title: Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors.
abstract: Positron emission tomography (PET) reconstruction is an ill-posed inverse problem which typically involves fitting a high-dimensional forward model of the imaging process to noisy, and sometimes undersampled photon emission data. To improve the image quality, prior information derived from anatomical images of the same subject has been previously used in the penalised maximum likelihood (PML) method to regularise the model complexity and selectively smooth the image on a voxel basis in PET reconstruction. In this work, we propose a novel perspective of incorporating the prior information by exploring the sparse property of natural images. Instead of a regular voxel grid, the sparse image representation jointly determined by the prior image and the PET data is used in reconstruction to leverage between the image details and smoothness, and this prior is integrated into the PET forward model and has a closed-form expectation maximisation (EM) solution. Simulations show that the proposed approach achieves improved bias versus variance trade-off and higher contrast recovery than the current state-of-the-art methods, and preserves the image details better. Application to clinical PET data shows promising results.
authors.forename: Jieqing
authors.initials: J
authors.lastname: Jiao
authors.lastname: Pawel-Markiewicz
authors.forename: Ninon
authors.initials: N
authors.lastname: Burgos
authors.forename: David
authors.initials: D
authors.lastname: Atkinson
authors.forename: Brian
authors.initials: B
authors.lastname: Hutton
authors.forename: Simon
authors.initials: S
authors.lastname: Arridge
authors.forename: Sebastien
authors.initials: S
authors.lastname: Ourselin
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016013:Likelihood Functions; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique