Motion correction of PET for improved image quality

Advances in PET technology have led to improved resolution capability in modern PET scanners, and hence the impact of motion has become increasingly significant in order to produce high resolution PET images. This thesis aims to advance the utility of motion correction in combined PET and MR by improving the capabilities of motion correction, and by easing the effort required in order to apply them. In particular, PET-based data-driven motion tracking methods, which are able to track motion without requiring additional hardware, are investigated and applied to the head. First, existing techniques for motion correction, especially in the context of PET/MR, are reviewed and a general framework for PET motion correction is presented. The framework provides a top-level perspective on PET motion correction, simplifying the process of comparing and contrasting various motion correction techniques. Next, the implementation of a patient-based orientation system within the open-source PET reconstruction packages the Software for Tomographic Image Reconstruction (STIR) and the Synergistic Image Reconstruction Software (SIRF) is described. Additionally, an implementation of the general PET motion correction framework platform is introduced. To investigate current technological limitations in combined PET and MR motion correction, the efficacy of a commercial PET/MR motion correction product, Brain COMPASS (herein just COMPASS for brevity), was assessed. An experiment introduced a methodology for measuring motion blur in vivo and uses this to measure reduction in motion blur after applying COMPASS. N=10 subjects wore a head-mounted apparatus with fiducial markers affixed. COMPASS reduced blur in the direction of motion, measured by the principal component of the full-width at half-maximum (FWHM) of the source point-spread-function (PSF) distribution, only when the motion was sufficient to cause that blur to exceed 1.9 mm. However, blur across all directions almost always increased (mean=0.3 mm) when COMPASS was used. Results suggested that any error in estimating the patient pose was low and was not the cause of the blur. Increases in overall blur were therefore suspected to be caused by interpolation when resampling transformed reconstructions. A second experiment measured neocortical standardised uptake value ratio (SUVr) in an 18F-Florbetaben (18F-FBB) study of N=42 young, 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, and the 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 a discernible 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 session time. Because MR is not always available for motion tracking, alternative PET-alone motion correction techniques were investigated. Several data-driven tracking techniques are available; however, it is not clear which are most appropriate. A phantom investigation was conducted on a Biograph mCT Flow to determine which data-driven head motion tracking techniques were most appropriate. Of the four methodologies investigated principal component analysis (PCA) was found to be the most promising technique for PET data-driven motion tracking of head motion. The PET-alone motion correction technique outlined above had two key limitations. Firstly, they provide frame-based motion correction with low temporal resolution. However, the type of motion seen in the COMPASS investigation were continuous “drooping” of the head, is not modelled in a discrete, frame-based scheme. Secondly, the PET reconstructions without attenuation provide noisy pose estimates compared to those from MR volume registration. Therefore, a scheme was proposed that, unlike previous techniques, models head motion directly from motion surrogates. Here, a radial basis function (RBF) regression relates the endogenous tracking signal to the exogenous rigid pose parameters, which are supplied by temporal-sparse MR acquisitions. A proof-of-concept is presented in which low temporal resolution MR pose estimates are upsampled. The technique is compared to linear 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 motion is present. This demonstrates the capability of the technique to reduce error in motion estimation by improving the temporal resolution. In a clinical setting, motion correction is likely to become increasingly important in the future. The work in this thesis will make motion correction easier to apply in a clinical setting, so that it may be applied more regularly, providing wider benefit. Declaration by author This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis. I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, financial support and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my higher degree by research candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award. I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School. I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis and have sought permission from co-authors for any jointly authored works included in the thesis. Publications included in this thesis [1] Ashley Gillman, Jye Smith, Paul Thomas, Stephen Rose, and Nicholas Dowson. “PET motion correction in context of integrated PET/MR: Current techniques, limitations, and future projections”. In: Medical Physics 44.12 (2017), e430–e445 Submitted manuscripts included in this thesis [2] Ashley Gillman, Stephen Rose, Jurgen Fripp, Pierrick Bourgeat, Parnesh Raniga, David Faul, Jye Smith, Jason A. Dowling, Paul Thomas, and Nicholas Dowson. “The efficacy and effectiveness in a neurological florbetaben study of Siemens’ Brain COMPASS on the Biograph mMR”. in: Medical Physics (submitted) (2019) [3] Ashley Gillman, Stephen Rose, Jye Smith, Jason A. Dowling, and Nicholas Dowson. “A comparison of techniques for PET data-driven motion tracking of head motion”. In: Medical Physics (submitted) (2019) Other publications during candidature Conference abstracts [4] Lee B. Reid, Ashley Gillman, Alex M. Pagnozzi, José V. Manjón, and Jurgen Fripp. “MRI denoising and artefact removal using self-organizing maps for fast global block-matching”. In: Patch-Based Techniques in Medical Imaging. Ed. by Wenjia Bai, Gerard Sanroma, Guorong Wu, Brent C. Munsell, Yiqiang Zhan, and Pierrick Coupé. Lecture Notes in Computer Science. Springer International Publishing, 2018, pp. 20–27 [5] Richard Brown, Sam Ellis, Edoardo Pasca, Evguenni Ovtchinnikov, Ashley Gillman, Camila Munoz, James Bland, Abolfazl Mehranian, Claudia Prieto, Andrew J. Reader, and Kris Thielemans. “SIRF: A research tool for rapid prototyping of PET-MR reconstruction”. In: PSMR Conference on PET/MR and SPECT/MR. PSMR Conference on PET/MR and SPECT/MR. Munich, Germany, Apr. 15, 2019 Presentations at international conferences