Research Ben Jeurissen

Abstract

Neurodegenerative diseases are rapidly emerging as an insidious epidemic, presenting a significant challenge due to their limited therapeutic options. While Magnetic Resonance Imaging (MRI) has become indispensable for monitoring disease progression in both clinical and preclinical settings, its capacity to capture the underlying pathophysiological mechanisms remains constrained. Our preliminary work has demonstrated that sophisticated contrasts obtained from diffusion weighted MRI (DWI) or arterial spin labeling (ASL) hold promise in detecting subtle microstructural and perfusion alterations, respectively. However, their sensitivity and resolution are hindered by imaging time limitations. Light sheet microscopy (LSM) can complement these in vivo imaging modalities with molecular information, but equally suffers from suboptimal image quality. Recognizing the complementary potential of these modalities and acknowledging their existing limitations, our intent is to propel multimodal brain imaging forward by enhancing MRI and LSM images through model-based superresolution reconstruction. Our proposed framework is built on the premise that isotropic high-resolution images can be estimated from a collection of oblique lower resolution images. We plan to accomplish this by employing iterative algorithms and leveraging deep learning techniques, rendering the calculations more efficient. Specifically, we seek to develop superresolution reconstruction frameworks that will enable precise estimation of neuronal density from DWI, reproducible estimation of cerebral blood flow from ASL, and comprehensive quantification of sub-cellular structures from LSM. Upon successful development, we will validate these enhanced imaging techniques using a well-characterized mouse model for Huntington's Disease, a condition that necessitates a comprehensive high-resolution approach. By correlating the different imaging modalities at high resolution, we intend to enable ultra-high-content imaging of the brain, ultimately revealing intricate relationships between measured parameters and pathological defects at an individual level. Our team comprises experts from diverse disciplines, including image processing and modeling (VLAB), neuro-oriented MRI (BIL), and advanced cell biology coupled with microscopy (CBH). This multidisciplinary collaboration positions us ideally to accomplish our ambitious objectives. Moreover, as members of the µNEURO research excellence consortium, along with our roles as representatives of core facilities and coordinators of two valorization platforms, we have established a robust platform for amplifying the impact of our project. This strategic positioning ensures that the outcomes of our research will have a far-reaching effect in advancing our understanding of neurodegenerative diseases through cutting-edge imaging technologies.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: Bertoglio Daniele
• Co-promoter: De Vos Winnok
• Co-promoter: Jeurissen Ben
• Co-promoter: Verhoye Marleen

Research team(s)

• Vision lab

Project type(s)

• Research Project

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

Quantitative Magnetic Resonance Imaging (qMRI) is a subset of MRI techniques that aims to measure physical, chemical, or biological tissue properties in a quantitative manner, rather than just providing qualitative or anatomical images. Unlike conventional MRI, which produces images primarily for visual interpretation, qMRI provides numeric values that describe specific tissue characteristics, enabling more objective and reproducible assessments.

Researcher(s)

• Promoter: Jeurissen Ben
• Fellow: Jeurissen Ben

Research team(s)

• Vision lab

Project type(s)

• Research Project

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

Tinnitus is a highly prevalent disorder affecting 10 to 15% of adults. It has a high socioeconomic burden, because it affects patients' quality of life, is associated with depression, reduced productivity at work and sleeping difficulties. Many different risk factors for the development of tinnitus have been described, such as hearing loss. In about 25% of patients, tinnitus is influenced by neck or jaw related muscle tension or limitations in joint movement, then called somatic tinnitus (ST). Animal research has proposed that ST originates from brainstem connections between the areas that collect information from the neck and jaw and hearing related areas. One brain imaging study has shown that these connections also exist in humans. This, however, does not explain why some patients with tinnitus experience changes in their tinnitus when they are having neck pain while others don't. This is why we aim to use a unique multimodal medical imaging approach with the ultimate goal to identify how function, structure and neurochemistry of key nodes (somatosensory and auditory) in the brain are related to ST. By comparing the results of a group of patients with ST to patients with other types of tinnitus and patients with neck pain without tinnitus, we aim to better identify the mechanisms of ST. This will give us the tools to improve future assessment and treatment strategies for patients with tinnitus.

Researcher(s)

• Promoter: Gilles Annick
• Co-promoter: Jeurissen Ben

Research team(s)

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