Medical image reconstruction techniques - Reader, Andrew (Henry Stewart Talks, 2010)
Overview of PET and the image reconstruction problem, Measured data: list-mode data, sinograms and backprojected images, from point sources to more complex objects, Line integral and convolution models, and the backproject then filter (BPF) algorithm, 2D and 3D PET data, sinograms and rebinning methods (SSRB, MSRB and FOR), Object representation and the system model/matrix (creating the system matrix), Incorporating object motion, resolution, time-of-flight, attenuation and normalization in the system model, Objective functions: least squares and maximum likelihood (ML), regularization, Deriving a maximum likelihood reconstruction algorithm: Expectation Maximization ML (EM-ML) reconstruction, Ordered subsets EM (OSEM) and ordinary Poisson OSEM, Fourier reconstruction and filtered backprojection (FBP), for PET and CT data, 3D FBP and the reprojection method (3D RP), Example FBP and ML-EM reconstructions, post-smoothing
Reviewed at 2020/06/15