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.

- Real-time MRI
- High-Dimensional (Cardiac) MRI
- Model-based Reconstruction for Quantitative MRI
- High-Performance Computing
- Compressed Sensing (and Parallel Imaging)
- Sampling Theory for Parallel Imaging
- ENLIVE Algorithm for Parallel Imaging
- ESPIRiT Algorithm for Parallel Imaging
- NLINV Algorithm for Parallel Imaging

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.

- Christina Unterberg-Buchwald, Christian Oliver Ritter, Verena Reupke, Robin N. Wilke, Christine Stadelmann, Michael Steinmetz, Andreas Schuster, Gerd Hasenfuß, Joachim Lotz, Martin Uecker, Targeted endomyocardial biopsy guided by real-time magnetic resonance imaging, Journal of Cardiovascular Magnetic Resonance, 19:45 (2017) [open access] PMC5395773
- Sebastian Schaetz, Dirk Voit, Jens Frahm, and Martin Uecker, Accelerated Computing in Magnetic Resonance Imaging - Real-Time Imaging Using Non-Linear Inverse Reconstruction, Computational and Mathematical Methods in Medicine, 2017:3527269 (2017)
- Sebastian Schätz and Martin Uecker, A Multi-GPU Programming Library for Real-Time Applications, 12th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP-2012), Fukuoka 2012, In Lecture Notes in Computer Science, 7439:114-128 (2012) arXiv:1301.1215 [cs.DC]
- Shuo Zhang, Martin Uecker, Dirk Voit, Klaus-Dietmar Merboldt, and Jens Frahm, Real-time cardiac MRI at high temporal resolution: radial FLASH with nonlinear inverse reconstruction, Journal of Cardiovascular Magnetic Resonance 12:39 (2010)
- Martin Uecker, Shuo Zhang, Dirk Voit, Alexander Karaus, Klaus-Dietmar Merboldt, and Jens Frahm, Real-time magnetic resonance imaging at a resolution of 20 ms, NMR in Biomedicine 23: 986–994 (2010)
- Martin Uecker, Shuo Zhang, and Jens Frahm, Nonlinear Inverse Reconstruction for Real-time MRI of the Human Heart Using Undersampled Radial FLASH, Magnetic Resonance in Medicine 63 (6): 1456–1462 (2010)

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.

- Mehdi H. Moghari, Martin Uecker, Sebastien Roujol, Majid Sabbagh, Tal Geva, Andrew J. Powell, Accelerated Whole-heart Magnetic Resonance Angiography Using a Variable-Density Poisson-Disc Undersampling Pattern and Compressed Sensing Reconstruction, Magnetic Resonance in Medicine, 79:761-769 (2018)
- Joseph Y. Cheng, Tao Zhang, Marcus T. Alley, Martin Uecker, Michael Lustig, John M. Pauly, and Shreyas S. Vasanawala, Comprehensive Multi-Dimensional MRI for the Simultaneous Assessment of Cardiopulmonary Anatomy and Physiology, Scientific Reports 7:5330 (2017) [open access] PMC5509743
- Joseph Y. Cheng, Kate Hanneman, Tao Zhang, Marcus T. Alley, Peng Lai, Jonathan I. Tamir, Martin Uecker, John M. Pauly, Michael Lustig, Shreyas S. Vasanawala, Comprehensive Motion-Compensated Highly-Accelerated 4D Flow MRI with Ferumoxytol Enhancement for Pediatric Congenital Heart Disease, Journal of Magnetic Resonance Imaging, 43:1355-1368 (2016) PMC4865413
- Joseph Y. Cheng, Tao Zhang, Nichanan Ruangwattanapaisarn, Marcus T. Alley, Martin Uecker, John M. Pauly, Michael Lustig, Shreyas S. Vasanawala, Free-Breathing Pediatric MRI with Nonrigid Motion Correction and Acceleration, Journal of Magnetic Resonance Imaging, 42:407--420 (2015) PMC4404177 2015 ISMRM Young Investigator (W. S. Moore) Award, Winner
- Tao Zhang, Joseph Y. Cheng, Aaron G. Potnick, Richard A. Barth, Marcus T. Alley, Martin Uecker, Michael Lustig, John M. Pauly, and Shreyas S. Vasanawala, Fast Pediatric 3D Free Breathing Abdominal Dynamic Contrast Enhanced MRI with a High Spatiotemporal Resolution, Journal of Magnetic Resonance Imaging, 41:460-473 (2015) PMC4065644 2014 ISMRM Young Investigator (W. S. Moore) Award, Semi-Finalist

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.

- Xiaoqing Wang, Dirk Voit, Volkert Roeloffs, Martin Uecker, Jens Frahm, Fast Interleaved Multi-Slice T1 Mapping -- Model-based Reconstruction of Single-Shot Inversion-Recovery Radial FLASH. Computational and Mathematical Methods in Medicine, accepted (2018)
- Xiaoqing Wang, Volkert Roeloffs, Jakob Klosowski, Zhengguo Tan, Dirk Voit, Martin Uecker, and Jens Frahm, Model-based T1 Mapping with Sparsity Constraints Using Single-Shot Inversion-Recovery Radial FLASH, Magnetic Resonance in Medicine, 79:730-740 (2018)
- Jonathan I. Tamir, Martin Uecker, Weitian Chen, Peng Lai, Marcus T. Alley, Shreyas S. Vasanawala, Michael Lustig, T2 Shuffling: Sharp, Multicontrast, Volumetric Fast Spin-Echo Imaging, Magnetic Resonance in Medicine, 77:180-195 (2017) PMC4990508
- Tilman J. Sumpf, Andreas Petrovic, Martin Uecker, Florian Knoll, and Jens Frahm. Fast T2 Mapping with Improved Accuracy Using Undersampled Spin-echo MRI and Model-based Reconstructions with a Generating Function, IEEE Transactions on Medical Imaging, 33:2213-2222 (2014) arxiv:1405:3574 [physics.med-ph]
- Tilman J Sumpf, Martin Uecker, Susann Boretius, and Jens Frahm, Model-based Nonlinear Inverse Reconstruction for T2 Mapping Using Highly Undersampled Spin-Echo MRI, Journal of Magnetic Resonance Imaging, 34:420-428 (2011) [source code]
- Kai Tobias Block, Martin Uecker, and Jens Frahm, Model-based Iterative Reconstruction for Radial Fast Spin-Echo MRI, IEEE Transactions on Medical Imaging 28:1759-1769 (2009)
- Kai Tobias Block, Martin Uecker, and Jens Frahm, Iterative Reconstruction for R2 Mapping Based on Radial Fast Spin-Echo MRI, ISMRM Annual Meeting, Toronto 2008, In Proc. Intl. Soc. Mag. Reson. Med. 16: 1432 (2008)

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.

- Sebastian Schaetz, Dirk Voit, Jens Frahm, and Martin Uecker, Accelerated Computing in Magnetic Resonance Imaging - Real-Time Imaging Using Non-Linear Inverse Reconstruction, Computational and Mathematical Methods in Medicine, 2017:3527269 (2017)
- Martin Uecker, Frank Ong, Jonathan I Tamir, Dara Bahri, Patrick Virtue, Joseph Y Cheng, Tao Zhang, and Michael Lustig. Berkeley Advanced Reconstruction Toolbox, Annual Meeting ISMRM, Toronto 2015, In Proc. Intl. Soc. Mag. Reson. Med 23; 2486 (2015)
- Sebastian Schätz and Martin Uecker, A Multi-GPU Programming Library for Real-Time Applications, 12th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP-2012), Fukuoka 2012, In Lecture Notes in Computer Science, 7439:114-128 (2012) arXiv:1301.1215 [cs.DC]
- Martin Uecker, Shuo Zhang, and Jens Frahm, Nonlinear Inverse Reconstruction for Real-time MRI of the Human Heart Using Undersampled Radial FLASH, Magnetic Resonance in Medicine 63 (6): 1456-1462 (2010) [source code]

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.

