Parallel image reconstruction on MIMD computers for three-dimensional cone-beam tomography
Parallel Computing - Special double issue on biomedical applications
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
Algorithm 583: LSQR: Sparse Linear Equations and Least Squares Problems
ACM Transactions on Mathematical Software (TOMS)
Mathematical methods in image reconstruction
Mathematical methods in image reconstruction
The mathematics of computerized tomography
The mathematics of computerized tomography
Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms)
Fundamentals of Computerized Tomography: Image Reconstruction from Projections
Fundamentals of Computerized Tomography: Image Reconstruction from Projections
Performance instrumentation and measurement for terascale systems
ICCS'03 Proceedings of the 2003 international conference on Computational science
Constrained numerical optimization methods for blind deconvolution
Numerical Algorithms
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We describe an efficient parallel implementation of a reliable iterative reconstruction algorithm for estimating the three-dimensional (3D) density map of a macromolecular complex from a large number of two-dimensional (2D) cryo-electron microscopy (Cryo-EM) images. Our algorithm is based on a hybrid regularization approach first developed by Bj脙露rck and Oâ聙聶Learyâ聙聰Simmons. Our implementation uses a special data structure to represent the 3D density map to improve data locality in the reconstruction computation. Our parallelization strategy allows both 2D images and 3D data to be distributed on a 2D processor grid. We have used our implementation successfully on several datasets of different sizes, and we are able to achieve scalable parallel performance on a distributed memory cluster using over 15,000 CPUs for the largest dataset.