Iterative PET Image Reconstruction Using Translation Invariant Wavelet Transform.
Abstract
The present work describes a Bayesian maximum a posteriori (MAP) method using a statistical multiscale wavelet prior model. Rather than using the orthogonal discrete wavelet transform (DWT), this prior is built on the translation invariant wavelet transform (TIWT). The statistical modeling of wavelet coefficients relies on the generalized Gaussian distribution. Image reconstruction is performed in spatial domain with a fast block sequential iteration algorithm. We study theoretically the TIWT MAP method by analyzing the Hessian of the prior function to provide some insights on noise and resolution properties of image reconstruction. We adapt the key concept of local shift invariance and explore how the TIWT MAP algorithm behaves with different scales. It is also shown that larger support wavelet filters do not offer better performance in contrast recovery studies. These theoretical developments are confirmed through simulation studies. The results show that the proposed method is more attractive than other MAP methods using either the conventional Gibbs prior or the DWT-based wavelet prior.
Domains
Life Sciences [q-bio] Bioengineering Engineering Sciences [physics] Signal and Image processing Life Sciences [q-bio] Bioengineering Imaging Life Sciences [q-bio] Bioengineering Nuclear medicine Physics [physics] Physics [physics] Medical Physics [physics.med-ph] Computer Science [cs] Signal and Image Processing
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