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.
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.
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.
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.
Martin Uecker, Patrick Virtue, Shreyas S Vasanawala, and Michael Lustig. ESPIRiT Reconstruction Using Soft SENSE. Annual Meeting ISMRM, Salt Lake City 2013, In Proc. Intl. Soc. Mag. Reson. Med 21; 127 (2013)
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.
(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
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.