Breakthroughs in Quantitative Magnetic resonance ImagiNg for improved DEtection of brain Diseases

Project Information

B-Q MINDED

Grant agreement ID: 764513

DOI

10.3030/764513

Project closed

EC signature date 17 August 2017

Start date 1 January 2018

End date 31 March 2022

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 3 861 067,32

EU contribution

€ 3 861 067,32

Coordinated by

UNIVERSITEIT ANTWERPEN
Belgium

Periodic Reporting for period 2 - B-Q MINDED (Breakthroughs in Quantitative Magnetic resonance ImagiNg for improved DEtection of brainDiseases)

Reporting period: 2020-01-01 to 2022-03-31

Summary of the context and overall objectives of the project

Magnetic resonance imaging (MRI) is one of the most useful and rapidly growing neuroimaging tools. Unfortunately, signal intensities in conventional MRI images are expressed in relative units that depend on scanner hardware and acquisition protocols. While this does not hinder visual inspection of anatomy, it hampers quantitative comparison of tissue properties within a scan, between successive scans, and between subjects. In contrast, advanced quantitative MRI (Q-MRI) methods like MR relaxometry or diffusion MRI do enable absolute quantification of biophysical tissue characteristics. Evidence is growing that Q-MRI techniques detect subtle microscopic damage, enabling more accurate and early diagnosis of neurodegenerative diseases. However, due to the long scan time required for Q-MRI, causing discomfort for patients and limiting the throughput, Q-MRI methods have not entered clinical practice yet.
B-Q MINDED aims to overcome the current barriers by developing widely-applicable post-processing breakthroughs for accelerating Q-MRI. The originality of B-Q MINDED lies in its ambition to replace the conventional rigid multi-step processing pipeline with an integrated single-step parameter estimation framework. This approach will unlock a wealth of options for optimization of Q-MRI. To accomplish this goal, B-Q MINDED follows a collaborative cross-disciplinary approach (from basic MR physics to clinical applications) with strong involvement of industry (two MRI vendors and two MRI-software SMEs).
B-Q MINDED provides a unique training platform that enables young European researchers to develop a holistic view on Q-MRI research and development. The fellows enrolled in B-Q MINDED have access to a variety of network-wide training events and gain essential transferable skills that will positively affect their employability in academia and industry. By combining research, innovation, and education, B-Q MINDED aims to pave the way for introducing Q-MRI into the clinic.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the reporting period, proof-of-concept superresolution techniques, optimal experimental designs, multiple integrated quantitative mapping methods, and faster pulse sequences were developed and validated with simulation experiments. Additionally, the B-Q MINDED team performed real MRI experiments with hardware phantoms, small animals (ex-vivo and in-vivo), and human volunteers to validate the novel methods for (pre)clinical use.
The main results achieved so far can be summarized as follows:
● Neural networks were implemented for T1 relaxometry mapping. Preliminary results indicate that these networks are able to produce relaxometry maps with higher precision than conventional T1 mapping methods.
● A theoretical time-efficiency metric was developed to compare acquisition settings for a highly accelerated intra-scan modulated acquisition. The metric was used to find time-efficient undersampling patterns for 3D-inversion prepared fast spin echo acquisition which allows mapping T1 and T2 at high acceleration factors, beyond those
possible with parallel imaging.
● A proof-of-concept framework was developed that augments T1 super-resolution reconstruction with optimal experiment design.
● A framework to reduce scan times in diffusion MRI was developed by blending diffusion parameter estimation and intra-scan contrast modulation. Simulation results have shown that using this joint model-based framework, diffusion parameters can be estimated more accurately and precisely than with conventional methods.
● Multislice EPI, FLASH and RARE sequences for fast read-out were tested and compared for super-resolution purposes. T1 mapping techniques were optimized based on gradient echo sequences with focus on parameters selection, spoiling and steady state.
● Quantitative echo planar imaging (QUTE) for T2* mapping has been implemented for a large number of echoes. Using an intra-scan modulation approach that combines image reconstruction and parameter estimation in a joint framework, it was shown that it is possible to substantially reduce the acquisition time compared to the
conventional QUTE imaging sequence.
● A generative numerical model for myocardium microstructure has been generated to simulate the diffusion MRI signal at a sub-voxel scale.
● Methods have been studied to standardize brain MRI diffusion maps for multi-centre studies. State-of-the-art data harmonization methods to reduce the between-subject variability in the population data (such as ComBat) were implemented and are being used as benchmark for novel standardization developments.
● Outlier rejection methods have been implemented to reduce within-subject variability of diffusion tensor imaging and diffusion kurtosis imaging parameters. The performance of these outlier rejection methods were quantified in terms of improved reproducibility and improved sensitivity for pathology.
● A super-resolution reconstruction method for T2* mapping for quantitative musculoskeletal MRI has been developed and implemented on a clinical MRI system.
● Segmentation algorithms that are conventionally applied to structural MRI data (e.g. T1-weighted images) have been adapted for quantitative T1 maps.
● 2D and 3D radial pulse sequences were implemented and signal corrections for echo alignment were developed for 3D radial pulse sequences. This will allow acquisitions with a very short echo time.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Based on the project’s progress so far, we believe that B-Q MINDED is on track to achieve its ambition to remove current technological barriers that prevent quantitative MRI to be used in routine clinical practice. We expect that the newly developed methods for (super resolution) image reconstruction, parameter estimation, intra-scan modulation and optimal experiment design have the potential to provide the required reduction of scan time. We are well on track to reduce image acquisition time by a factor 2-5. Furthermore, the in silico numerical models that are being developed will support the biological interpretation of the Q-MRI metrics, in particular in the domain of diffusion imaging and relaxometry. Since the potential of the worldwide (pre)clinical Q-MRI market is huge and there is a clear demand for technological solutions to reduce scan time for improving throughput, cost efficiency and patient comfort, the potential socio-economic impact of a successful delivery of our project goals is very high. Moreover, reduction of scan time through accelerated Q-MRI limits motion related artefacts and hence improves image quality, which has a direct positive impact on clinical diagnostic value.

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