DataFully sampled and undersampled datasets – work in progressThis web site provides open datasets to researchers who desire to contribute to a community of reproducible research, where they can test and validate their algorithms against known undersampled acquisitions. These datasets were acquired through a collaboration between Michael Lustig at UC Berkeley and Dr. Shreyas Vasanawala at Stanford's Lucille Packard Children's Hospital. The undersampled datasets are of two varieties: variabledensity undersampling and uniformdensity undersampling. At present, all of the datasets are of knee images. In addition to undersampled datasets, we also provide separate cases of fully sampled knees, for researchers who wish to experiment with their own undersampling patterns SoftwareBART: Berkeley Advanced Reconstruction ToolboxIf you need a state of the art, efficient implementation of parallel imaging and compressed sensing, you have reached the right place. The Berkeley Advanced Reconstruction Toolbox (BART) is a free and opensource imagereconstruction framework for Magnetic Resonance Imaging (MRI). It consists of a programming library and a toolbox of commandline programs. The library provides common operations on multidimensional arrays, Fourier and wavelet transforms, as well as generic implementations of iterative optimization algorithms. The commandline tools provide direct access to basic operations on multidimensional arrays as well as efficient implementations of many calibration and reconstruction algorithms for parallel imaging and compressed sensing.
T2 Shuffling: Sharp, Multicontrast, Volumetric Fast SpinEcho ImagingThe following code contains a Matlab reference implementation of T2 Shuffling, an acquisition and reconstruction method based on 3D fast spinecho. T2 Shuffling accounts for temporal dynamics during the echo trains to reduce image blur and resolve multiple image contrasts along the T2 relaxation curve. The code was developed by Jon Tamir to demonstrate the methods and reproduce the figures in the paper:
Code and examples: GitHub Page ESPIRiT: Reference Implementation of Compressed Sensing and Parallel Imaging in MatlabThe Matlab code is a reference to the following papers:
Matlab Library:  Includes implementations of SPIRiT, ESPIRiT, Coil Compression, SAKE lowrank calibrationless Parallel Imaging and poissondisc sampling.
Sparse MRISparseMRI is a collection of Matlab functions that implement the algorithms and examples described in the paper M. Lustig, D.L Donoho and J.M Pauly “Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging” Magnetic Resonance in Medicine, 2007 Dec; 58(6):11821195. And in the highlevel, nonexpert overview M. Lustig, D.L Donoho, J.M Santos and J.M Pauly “Compressed Sensing MRI”, IEEE Signal Processing Magazine, 2008; 25(2): 7282
DivergenceFree Wavelet DenoisingThe following code contains an implementation of divergencefree wavelet, a vectorwavelet that provides a sparse representation of MR flow data. Divergencefree wavelet can be used to enforce “soft” divergencefree conditions when discretization and partial voluming result in numerical nondivergencefree components. Efficient 4D flow denoising is achieved by appropriate shrinkage of divergencefree and nondivergencefree wavelet coefficients. The package was developed by Frank Ong and accompanies the paper: Frank Ong, Martin Uecker, Umar Tariq, Albert Hsiao, Marcus T Alley, Shreyas S. Vasanawala and Michael Lustig , Robust 4D flow denoising using divergencefree wavelet transform, Magnetic Resonance in Medicine, 2014 Published online DOI: 10.1002/mrm.25176 Time Optimal Gradient DesignAn implementation of the algorithms and examples described in the paper M. Lustig SJ Kim and J.M Pauly, “A Fast Method for Designing Time Optimal Gradient Waveforms for Arbitrary kSpace Trajectories”, Transactions on Medical Imag ing, 2008; 27(6): 866873 The following is an improved method based on: S. Vaziri and M. Lustig “The Fastest Gradient Waveforms” which was accepeted for presentation at the annual Meeting of the ISMRM, 2012. The project was funded by the SRC Program and by an undergraduate research grant from Intel.
Nonrigid Motion Correction in 3D using Autofocussing and Buttefly NavigatorsPatient motion is a serious problem in MRI, and in particular when imaging pediatric patients. Butterfly navigators are modifications to the regular 2D/3DFT pulse sequence and allow collection of navigation information during the prewinder stage of the readout. In this work we use the navigators along with multichannel array and autofocussing image criteria to correct for nonrigid motion in body MRI of pediatric patients. The following code was developed by Joseph Cheng and accompanies the paper: Cheng JY, Alley MT, Cunningham CH, Vasanawala SS, Pauly JM, Lustig M. “Nonrigid Motion Correction in 3D Using Autofocusing with Localized Linear Translations,” Magnetic Resonance in Medicine 2012.
Coil Compression for Accelerated Imaging with Cartesian SamplingCoil arrays are used to accelerate the acquisition of MRI by exploiting the spatial sensitivity of the coils for spatial encoding. The increasing number of channels in systems today provides better acceleration, but at the same time results in significant increase in computation time. This in particularly a problem in iterative reconstructions. In this work we exploit redundancy between the channels and the fact that the readout dimension in Cartesian imaging is never subsampled to compress the coils data into MUCH fewer virtual coils. The software provided here is a Matlab protoype developed by Tao Zhang. It is the implementation of the Technique described in Zhang T, Pauly JM, Vasanawala SS, Lustig M. “Coil Compression for Accelerated Imaging with Cartesian Sampling,” MRM 2013;69(2):57182.
