Key µNEURO Research Projects

With three joint PhD students, this project relies on an intensive collaboration between the imec-Vision Lab, Bio-Imaging Lab and the Laboratory of Cell Biology and Histology.

This project is funded by the Research Fund of the University of Antwerp (BOF-Impuls, 01/01/2025 - 31/12/2028).

Key µNEURO members involved: Prof. Jan Sijbers, Prof. Daniele Bertoglio, Prof. Winnok De Vos, Prof. Ben Jeurissen, Prof. Marleen Verhoye

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.

The MSCA Doctoral Network IQ-BRAIN is coordinated by the imec-Vision Lab, in close collaboration with the Bio-Imaging Lab. 

The project is funded by the European Union (December 2024- November 2028, Grant Agreement No. 101169519). 

Key µNEURO members involved: Prof. Jan Sijbers, Dr. Arjan den Dekker, Prof. Ben Jeurissen, Prof. Marleen Verhoye, Prof. Daniele Bertoglio, Dr. Liesbeth Vanherp

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.

The ERC Consolidator Grant of Prof. Ben Jeurissen focusses on developing a Data-driven Approach to Microstructural Imaging. 

The project is hosted in Imec-Vision Lab, and partly relies on collaboration with the Bio-Imaging Lab (Prof. Verhoye) and the Laboratory of Cell Biology and Histology (Prof. De Vos).

This project is funded by the European Research Council (ERC CoG, 01/05/2024 - 30/04/2029, Grant Agreement Nr. 101126235)​

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.