Title :
A new framework for sparse regularization in limited angle x-ray tomography
Author_Institution :
Image Diagnost Int. GmbH, München, Germany
Abstract :
We propose a new framework for limited angle tomographic reconstruction. Our approach is based on the observation that for a given acquisition geometry only a few (visible) structures of the object can be reconstructed reliably using a limited angle data set. By formulating this problem in the curvelet domain, we can characterize those curvelet coefficients which correspond to visible structures in the image domain. The integration of this information into the formulation of the reconstruction problem leads to a considerable dimensionality reduction and yields a speedup of the corresponding reconstruction algorithms.
Keywords :
computerised tomography; curvelet transforms; image reconstruction; medical image processing; sparse matrices; curvelet coefficients; dimensionality reduction; limited angle X-ray tomography; sparse regularization; tomographic reconstruction; Attenuation measurement; Biomedical imaging; Breast; Error correction; Fourier transforms; Geometry; Image reconstruction; Reconstruction algorithms; TV; X-ray tomography; Limited angle tomography; curvelets; dimensionality reduction; sparse regularization; wavefront set;
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on
Conference_Location :
Rotterdam
Print_ISBN :
978-1-4244-4125-9
Electronic_ISBN :
1945-7928
DOI :
10.1109/ISBI.2010.5490113