Our group develops new methods for (Cardiovascular) Magnetic Resonance Imaging (MRI) 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 MRI which currently still rely on repeated breathholds and synchronization to an ECG with fast free-breathing techniques. A major step towards this goal 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 new formulation of parallel MRI as a non-linear inverse problem (see below).
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. The images are reconstructed and displayed in real-time with sub-second latency. As one of many
important applications we are working on interventional MRI procedures under real-time MRI guidance.
Using the combination of compressed sensing and parallel imaging
(more) and the ESPIRIT
algorithm as implemented in our BART toolbox, we develop highly accelerated methods for MRI
in collaboration with researchers from UC Berkeley, Stanford University, and Harvard Medical School. These methods are under clinical evaluation in the Lucile Packard Children's Hospital and Boston Children's Hospital.
Model-based reconstruction methods formulate quantitative reconstruction
as parameter estimation in domain-specific physical models.
This enables the development of fundamentally methods for quantitative MRI with short scan times.
Iterative algorithms are computationally demanding. Early on,
we started to look at graphical processing units
for acceleration. In our work from 2010, we describe
Toeplitz embedding for highly accelerated image reconstruction
on Graphical Processing Units (GPUs) for non-Cartesian MRI - an
implementation technique we used for real-time MRI using
multi-GPU systems and that we later also reused when implementing
the nuFFT in our BART toolbox.
Martin Uecker, Frank Ong, Jonathan I Tamir, Dara Bahri, Patrick Virtue, Joseph Y Cheng, Tao Zhang, Michael Lustig.
Berkeley Advanced Reconstruction Toolbox.
Annual Meeting ISMRM, Toronto 2015, In Proc. Intl. Soc. Mag. Reson. Med 23; 2486 (2015)
(Non-Cartesian) Parallel MRI with Compressed Sensing
Compressed sensing is a new technique that can be used to accelerate
measurements by exploiting the
redundancy (compressibility) of the acquired data.
Our publication from 2007 is one of the first examples where this method is used in an imaging application. For more information about compressed sensing in MRI,
see this page from Michael Lustig at UC Berkeley who pioneered the use of this technique in MRI.
Parallel MRI and compressed sensing can be combined
to achieve even higher acceleration for MRI, which is the basis for
most advanced image reconstruction methods in MRI.
The 2007 paper is the first work to use this important combination.
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 using multiple receivers can be expressed as approximation in this space. This mathematical formulation yields
insights about sampling which go beyond what is possible with the traditional analysis.
ENLIVE is our new algorithm for efficient and robust
calibrationless parallel MRI. ENLIVE is based
on NLINV but also integrates
an important feature inspired by
ESPIRiT: The classical SENSE
model is relaxed to make the algorithm more robust
to model violations.
ESPIRiT is an algorithm for autocalibrated parallel MRI, which combines
the robustness of the GRAPPA method with the speed and flexibility of a SENSE-based
reconstruction methods. The corresponding publication with Peng Lai (GE Healthcare), Michael Lustig (UC Berkeley) and colleagues is the highest-cited research paper published in the year 2014 (after two years) in Magnetic Resonance in Medicine, the leading jounral in MR methodology. Implementations of ESPIRiT calibration and reconstruction
are available in our reconstruction toolbox.
Calibrationless 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 avoids an explicit calibration step
and 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. In fact, our
method for real-time MRI is based on this algorithm.