Our group develops new methods for Cardiovascular Magnetic Resonance Imaging (CMRI) with the goal of faster, more robust, and quantitative imaging. Our research interests extend from the development of fundamentally new measurement techniques to the translation of new methods into clinical use. A long-term aim is to replace all traditional methods in CMRI which currently still rely on repeated breathholds and synchronization to an ECG with fast free-breathing techniques. A major achievement towards this was the development of a new method that allows two-dimensional imaging in real-time. Methodologically, we mostly focus on computational imaging methods that combine advanced numerical algorithms for image reconstruction with jointly designed data acquisition techniques.

Real-time MRI

Continuous advances in hardware and software have made it possible to image dynamic processes in the human body in real-time with good quality using MRI. Our method is based on a novel non-linear reconstruction algorithm for dynamic MRI. The method is fast enough to observe turbulence after stirring in a water beaker, visualize swallowing and speaking, and to acquire images of the human heart without synchronization to an ECG.

Figure: Real-time MRI of a human heart at a resolution of 50 ms.

Parallel Imaging as Approximation in a Reproducing Kernel Hilbert Space

The space of ideal signals in parallel magnetic resonance imaging is a Reproducing Kernel Hilbert Space (RKHS) of vector-valued functions which is characterized by a kernel derived from the receive sensitivities. Parallel imaging in k-space can be expressed as approximation in this space. This novel formulation yields insights about sampling in k-space which go beyond what is possible with the traditional g-factor analysis.

Figure: Human brain image reconstructed from randomly distributed samples using parallel imaging. Theoretical error bounds (power function) and noise amplification maps in k-space quantifiy how well missing samples can be recovered with parallel imaging from acquired samples (black dots). In this example, better results can be obtained with Cartesian or Poisson-disc sampling.

Autocalibrated Parallel MRI with ESPIRiT

ESPIRiT is a new algorithm for autocalibrated parallel MRI, which combines the robustness of GRAPPA with the speed and flexibility of a SENSE-based reconstruction. Implementations of ESPIRiT calibration and reconstruction are available in our reconstruction toolbox. The algorithm is related to multi-channel multi-variate spectral estimation.

Figure: Images of a human brain acquired with a small FOV. While the SENSE reconstruction has an artifact in the center, GRAPPA is free from this problem. Using multiple set of maps estimated with the ESPIRiT calibration method, a SENSE-based ESPIRiT reconstruction is able to produce an artifact-free image similar to GRAPPA.

Autocalibrated Parallel MRI with Nonlinear Inverse Reconstruction

Hiqh quality reconstruction in parallel MRI requires exact knowledge of the sensitivity profiles of the receive coils. In nonlinear inverse reconstruction, image content and coil sensitivities are estimated jointly, which improves reconstruction quality especially if the amount of calibration data is small. The problem leads to a blind-deconvolution problem (although the roles of frequency and time are switched in MRI). Because the technique can be applied directly to non-Cartesian data, it is ideal for real-time MRI with radial data acquisition.

Figure: 3D FLASH MRI with 2D acceleration of 4 = 2x2 and 16x16 reference lines. Comparison between GRAPPA, SENSE with coil sensitivities obtained from the fully sampled k-space center, and nonlinear inverse reconstruction (Inv).

(Non-Cartesian) Parallel MRI with Compressed Sensing

Compressed sensing is a new technique, which can be used to accelerate MRI by exploiting the redundancy of the acquired images. Parallel MRI and compressed sensing can be combined to achieve even higher acceleration. This can be formulated as a linear inverse problem with non-linear penalties.

Figure: Reconstruction of a human brain from 96, 48, and 24 radial spokes. (Top) Conventional gridding reconstruction (Bottom) Combination of linear non-Cartesian parallel imaging (generalized SENSE) and compressed sensing using a total-variation penalty.

Model-based Reconstruction

Model-based reconstruction methods formulate reconstruction as parameter estimation in domain-specific physical models. This leads improved quantitative MRI and can also be used to obtain multiple images with different contrast from a single scan.

Figure: Spin-density and color-coded T2 maps of the human brain obtained by GF-MARTINI reconstructions with validity mask and constant undersampling factors of 1, 2, 6, and 12. The corresponding measurement times were 12:54, 6:27, 2:09, and 1:05 min.