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Quantitative MRI

Research group 8.13

Quantitative cardiac MRI

The contrast of MR images is principally determined by the parameters of an MR acquisition (sequence parameters) and by intrinsic tissue characteristics such as proton density, longitudinal (T1) and transversal (T2) relaxation times. Quantitative MRI provides information on these intrinsic tissue parameters using so-called mapping techniques. These yield highly valuable diagnostic information independent of external parameters (vendor of MR scanner, sequence parameters, etc.).

The main challenge of mapping techniques is the long acquisition time, which makes them difficult to apply in standard clinical practice. Novel data acquisition and image reconstruction methods can overcome this problem.

Model-based image reconstruction

The majority of quantitative MRI techniques record multiple images with different acquisition parameters. After image reconstruction, a signal model is fitted to the images on a pixel-by-pixel basis in order to obtain the quantitative tissue parameters.

During image reconstruction, model-based reconstruction approaches utilise information about the behaviour of magnetisation over time (e.g. T1-recovery after an inversion pulse) as prior knowledge. This additional information allows for a more accurate reconstruction and directly yields quantitative tissue parameters. In addition, this technique facilitates more flexible and efficient data acquisition.

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Simulated short-axis slice showing the change of image contrast after an MR inversion pulse. The recovery of the longitudinal magnetisation (Mz) depends on intrinsic tissue parameters. These relaxation curves can be utilised during image reconstruction to improve image quality and accuracy of mapping techniques.

Clinical applications

In cardiac MRI, T1-mapping is commonly used to assess the viability of the myocardium. A wide range of different diseases such as myocardial infarction and fibrosis lead to changes in the T1-times of the myocardium and can therefore be detected and diagnosed. Faster and more accurate mapping approaches hold the promise of an earlier and more precise diagnosis leading to improved treatment outcomes.

The main challenge of the majority of cardiac MRI techniques is physiological motion of the heart due to breathing and the heart beat. To overcome this problem, we are working on novel approaches for motion-compensated image reconstruction to achieve an accurate assessment of quantitative tissue parameters independent of any physiological motion.

This development of novel techniques is being carried out in close collaboration with Prof. Dr. Jeanette Schulz-Menger, head of the MRI group at the “Experimental and Clinical Research Center“ (ECRC) at Charité Berlin.

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Real-time reconstruction showing a 4-chamber view of the heart with a frame rate of 40 fps. Images are reconstructed using direct (gridding) and iterative image reconstruction techniques. The temporal profiles of a line through the left and right ventricle are shown on the right hand side. The contraction of the ventricles during the cardiac cycles and the changes of the heart due to respiratory motion are clearly visible. This data yields both cardiac and respiratory motion information which can be utilised in a motion-compensated image reconstruction to improve image quality and achieve high diagnostic accuracy.

Current projects

Model-based image reconstruction for fast and accurate T1-mapping of the heart

Development of novel motion-compensated T1-mapping approaches which utilise signal models during image reconstruction in order to provide accurate T1 maps in the shortest possible scan time.

Further information on motion compensation

Cooperation with:
Prof. Dr. Jeanette Schulz-Menger, Experimental and Clinical Research Center, Charité Berlin, Germany

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Selected references

K. Becker, J. Schulz-Menger, T. Schaeffter, C. Kolbitsch,
Multi-parametric cardiac MRI for T1 mapping and cine imaging using iterative model-based image reconstruction
Proceedings of the 25th Annual Meeting of ISMRM, Honolulu, USA, 2722 (2017).

M. Usman, D. Atkinson, C. Kolbitsch, T. Schaeffter, C. Prieto
Manifold learning based ECG-free free-breathing cardiac CINE MRI
Opens external link in new window J Magn Reson Imaging 41, 1521-7 (2015).

C. Kolbitsch, C. Prieto, T. Schaeffter
Cardiac functional assessment without electrocardiogram using physiological self-navigation
Opens external link in new window Magn Reson Med 71, 942-54 (2014).

M. Usman, D. Atkinson, F. Odille, C. Kolbitsch, G. Vaillant, T. Schaeffter, P. Batchelor, C. Prieto
Motion corrected compressed sensing for free-breathing dynamic cardiac MRI
Opens external link in new window Magn Reson Med 70, 504-16 (2013).

A. Freitas, C. Kolbitsch, T. Schaeffter
Optimization of inversion time using a multi-tissue model to reduce artifacts in delayed enhancement cardiac MRI due to heart rate variations
MAGMA 26, 151-301 (2013).

C. Kolbitsch, T. Schaeffter, J. Smink, C. Prieto
Image-based self-navigator using cardiac functional parameters for cine imaging
Proceedings of the 20th Annual Meeting of ISMRM, Melbourne, Australia, 602 (2012).

J. Burakiewicz, C. Kolbitsch, G. Charles-Edwards, T. Schaeffter
Reducing artefacts in inversion recovery prepared MRI caused by varying heart rate through real-time adaptation of the inversion time
Proceedings of the 19th Annual Meeting of ISMRM, Montreal, Canada, 4620 (2011).

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