to constraints on scan time and signal-to-noise ratio (SNR). However, the very fine structures of the WM, and particularly the intricate folding patterns of the cortical surface, require high spatial resolution. I propose a new SD algorithm that can obtain high-resolution fibre information, with adequate SNR and within a practical acquisition time.

Researcher(s)

• Promoter: Sijbers Jan
• Fellow: Jeurissen Ben

Research team(s)

• Vision lab

Project type(s)

• Research Project