Motion correction of PET for improved image quality

Abstract

Advances in PET technology have led to improved resolution capability in modern PET scanners, andhence the impact of motion has become increasingly significant in order to produce high resolutionPET images. This thesis aims to advance the utility of motion correction in combined PET and MR byimproving the capabilities of motion correction, and by easing the effort required in order to applythem. In particular, PET-based data-driven motion tracking methods, which are able to track motionwithout requiring additional hardware, are investigated and applied to the head. First, existing techniques for motion correction, especially in the context of PET/MR, are reviewedand a general framework for PET motion correction is presented. The framework provides a top-levelperspective on PET motion correction, simplifying the process of comparing and contrasting variousmotion correction techniques. Next, the implementation of a patient-based orientation system withinthe open-source PET reconstruction packages the Software for Tomographic Image Reconstruction(STIR) and the Synergistic Image Reconstruction Software (SIRF) is described. Additionally, animplementation of the general PET motion correction framework platform is introduced. To investigate current technological limitations in combined PET and MR motion correction, theefficacy of a commercial PET/MR motion correction product, Brain COMPASS (herein just COMPASSfor brevity), was assessed. An experiment introduced a methodology for measuring motion blur invivo and uses this to measure reduction in motion blur after applying COMPASS. N=10 subjects worea head-mounted apparatus with fiducial markers affixed. COMPASS reduced blur in the directionof motion, measured by the principal component of the full-width at half-maximum (FWHM) ofthe source point-spread-function (PSF) distribution, only when the motion was sufficient to causethat blur to exceed 1.9 mm. However, blur across all directions almost always increased (mean=0.3mm) when COMPASS was used. Results suggested that any error in estimating the patient posewas low and was not the cause of the blur. Increases in overall blur were therefore suspected to becaused by interpolation when resampling transformed reconstructions. A second experiment measuredneocortical standardised uptake value ratio (SUVr) in an 18F-Florbetaben (18F-FBB) study of N=42young, healthy, asymptomatic volunteers with high polygenetic risk of developing Alzheimer’s Disease.No significant changes in neocortical SUVr were observed. This cohort exhibited low motion, andthe neocortex is a large region-of-interest, so is less sensitive to motion corruption than other cohorts.Hence, it was concluded that COMPASS did not aid this investigation, but also does not have adiscernible negative impact despite low motion. During the COMPASS investigation, MR acquisitions were the time-limiting factor of the session,meaning that the MR acquisitions introduced for PET motion correction increased the overall sessiontime. Because MR is not always available for motion tracking, alternative PET-alone motion correctiontechniques were investigated. Several data-driven tracking techniques are available; however, it is notclear which are most appropriate. A phantom investigation was conducted on a Biograph mCT Flowto determine which data-driven head motion tracking techniques were most appropriate. Of the fourmethodologies investigated principal component analysis (PCA) was found to be the most promisingtechnique for PET data-driven motion tracking of head motion. The PET-alone motion correction technique outlined above had two key limitations. Firstly, theyprovide frame-based motion correction with low temporal resolution. However, the type of motionseen in the COMPASS investigation were continuous “drooping” of the head, is not modelled in adiscrete, frame-based scheme. Secondly, the PET reconstructions without attenuation provide noisypose estimates compared to those from MR volume registration. Therefore, a scheme was proposedthat, unlike previous techniques, models head motion directly from motion surrogates. Here, a radialbasis function (RBF) regression relates the endogenous tracking signal to the exogenous rigid poseparameters, which are supplied by temporal-sparse MR acquisitions. A proof-of-concept is presentedin which low temporal resolution MR pose estimates are upsampled. The technique is compared tolinear and nearest-neighbour interpolation, and for the regression, both linear and RBF are investigated.The proposed technique was found to introduce some negligible blurring when no motion is present (<0.1 mm mean-of-maximum displacement (MMD)) but reduce blurring by 0.7 mm MMD when motionis present. This demonstrates the capability of the technique to reduce error in motion estimation byimproving the temporal resolution. In a clinical setting, motion correction is likely to become increasingly important in the future. Thework in this thesis will make motion correction easier to apply in a clinical setting, so that it may beapplied more regularly, providing wider benefit.