Coupled Motion and Activity Estimation from PET and MR Data with Motion Model-Based Parameter Reduction

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

We propose a technique that estimates both the image
and the respiratory motion states of all PET gates during PET reconstruction. This is done using a gradient analytically-derived from the Poisson log-likelihood, and involves a parameterised motion model. This is incorporated into reconstruction using the motion compensated image reconstruction (MCIR) framework, coupled with optimisation of the analytical gradient. The use of the motion model in this way reduces the number of additional parameters to be optimised for motion correction to one per PET gate. This was tested using a 3D PET simulation derived from segmented UTE MR image volumes, warped using real MR-derived motion
measurements.
Original languageEnglish
Title of host publication Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2015 IEEE
PublisherIEEE
DOIs
Publication statusPublished - 6 Oct 2016

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