2026-09-19T05:51:45Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/45347 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Bresler, Yoram Pfister, Luke 2013-08-22T16:37:21Z 2013-08-22T16:37:21Z 2013-08 2013-08-22T16:37:21Z 2013-08 A major obstacle in computed tomography (CT) is the reduction of harmful x-ray dose while maintaining the quality of reconstructed images. Methods which exploit the sparse representations of tomographic images have long been known to improve the quality of reconstructions from low-dose data. Recent work has shown the promise of adaptive, rather than fixed, sparse representations. In particular, the synthesis dictionary learning framework has been shown to outperform traditional regularization techniques. However, these methods scale poorly with data size, and may be prohibitively expensive for practical tomographic reconstruction. In this thesis, we propose a new method for image reconstruction from low-dose data. The method combines a statistical iterative reconstruction framework with an adaptive sparsifying transform penalty. An alternating minimization approach is used to jointly reconstruct the image while learning a sparsifying transform adapted to the particular image being reconstructed. The Alternating Direction Method of Multipliers is used to provide a computationally efficient solution to the statistically weighted minimization problem. Numerical experiments are performed on phantom data and clinical CT images. Dose reduction is achieved through reduction in the number of views and reduction in the photon flux. The results indicate the adaptive sparsifying transform regularization outperforms state-of-the-art synthesis sparsity methods at speeds rivaling total-variation regularization. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-07-18T18:58:09Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Pfister_Luke.pdf: 9396023 bytes, checksum: 2352e1512aad1746f4dd4956c2088163 (MD5) Made available in DSpace on 2013-08-22T16:37:21Z (GMT). No. of bitstreams: 2 Luke_Pfister.pdf: 9396023 bytes, checksum: 2352e1512aad1746f4dd4956c2088163 (MD5) license.txt: 4062 bytes, checksum: d248a69f6036eb697b1a626a4b6b77dc (MD5) http://hdl.handle.net/2142/45347 en Copyright 2013 Luke Pfister Sparsity Sparsifying Transforms Tomography Low-dose Iterative Reconstruction Alternating Direction Method of Multipliers (ADMM) Tomographic reconstruction with adaptive sparsifying transforms text Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. MS:Electr & Computer Eng-UIUC 10KS1200MS