Sparse MRI reconstruction via multiscale L0-continuation

Joshua Trzasko; Armando Manduca; Eric Borisch

doi:10.1109/SSP.2007.4301242

Sparse MRI reconstruction via multiscale L₀-continuation

Joshua Trzasko, Armando Manduca, Eric Borisch

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

23 Scopus citations

Abstract

"Compressed Sensing" and related L₁-minimization methods for reconstructing sparse magnetic resonance images (MRI) acquired at sub-Nyquist rates have shown great potential for dramatically reducing exam duration. Nonetheless, the nontriviality of numerical implementation and computational intensity of these reconstruction algorithms has thus far precluded their widespread use in clinical practice. In this work, we propose a novel MRI reconstruction framework based on homotopy continuation of the L ₀ semi-norm using redescending M-estimator functions. Following analysis of the continuation scheme, the sparsity measure is extended to multiscale form and a simple numerical solver that can achieve accurate reconstructions in a matter of seconds on a standard desktop computer is presented.

Original language	English (US)
Title of host publication	2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings
Pages	176-180
Number of pages	5
DOIs	https://doi.org/10.1109/SSP.2007.4301242
State	Published - Dec 1 2007
Event	2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007 - Madison, WI, United States Duration: Aug 26 2007 → Aug 29 2007

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings

Other

Other	2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007
Country	United States
City	Madison, WI
Period	8/26/07 → 8/29/07

Keywords

Homotopy
L- minimization
Magnetic resonance imaging
Sparse reconstruction

ASJC Scopus subject areas

Signal Processing

Access to Document

10.1109/SSP.2007.4301242

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Trzasko, J., Manduca, A., & Borisch, E. (2007). Sparse MRI reconstruction via multiscale L₀-continuation. In 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings (pp. 176-180). [4301242] (IEEE Workshop on Statistical Signal Processing Proceedings). https://doi.org/10.1109/SSP.2007.4301242

Trzasko, J , Manduca, A & Borisch, E 2007, Sparse MRI reconstruction via multiscale L₀-continuation. in 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings., 4301242, IEEE Workshop on Statistical Signal Processing Proceedings, pp. 176-180, 2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007, Madison, WI, United States, 8/26/07. https://doi.org/10.1109/SSP.2007.4301242

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abstract = "{"}Compressed Sensing{"} and related L1-minimization methods for reconstructing sparse magnetic resonance images (MRI) acquired at sub-Nyquist rates have shown great potential for dramatically reducing exam duration. Nonetheless, the nontriviality of numerical implementation and computational intensity of these reconstruction algorithms has thus far precluded their widespread use in clinical practice. In this work, we propose a novel MRI reconstruction framework based on homotopy continuation of the L 0 semi-norm using redescending M-estimator functions. Following analysis of the continuation scheme, the sparsity measure is extended to multiscale form and a simple numerical solver that can achieve accurate reconstructions in a matter of seconds on a standard desktop computer is presented.",

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