ABSTRACT
Computed tomography (CT) image reconstruction is a crucial technique for many imaging applications. Among various reconstruction methods, Model-Based Iterative Reconstruction (MBIR) enables super-resolution with superior image quality. MBIR, however, has a high memory requirement that limits the achievable image resolution, and the parallelization for MBIR suffers from limited scalability. In this paper, we propose Asynchronous Consensus MBIR (AC-MBIR) that uses Consensus Equilibrium (CE) to provide a super-resolution algorithm with a small memory footprint, low communication overhead and a high scalability. Super-resolution experiments show that AC-MBIR has a 6.8 times smaller memory footprint and 16 times more scalability, compared with the state-of-the-art MBIR implementation, and maintains a 100% strong scaling efficiency at 146880 cores. In addition, AC-MBIR achieves an average bandwidth of 3.5 petabytes per second at 587520 cores.
- S. Ahn and J. A. Fessler. 2003. Globally Convergent Image Reconstruction for Emission Tomography Using Relaxed Ordered Subsets Algorithms. IEEE Transactions on Medical Imaging 22, 5 (May 2003), 613--626. Google ScholarCross Ref
- GE Aviation. 2016. Space Age Ceramics Are Aviation's New Cup Of Tea. https://www.ge.com/reports/space-age-cmcs-aviations-new-cup-of-tea/. (2016).Google Scholar
- T. Balke, S. Majee, G. T. Buzzard, S. Poveromo, P. Howard, M. A. Groeber, J. McClure, and C. A. Bouman. 2018. Separable Models for cone-beam MBIR Reconstruction. In Computational Imaging XVI, Burlingame, California, USA, 28 Jan 2018 - 1 Feb 2018.Google Scholar
- G. Buzzard, S. Chan, S. Sreehari, and C. Bouman. 2018. Plug-and-Play Unplugged: Optimization-Free Reconstruction Using Consensus Equilibrium. SIAM Journal on Imaging Sciences 11, 3 (2018), 2001--2020. arXiv:https://doi.org/10.1137/17M1122451 Google ScholarCross Ref
- H. Erdogan and J. Fessler. 1999. Ordered Subsets Algorithms for Transmission Tomography. Physics in Medicine & Biology 44(11) (1999).Google Scholar
- J. A. Fessler, E. P. Ficaro, N. H. Clinthorne, and K. Lange. 1997. Grouped-Coordinate Ascent Algorithms for Penalized-Likelihood Transmission Image Reconstruction. IEEE Transactions on Medical Imaging 16(2) (1997).Google Scholar
- J. A. Fessler and D. Kim. 2011. Axial Block Coordinate Descent (ABCD) Algorithm for X-ray CT Image Reconstruction. In 11th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine.Google Scholar
- M. C. Fonseca, B. H. S. Araujo, C. S. B. Dias, N. L. Archilha, D. P. A. Neto, E. Cavalheiro, H. Westfahl, A. J. R. da Silva, and K. G. Franchini. 2018. High-Resolution Synchrotron-Based X-ray Microtomography as A Tool to Unveil The Three-Dimensional Neuronal Architecture of The Brain. Scientific Reports 8, 1 (Aug 2018), 12074.Google ScholarCross Ref
- C. Fournier, F. Jolivet, L. Denis, N. Verrier, E. Thiebaut, C. Allier, and T. Fournel. 2017. Pixel Super-Resolution in Digital Holography by Regularized Reconstruction. Applied Optics 56, 1 (Jan 2017), 69--77. Google ScholarCross Ref
- S. Ha and K. Mueller. 2018. A GPU-Accelerated Multivoxel Update Scheme for Iterative Coordinate Descent (ICD) Optimization in Statistical Iterative CT Reconstruction (SIR). IEEE Transactions on Computational Imaging 4, 3 (Sep. 2018), 355--365. Google ScholarCross Ref
- C. Jiang, Q. Zhang, R. Fan, and Z. Hu. 2018. Super-Resolution CT Image Reconstruction Based on Dictionary Learning and Sparse Representation. Scientific Reports 8(1) (2018).Google Scholar
- P. Jin, C. A. Bouman, and K. D. Sauer. 2013. A Method for Simultaneous Image Reconstruction and Beam Hardening Correction. In 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 1--5.Google Scholar
- E. A. Kazerooni. 2001. High-resolution CT of the lungs. American Journal of Roentgenology 177, 3 (Sep 2001), 501--519.Google ScholarCross Ref
- A. Korn, M. Fenchel, B. Bender, S. Danz, T. K. Hauser, D. Ketelsen, T. Flohr, C. D. Claussen, M. Heuschmid, U. Ernemann, and H. Brodoefel. 2012. Iterative Reconstruction in Head CT: Image Quality of Routine and Low-Dose Protocols in Comparison with Standard Filtered Back-Projection. American Journal of Neuroradiology 33, 2 (Feb 2012), 218--224.Google ScholarCross Ref
- M. Li, T. Zhang, Y. Chen, and A. J. Smola. 2014. Efficient Mini-batch Training for Stochastic Optimization. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '14). ACM, New York, NY, USA, 661--670. Google ScholarDigital Library
- X. Li, Y. Liang, W. Zhang, T. Liu, H. Li, G. Luo, and M. Jiang. 2018. cuMBIR: An Efficient Framework for Low-dose X-ray CT Image Reconstruction on GPUs. In Proceedings of the 2018 International Conference on Supercomputing (ICS '18). ACM, New York, NY, USA, 184--194. Google ScholarDigital Library
- P. Milanfar. 2010. Super-Resolution Imaging. CRC Press.Google Scholar
