Magnetic Resonance Imaging (MRI) is one of the most important diagnostic imaging techniques, with over 30 Million scans per year in the EU. Alterations in the intrinsic physical tissue parameters measured with MRI, such as of longitudinal (T1) and transverse (T2) relaxation times, have been implicated in major neurological conditions. Although these differences have been noted as useful signs in image contrasts, parameter quantification has not been exploited as a marker for disease stage or for monitoring treatment efficacy. Previous attempts to perform quantitative MRI protocols have suffered from sensitivity to system imperfections as well as infeasible, long acquisition times.
Recently, a new approach called MR fingerprinting (MRF) has been developed for the estimation of multiple parameters at once, featuring a new dedicated acquisition strategy. The method has shown great promise as data can be acquired in clinically acceptable time, is relatively insensitive to system imperfections and has high accuracy. In this project, we aimed to build on these novel techniques by demonstrating novel 3D MRF acquisition for imaging the whole brain using optimized acquisitions and reconstructions. We compared different strategies, using new automatic optimization algorithms and iterative reconstructions.
In improving imaging technology, research from QuantMR7 has worked towards addressing two significant limitations of current MRI examinations: firstly, the methods developed and demonstrated during the project go towards a quantitative rather than qualitative depiction of image information for radiological assessment and automated feature extraction; secondly, these methods allow for a simultaneous assessment of multiple parameters at once, greatly accelerating MRI examinations.
