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PET reconstruction of the posterior image probability, including multimodal images


Scalable and Secure Big Data I

A Wavelet-Predominant Algorith

3D Reconstruction in Canonical
Abstract


In PET image reconstruction, it would be useful to obtain the entire posterior probability distribution of the image, because it allows for both estimating image intensity and assessing the uncertainty of the estimation, thus leading to more reliable interpretation. We propose a new entirely probabilistic model: the prior is a distribution over possible smooth regions (distance-driven Chinese restaurant process), and the posterior distribution is estimated using a Gibbs MCMC sampler. Data from other modalities (here one or several MR images) are introduced into the model as additional observed data, providing side information about likely smooth regions in the image. The reconstructed image is the posterior mean, and the uncertainty is presented as an image of the size of 95% posterior intervals. The reconstruction was compared to MLEM and OSEM algorithms, with and without post-smoothing, and to a penalized ML or MAP method that also uses additional images from other modalities. Qualitative and quantitative tests were performed on realistic simulated data with statistical replicates and on several clinical examinations presenting pathologies. The proposed method presents appealing properties in terms of obtained bias, variance, spatial regularization, and use of multimodal data, and produces in addition potentially valuable uncertainty information.

KeyWords
PET, image reconstruction, posterior probability distribution, uncertainty, multimodal, MCMC sampler, Bayesian inference, spatial regularization.



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