- Florian Knoll, Christian Clason, Kristian Bredies, Martin Uecker, and Rudolf Stollberger, Parallel Imaging with Nonlinear Reconstruction using Variational Penalties, Magnetic Resonance in Medicine, 67:34-41 (2012) [free full text] PMC4011127 [PubMan] [source code] 2011 Stefan-Schuy-Award, Austrian Society for Biomedical Engineering, Winner
- Kai Tobias Block, Martin Uecker, and Jens Frahm, Undersampled Radial MRI with Multiple Coils. Iterative Image Reconstruction Using a Total Variation Constraint, Magnetic Resonance in Medicine 57:1086-1098 (2007) [free full text] [PubMan] 2007 ISMRM Young Investigator (I.I. Rabi) Award, Winner and highly cited paper: top 1% in clinical medicine

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.

- Vivek Athalye, Michael Lustig, and Martin Uecker. Parallel Magnetic Resonance Imaging as Approximation in a Reproducing Kernel Hilbert Space, Inverse Problems, 31:045008 (2015) arXiv:1310.7489 [physics.med-ph]

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.

- H. Christian M. Holme, Sebastian Rosenzweig, Frank Ong, Robin N. Wilke, Michael Lustig, Martin Uecker, ENLIVE: An Efficient Nonlinear Method for Calibrationless and Robust Parallel Imaging, submitted 2017, arXiv:1706.09780 [physics.med-ph]

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.

- Martin Uecker and Michael Lustig, Estimating Absolute-Phase Maps Using ESPIRiT and Virtual Conjugate Coils, Magnetic Resonance in Medicine, 77:1201-1207 (2017) PMC5018407 arXiv:1509.03557 [cs.CV]
- Martin Uecker, Peng Lai, Mark J. Murphy, Patrick Virtue, Michael Elad, John M. Pauly, Shreyas S. Vasanawala, and Michael Lustig, ESPIRiT - An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE meets GRAPPA. Magnetic Resonance in Medicine, 71:990-1001 (2014) PMC4142121 [source code: Reconstruction Toolbox] highly cited paper: top 1% in clinical medicine and highest cited paper published in Magnetic Resonance in Medicine in 2014

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.

- Sebastian Rosenzweig, H. Christian M. Holme, Robin N. Wilke, Dirk Voit, Jens Frahm, Martin Uecker, Simultaneous Multi-Slice Reconstruction Using Regularized Nonlinear Inversion: SMS-NLINV, Magnetic Resonance in Medicine, 79:2057-2066 (2018) arXiv:1705.04135 [physics.med-ph] Gö-VIP-16 Clinical Science Award
- Florian Knoll, Christian Clason, Kristian Bredies, Martin Uecker, and Rudolf Stollberger, Parallel Imaging with Nonlinear Reconstruction using Variational Penalties, Magnetic Resonance in Medicine, 67:34-41 (2012) [free full text] PMC4011127 [PubMan] [source code] 2011 Stefan-Schuy-Award, Austrian Society for Biomedical Engineering, Winner
- Martin Uecker, Shuo Zhang, and Jens Frahm, Nonlinear Inverse Reconstruction for Real-time MRI of the Human Heart Using Undersampled Radial FLASH, Magnetic Resonance in Medicine 63 (6): 1456-1462 (2010) [source code]
- Martin Uecker, Thorsten Hohage, Kai Tobias Block, and Jens Frahm, Image Reconstruction by Regularized Nonlinear Inversion - Joint Estimation of Coil Sensitivities and Image Content, Magnetic Resonance in Medicine 60:674-682 (2008) [free full text] [PubMan] [source code] Gorter Award, Finalist