Fully 3D PET image reconstruction using a fourier preconditioned conjugate-gradient algorithm
- Univ. of Michigan, Ann Arbor, MI (United States)
Since the data sizes in fully 3D PET imaging are very large, iterative image reconstruction algorithms must converge in very few iterations to be useful. One can improve the convergence rate of the conjugate-gradient (CG) algorithm by incorporating preconditioning operators that approximate the inverse of the Hessian of the objective function. If the 3D cylindrical PET geometry were not truncated at the ends, then the Hessian of the penalized least-squares objective function would be approximately shift-invariant, i.e. G{prime}G would be nearly block-circulant, where G is the system matrix. We propose a Fourier preconditioner based on this shift-invariant approximation to the Hessian. Results show that this preconditioner significantly accelerates the convergence of the CG algorithm with only a small increase in computation.
- OSTI ID:
- 513259
- Report Number(s):
- CONF-961123-; CNN: Grant CA-60711; Grant CA-54362; TRN: 97:014312
- Resource Relation:
- Conference: Institute of Electrical and Electronic Engineers (IEEE) nuclear science symposium and medical imaging conference, Anaheim, CA (United States), 2-9 Nov 1996; Other Information: PBD: 1996; Related Information: Is Part Of 1996 IEEE nuclear science symposium - conference record. Volumes 1, 2 and 3; Del Guerra, A. [ed.]; PB: 2138 p.
- Country of Publication:
- United States
- Language:
- English
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