- K. A. Mohan, S. V. Venkatakrishnan, J. W. Gibbs, E. B. Gulsoy, X. Xiao, M. D. Graef, P. W. Voorhees, and C. A. Bouman. 2015. TIMBER: A Method for Time-Space Reconstruction from Interlaced Views. IEEE Transactions on Computational Imaging 1, 2 (June 2015), 96--111.Google Scholar
- Z. Nadir, M. S. Brown, M. L. Comer, and C. A. Bouman. 2017. A Model-Based Iterative Reconstruction Approach to Tunable Diode Laser Absorption Tomography. IEEE Transactions on Computational Imaging 3, 4 (Dec 2017), 876--890.Google ScholarCross Ref
- A. Sabne, X. Wang, S. J. Kisner, C. A. Bouman, A. Raghunathan, and S. P. Midkiff. 2017. Model-Based Iterative CT Image Reconstruction on GPUs. In Proceedings of the 22Nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP '17). ACM, New York, NY, USA, 207--220. Google ScholarDigital Library
- S. Sreehari, S. V. Venkatakrishnan, K. L. Bouman, J. P. Simmons, L. F. Drummy, and C. A. Bouman. 2016. Multi-Resolution Data Fusion for Super-Resolution Electron Microscopy. CoRR abs/1612.00874 (2016). arXiv:1612.00874 http://arxiv.org/abs/1612.00874Google Scholar
- V. Sridhar, G. T. Buzzard, and C. A. Bouman. 2018. Distributed Framework for Fast Iterative CT Reconstruction from View-subsets. In Computational Imaging XVI, Burlingame, California, USA, 28 Jan 2018 - 1 Feb 2018.Google Scholar
- J. B. Thibault, K. D. Sauer, C. A. Bouman, and J. Hsieh. 2007. A Three-Dimensional Statistical Approach to Improved Image Quality for Multi-Slice Helical CT. Medical Physics 34(11) (2007).Google Scholar
- S. V. Venkatakrishnan, L. F. Drummy, M. Jackson, M. D. Graef, J. Simmons, and C. A. Bouman. 2013. High Angle Annular Dark Field- Scanning Transmission Electron Microscope (HAADF-STEM) Tomography. IEEE Transactions on Image Processing 22(1) (2013).Google Scholar
- X. Wang, K. A. Mohan, S. J. Kisner, C. A. Bouman, and S. P. Midkiff. 2016. Fast Voxel Line Update for Time-Space Image Reconstruction. In The 41st IEEE International Conference on Acoustics, Speech and Signal Processing.Google Scholar
- X. Wang, A. Sabne, S. J. Kisner, A. Raghunathan, C. A. Bouman, and S. P. Midkiff. 2016. High Performance Model Based Image Reconstruction. SIGPLAN Notice 51, 8, Article 2 (Feb. 2016), 12 pages. Google ScholarDigital Library
- X. Wang, A. Sabne, P. Sakdhnagool, S. J. Kisner, C. A. Bouman, and S. P. Midkiff. 2017. Massively Parallel 3D Image Reconstruction. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '17). ACM, New York, NY, USA, Article 3, 12 pages. Google ScholarDigital Library
- R. Yan, S. V. Venkatakrishnan, J. Liu, C. A. Bouman, and W. Jiang. 2019. MBIR: A Cryo-ET 3D Reconstruction Method that Effectively Minimizes Missing Wedge Artifacts and Restores Missing Information. Journal of Structural Biology (Mar 2019).Google Scholar
- Z. Yu, J-B. Thibault, C. A. Bouman, K. D. Sauer, and J. Hsieh. 2011. Fast Model-Based X-Ray CT Reconstruction using Spatially Nonhomogeneous ICD Optimization. IEEE Transactions on Image Processing 20(1) (2011).Google Scholar
- R. Zhang and J. T. Kwok. 2014. Asynchronous Distributed ADMM for Consensus Optimization. In Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32 (ICML'14). JMLR.org, II-1701--II-1709.Google Scholar
- J. Zheng, S. S. Saquib, K. Sauer, and C. A. Bouman. 2000. Parallelizable Bayesian Tomography Algorithms with Rapid, Guaranteed Convergence. IEEE Transactions on Image Processing 9(10) (2000).Google Scholar
- M. Zinkevich, M. Weimer, L. Li, and A. J. Smola. 2010. Parallelized Stochastic Gradient Descent. In Advances in Neural Information Processing Systems 23. Curran Associates, Inc., 2595--2603. http://papers.nips.cc/paper/4006-parallelized-stochastic-gradient-descent.pdfGoogle Scholar
Index Terms
- Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction
Recommendations
Improved Super-Resolution Reconstruction From Video
Super-resolution (SR) reconstruction usually consists of four steps: registration, interpolation, restoration, and postprocessing. The registration precision (RP) and the initial SR image estimation (ISIE) greatly influence the quality of reconstructed ...
Super-resolution acquisition and reconstruction for cone-beam SPECT with low-resolution detector
Highlights- Novel super resolution reconstruction algorithm for cone-beam SPECT with low resolution detector.
Abstract Background and objectiveSingle-photon emission computed tomography (SPECT) imaging, which provides information that reflects the human body's metabolic processes, has unique application value in disease diagnosis and ...
Guided filter‐based multi‐scale super‐resolution reconstruction
The learning‐based super‐resolution reconstruction method inputs a low‐resolution image into a network, and learns a non‐linear mapping relationship between low‐resolution and high‐resolution through the network. In this study, the multi‐scale super‐...
Comments