(2018) Spatially-variant Strength for Anatomical Priors in PET Reconstruction. In: Proceedings of the 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE: New York, USA.

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Abstract

This study explores the use of a spatially-variant penalty strength, proposed initially for quadratic penalties, in penalized image reconstruction using anatomical information. We have used the recently proposed Parallel Level Sets (PLS) anatomical prior as it has shown promising results in the literature. It was incorporated into the previously proposed preconditioned algorithm (L-BFGS-B-PC) for achieving both good image quality and fast convergence rate. A 2-dimensional (2D) disc phantom with a hot spot at the center and a 3D XCAT thorax phantom with lesions inserted in different slices are used to study how surrounding activity and lesion location affect both the visual appearance and quantitative consistency, respectively. Anatomical information is provided and assumed to be well-aligned with the corresponding activity images. For the XCAT phantom, the inserted lesions are either present or absent in the anatomical images to investigate the influence of the anatomical penalty. The reconstructed images for both phantoms with and without applying the spatially-variant penalty strength are compared. Preliminary results demonstrate that applying the spatially-variant penalization with an anatomical prior can reduce the dependence of local contrast on background activity and lesion location. Further work to explore the potential benefit in clinical imaging is warranted.

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