B-Q MINDED has three scientific workpackages:
WP 1: Q-MRI algorithm and framework development
(WPL: EMC, Dr. D. Poot together with prof. W. Niessen)
WP1 aims to develop post-processing solutions for accelerating Q-MRI. The envisaged developments will be based on generic concepts that have the advantage of being applicable to both relaxometry and dMRI. Specifically, WP1 will focus on development of following technological building blocks:
- an integrated single-step parameter estimation framework (including motion modelling),
- advanced SRR approaches for Q-MRI
- intra-scan modulation multi-contrast (under-sampling) acquisitions,
- multi-band parallel imaging approaches,
- taking advantage of advanced simulations.
WP 2: Q-MRI implementation, testing and iterating
(WPL: JUEL, Dr. A.-M. Oros-P. and prof. N. Jon Shah)
WP2 is instrumental for the success of B-Q MINDED as it is positioned at the crossroad of fundamental academic (WP1) and more application-oriented (WP3) research. WP2 has two major goals:
- Serving as ‘Implementation Tool’: Converting theoretical Q-MRI algorithms and processing frameworks into working prototypes for verification. Prototypes will be benchmarked - and further iterated - in settings with gradually increasing levels of complexity, i.e. phantom tests, ex vivo tissues and in vivo evaluations.
- Generating biological ‘Proof of concept’ (PoC) of Q-MRI: data acquired in in silico simulations, animal experiments and small-scale clinical studies will improve our understanding of the meaning of changes in Q-MRI metrics and their possible role as biomarker of brain diseases.
WP 3: Translating B-Q MINDED results towards the market
(WPL: ICO, Dr. A Ribbens).
Often (Q-)MRI algorithms developed in academia do not reach the stage of maturity for clinical use. UA and EMC, however, previously demonstrated their ability to successfully translate academic knowledge into commercial success illustrated by two successful spin-off companies (ICO and QIB). WP3 will push Q-MRI results towards the market and has two major aims:
- Turning novel Q-MRI methods into (regulatory-approved) Q-MRI applications, either by de novo development or by integration in established platforms.
- Performance evaluation of the Q-MRI applications in relevant (clinical) settings to push the TRL level.