Research Pieter Van Dyck

Abstract

Owing to their high spatiotemporal resolution and non-invasive nature, (bio)medical imaging technologies have become key to understanding the complex structure and function of the nervous system in health and disease. Recognizing this unique potential, μNEURO has assembled the expertise of eight complementary research teams from three different faculties, capitalizing on advanced neuro-imaging tools across scales and model systems to accelerate high-impact fundamental and clinical neuro-research. Building on the multidisciplinary collaboration that has been successfully established since its inception (2020-2025), μNEURO (2026-2031) now intends to integrate and consolidate the synergy between its members to become an international focal point for true multidimensional neuroscience. Technologically, we envision enriching spatiotemporally resolved multimodal imaging datasets (advanced microscopy, MRI, PET, SPECT, CT) with functional read-outs (fMRI, EEG, MEG, electrophysiology, behaviour and clinical evaluation) and a molecular context (e.g., fluid biomarkers, genetic models, spatial omics) to achieve unprecedented insight into the nervous system and mechanisms of disease. Biologically, μNEURO spans a variety of neurological disorders including neurodegeneration, movement disorders, spinal cord and traumatic brain injury, glioblastoma and peripheral neuropathies, which are investigated in a variety of complementary model systems ranging from healthy control and patient-derived organoids and assembloids to fruit flies, rodents, and humans. With close collaboration between fundamental and preclinical research teams, method developers, and clinical departments at the University Hospital Antwerp (UZA), μNEURO effectively encompasses a fully translational platform for bench-to-bedside research. Now that we have intensified the interaction, in the next phase, μNEURO intends to formalize the integration by securing additional large-scale international research projects, by promoting the interaction between its members and core facilities and by fuelling high-risk-high-gain research within the hub and beyond. This way, μNEURO will foster breakthroughs for the neuroscience community. In addition, by focusing on technological and biological innovations that will streamline the translational pipeline for discovery and validation of novel biomarkers and therapeutic compounds, μNEURO aims to generate a long-term societal impact on the growing burden of rare and common diseases of the nervous system, connecting to key research priorities of the University of Antwerp, Belgium, and Europe.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: Baets Jonathan
• Co-promoter: Bertoglio Daniele
• Co-promoter: Bruffaerts Rose
• Co-promoter: De Vos Winnok
• Co-promoter: Ellender Tommas
• Co-promoter: Kumar-Singh Samir
• Co-promoter: Snoeckx Annemiek
• Co-promoter: Stroobants Sigrid
• Co-promoter: Timmerman Vincent
• Co-promoter: Van Dyck Pieter
• Co-promoter: Verhoye Marleen

Research team(s)

• Vision lab

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

Traumatic Brain Injury (TBI) is a sudden damage in the brain caused by an external force to the head. There are 50 million TBI cases worldwide every year, with over 2.5 million people in Europe. Mild TBI (mTBI) cases account for over 85% of the head injuries. In up to 40% of the cases, recovery from mTBI may be incomplete, with patients having persistent motor and psychological impairment for months to years after injury. Magnetic resonance imaging (MRI) is a helpful tool for documenting the extent of brain damage and is routinely applied in clinical practice. However, patients with mTBI often do not show abnormalities on conventional MRI, despite changes in behavior or cognitive deficits. This observation indicates that some microstructural changes in the brain cannot be detected by conventional MRI, but may be important determinants of a patient's clinical outcome. A number of studies using more advanced diffusion MRI (dMRI) have reported changes in diffusion parameters associated with meaningful clinical measures, such as cognitive and functional impairment in mTBI. Currently, radiologists and neurologists cannot predict the outcome of mTBI patients based on conventional MR images. However, recent developments in machine learning methods have shown promise for improving outcome prediction by combining multiple clinical parameters. We have previously explored Support Vector Machine learning algorithms for outcome prediction of mTBI patients based on voxel-wise analysis of FLAIR, SWI, FA and MD images. In the next step, we aim to explore Deep Learning algorithms for outcome prediction. Deep learning is facilitated by neural networks that mimic the neurons in the human brain, which autonomous learns from data. Specifically, we will implement a convolutional neural networks (CNN) based methods for outcome prediction in mTBI, using conventional and diffusion MRI from the multi-site CENTER-TBI project. Good ve\rsus incomplete recovery will be dichotomised using the Extended Glasgow Coma Scale (GOSE) score, evaluated at 6 months after injury. Ultimately, improved prediction of mTBI outcome would help to stratify patients and better organize the management of mTBI patients with poor outcome.

Researcher(s)

• Promoter: Van Dyck Pieter

Research team(s)

• Molecular Imaging and Radiology (MIRA)

Project type(s)

• Research Project

Abstract

Tears of the anterior cruciate ligament (ACL) of the knee are a frequent injury with increasing incidence. Surgical treatment of ACL injuries is superior to conservative treatment for the majority of patients to facilitate a return to the desired daily activities, including sports. Although ACL reconstruction using autograft tissue remains the gold standard for treating ACL injuries, there is a current surgical trend toward primary repair of the ACL. Successful surgery requires that the ACL graft or repair tissue transforms into ACL-like tissue. A common challenge in ACL surgery and rehabilitation is the lack of a noninvasive, sensitive outcome measure to evaluate the efficacy of surgical treatment. With the recent developments in MR technology, several advanced imaging techniques have now become available for use on clinical 3T scanners. In this project we will focus on the use of quantitative diffusion tensor imaging (DTI) to asses the normal, the injured and postoperative ACL. We will conduct a large-scale study to investigate the ability of DTI to monitor ACL healing both in patients with ACL reconstruction and primary repair of the ACL. It is our aim to document within-patient temporal changes using the DTI technique and to correlate DTI metrics with ACL structural properties. This will help in understanding the ACL healing process, and ultimately, in determining the appropriate timing for patients to return to sports.

Researcher(s)

• Promoter: Parizel Paul
• Promoter: Van Dyck Pieter
• Fellow: Van Dyck Pieter

Research team(s)

• Antwerp Surgical Training, Anatomy and Research Centre (ASTARC)
• Molecular Imaging and Radiology (MIRA)

Project type(s)

• Research Project