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 | IEEE Workshop on Statistical Signal Processing Proceedings |
| Pages | 176-180 |
| Number of pages | 5 |
| DOIs | |
| State | Published - 2007 |
| Event | 2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007 - Madison, WI, United States Duration: Aug 26 2007 → Aug 29 2007 |
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 |
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Keywords
- Homotopy
- L- minimization
- Magnetic resonance imaging
- Sparse reconstruction
ASJC Scopus subject areas
- Signal Processing
Cite this
Sparse MRI reconstruction via multiscale L0-continuation. / Trazasko, Joshua D; Manduca, Armando; Borisch, Eric.
IEEE Workshop on Statistical Signal Processing Proceedings. 2007. p. 176-180 4301242.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Sparse MRI reconstruction via multiscale L0-continuation
AU - Trazasko, Joshua D
AU - Manduca, Armando
AU - Borisch, Eric
PY - 2007
Y1 - 2007
N2 - "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.
AB - "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.
KW - Homotopy
KW - L- minimization
KW - Magnetic resonance imaging
KW - Sparse reconstruction
UR - http://www.scopus.com/inward/record.url?scp=47849110073&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=47849110073&partnerID=8YFLogxK
U2 - 10.1109/SSP.2007.4301242
DO - 10.1109/SSP.2007.4301242
M3 - Conference contribution
SN - 142441198X
SN - 9781424411986
SP - 176
EP - 180
BT - IEEE Workshop on Statistical Signal Processing Proceedings
ER -