Tomographic reconstruction with adaptive sparsifying transforms

Pfister, Luke

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https://hdl.handle.net/2142/45347 Copy

Description

Title

Tomographic reconstruction with adaptive sparsifying transforms

Author(s)

Pfister, Luke

Issue Date

2013-08-22T16:37:21Z

Director of Research (if dissertation) or Advisor (if thesis)

Bresler, Yoram

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Sparsity
• Sparsifying Transforms
• Tomography
• Low-dose
• Iterative Reconstruction
• Alternating Direction Method of Multipliers (ADMM)

Abstract

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.

Graduation Semester

2013-08

Permalink

http://hdl.handle.net/2142/45347

Copyright and License Information

Copyright 2013 Luke Pfister

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