Research team

Expertise

Prof. dr. Pieter Van Dyck is head clinic radiology at the Antwerp University Hospital (UZA) specialized in musculoskeletal (MSK) imaging. He has large clinical expertise in conventional radiography (XR), ultrasound (US), computer tomography (CT) and magnetic resonance (MRI) imaging of the MSK system. Clinical research in the domain of new and advanced MRI techniques for MSK applications in collaboration with the Imec-Vision Lab (UAntwerpen). Emphasis on the development, clinical validation and implementation of super-resolution reconstruction (SRR) techniques for anatomical and quantitative MRI of the knee.

Development of algorithms to predict outcome after mild traumatic brain injury using magnetic resonance imaging. 01/09/2021 - 31/08/2022

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)

Research team(s)

Project type(s)

  • Research Project

Quantitative diffusion tensor imaging of the postoperative anterior cruciate ligament of the knee. 01/10/2016 - 30/09/2021

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)

Research team(s)

Project type(s)

  • Research Project