Fast algorithms for nonconvex compression sensing: MRI reconstruction from very few data
Conference
·
OSTI ID:956525
- Los Alamos National Laboratory
Compressive sensing is the reconstruction of sparse images or signals from very few samples, by means of solving a tractable optimization problem. In the context of MRI, this can allow reconstruction from many fewer k-space samples, thereby reducing scanning time. Previous work has shown that nonconvex optimization reduces still further the number of samples required for reconstruction, while still being tractable. In this work, we extend recent Fourier-based algorithms for convex optimization to the nonconvex setting, and obtain methods that combine the reconstruction abilities of previous nonconvex approaches with the computational speed of state-of-the-art convex methods.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 956525
- Report Number(s):
- LA-UR-09-00316; LA-UR-09-316; TRN: US201014%%1894
- Resource Relation:
- Conference: International Symposium on Biomedical Imaging ; June 28, 2009 ; Boston, MA
- Country of Publication:
- United States
- Language:
- English
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