Justin P. Haldar: Software
Software implementations of many of the methods and algorithms we have developed is publically distributed, and implementations of other methods may be available on request. Please see the links and descriptions below.
BrainSuite is a collection of image analysis tools designed to process magnetic resonance images (MRI) of the human brain. BrainSuite13 provides an automatic sequence to extract cortical surface mesh models from the MRI, tools to register these to a labeled atlas to define anatomical regions of interest, and tools for processing diffusion imaging data (including distortion correction, coregistration to anatomical data, ODF and tensor fitting, and tractography). BrainSuite also contains visualization tools for exploring this kind of data, and can produce interactive maps of regional white matter connectivity. The software package includes an implementation of the Funk-Radon and Cosine Transform, distortion correction, and diffusion tensor estimation methods described respectively in:
An implementation of the low-rank signal reconstruction method described in:
Implementations of the greedy algorithms described in:
An implementation of a modified version of the regularized statistical denoising method described in:
Specifically, the algorithm has been modified to handle magnitude images, as described in:
An implementation example for a variation on the IRPF matrix recovery algorithm described in:
This IRPF code can be used to implement variations on the algorithms presented in:
J. P. Haldar, R. M. Leahy.
Linear Transforms for Fourier Data on the Sphere: Application to High Angular Resolution Diffusion MRI of the Brain.
NeuroImage 71:233-247, 2013.
C. Bhushan, J. P. Haldar, A. A. Joshi, R. M. Leahy.
Correcting Susceptibility-Induced Distortion in Diffusion-Weighted MRI using Constrained Nonrigid Registration.
Asia Pacific Signal and Information Processing Association (APSIPA) Annual Summit and Conference, Hollywood, 2012.
J. H. Kim, J. Haldar, Z.-P. Liang, S.-K. Song.
Diffusion Tensor Imaging of Mouse Brain Stem and Cervical Spinal Cord.
Journal of Neuroscience Methods 176:186-191, 2009.
An implementation example for the reconstruction algorithm described in:
The IMPATIENT MRI toolkit is an open-source CUDA-based GPU implementation of iterative MR image reconstruction. IMPATIENT MRI is very flexible, and enables efficient solution of MRI reconstruction problems involving non-Cartesian data acquisition, field inhomogeneity modeling, and regularization. Our GPU implementation work is described in journal papers (listed below) and several conference papers (which can be found on the Publications page):
J. P. Haldar, Z.-P. Liang.
Spatiotemporal Imaging With Partially Separable Functions: A Matrix Recovery Approach.
IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, 2010, pp. 716-719.
Recipient of an IEEE ISBI 2010 Best Student Paper Award.
B. Zhao, J. P. Haldar, C. Brinegar, Z.-P. Liang.
Low Rank Matrix Recovery for Real-Time Cardiac MRI.
IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, 2010, pp. 996-999.
J. P. Haldar, Z.-P. Liang.
Low-Rank Approximations for Dynamic Imaging.
IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, 2011, pp. 1052-1055.
The IMPATIENT MRI toolkit can be used to implement many of our published methods, including methods used to produce some of the results shown the following incomplete list of journal papers:
J. Gai, N. Obeid, J. L. Holtrop, X.-L. Wu, F. Lam, M. Fu, J. P. Haldar, W.-m. W. Hwu, Z.-P. Liang, B. P. Sutton.
More IMPATIENT: A Gridding-Accelerated Toeplitz-based Strategy for Non-Cartesian High-Resolution 3D MRI on GPUs.
Journal of Parallel and Distributed Computing 2013, In Press.
S. S. Stone, J. P. Haldar, S. C. Tsao, W.-m. W. Hwu, B. P. Sutton, Z.-P. Liang.
Accelerating Advanced MRI Reconstructions on GPUs.
Journal of Parallel and Distributed Computing 68:1307-1318, 2008.
J. P. Haldar, V. J. Wedeen, M. Nezamzadeh, G. Dai, M. W. Weiner, N. Schuff, Z.-P. Liang.
Improved Diffusion Imaging through SNR-Enhancing Joint Reconstruction.
Magnetic Resonance in Medicine 69:277-289, 2013.
J. P. Haldar, D. Hernando, Z.-P. Liang.
Compressed-Sensing MRI with Random Encoding.
IEEE Transactions on Medical Imaging 30:893-903, 2011.
J. P. Haldar, D. Hernando, S.-K. Song, Z.-P. Liang.
Anatomically Constrained Reconstruction from Noisy Data.
Magnetic Resonance in Medicine 59:810-818, 2008.