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Dr.-Ing. Robert Grimm

Alumnus of the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg

Self-gating Reconstructions of Motion and Perfusion for Free-breathing T1-weighted DCE-MRI of the Thorax Using 3D Stack-of-stars GRE Imaging
  • Dynamic imaging of the lung and abdomen requires a trade-off between spatial resolution, temporal resolution, and total acquisition time. Due to its robustness against motion, the clinical use of free-breathing 3D  radial spoiled gradient echo (VIBE) imaging for abdominal applications has recently gained interest. It has also been shown that self-gating techniques enable the  reconstruction of different respiratory phases and compensation of respiratory motion in dynamic contrast-enhanced MRI (DCE-MRI). Typically, however, separate  acquisitions are required to capture the respiratory movement and dynamic perfusion. We show that a retrospective self-gating technique can be employed to extract all information from a single continuous free-breathing scan acquired with a stack-of-stars 3D VIBE sequence. This technique can be used to compute different sliding-window reconstructions that show a complete ‘virtual’ respiratory cycle with high spatial resolution, or, alternatively, a time series of the contrast-enhancement at a specified level of inspiration and with high temporal resolution.

    Related Publication:
    Grimm, Robert; Block, Kai Tobias; Hutter, Jana; Forman, Christoph; Hintze, Christian; Kiefer, Berthold; Hornegger, Joachim
    Proceedings of International Society for Magnetic Resonance in Medicine (ISMRM 20th Annual Meeting), Melbourne, Australia, May 5-11, pp. 3814, 2012 (BiBTeX, Who cited this?)
A Digital Perfusion Phantom for T1-weighted DCE-MRI
  • Recently, novel Compressed Sensing methods have been proposed that promise very high undersampling factors in the spatial as well as temporal domain. However, it is unclear if these advanced reconstruction techniques preserve the true dynamics of the contrast enhancement, which otherwise could have significant impact on the quantitative analysis of the contrast-agent kinetics. In the latter case, a noisy image that reflects the true underlying dynamics would be more favorable than a beautified image that shows less noise and undersampling artifacts but deviates from the true course of contrast enhancement. To investigate these effects, we developed an extension to the well-known analytical Shepp-Logan phantom in the temporal dimension to simulate contrast enhancement in arteries and in healthy and pathological  tissue. Unlike image-based approaches, a k-space based phantom allows for accurate sampling along arbitrary trajectories. This enables assessing the influence  of k-space sampling strategies as well as the evaluation of reconstruction techniques for dynamic imaging. MATLAB source code for the phantom as well as for the generation of the contrast dynamics will be made available online Opens internal link in current windowhere.

    Related Publication:
    Grimm, Robert; Churt, Johannes; Fieselmann, Andreas; Block, Kai Tobias; Kiefer, Berthold; Hornegger, Joachim
    Proceedings of International Society for Magnetic Resonance in Medicine (ISMRM 20th Annual Meeting), Melbourne (Australia), May 5-11, pp. 2559, 2012 (BiBTeX, Who cited this?)
Bias Correction for Respiration Detection in Radial 3D Gradient-Echo Imaging
  • Radial sampling of k-space allows to deduce information about the respiratory cycle directly from the data (“self-gating”). 
    Such signals are often biased  by imperfect gradient timings, leading to an angular modulation. For golden-angle trajectories with an angular increment of 111.25°, this causes high-frequency variations. Conventional band-pass filtering reduces the fluctuations but causes loss of temporal accuracy. We propose a robust filtering scheme to suppress the angular dependency in self-gating signals extracted from golden-angle radial MRI data. The proposed filter tolerates irregular breathing and preserves temporal resolution.

    Related Publication:
    Grimm, Robert; Block, Kai Tobias; Kiefer, Berthold; Hornegger, Joachim
    Proceedings of International Society for Magnetic Resonance in Medicine (ISMRM 19th Annual Meeting), Montréal, Québec, Kanada, 07.-13.05.2011, vol. 19, pp. 2677, 2011 (BiBTeX, Who cited this?)
Iterative Reconstruction Techniques for Radial Perfusion MRI

Radial MRI

In Magnetic Resonance Imaging (MRI), image acquisition takes place in the frequency domain. For samples taken on a cartesian grid, the image is reconstructed by simply computing the 2D Fast Fourier Transform of the measurement data.

With current hardware, also non-cartesian trajectories like spirals or radial spokes are feasible. Compared to cartesian trajectories, they offer several advantages such as improved robustness against motion artifacts.
However, commonly used parallel imaging techniques for cartesian MRI cannot be applied easily to radial data. Moreover, to achieve the same spatial resolution, more samples are required, resulting in longer imaging times.

To accelerate radial MRI, the number of radial spokes can be reduced. This undersampling leads to streaking artifacts in the reconstructed image: 


Iterative Reconstruction

MR image reconstruction can also be formulated as solving the linear equation

A x = y,

where x is the (unknown) image, y the observed data, and A the forward system matrix. For fully sampled cartesian data, A could simply be the matrix that applies the Discrete Fourier Transform to vector x

In general, the equation cannot be inverted directly. Instead, an iterative optimization scheme estimates the image that matches the observations best. A regularization term is incorporated to enforce sparsity of the estimated image in the Total Variation (TV) domain, or, in other words, to penalizes the streaking artifacts. With a weighting factor of  λ for the TV penalty, the unconstrained optimization problem is:

Minimize ||A x - y||2 + λ RTV).