Improved statistical quantitation of dynamic PET scans
University College Cork
Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in the management of cancer patients and in medical research. It plays a key role in a variety of tasks related to diagnosis, therapy planning, prognosis and treatment monitoring by injecting a radiotracer to characterize the specific biologic process (e.g., tumor metabolism, proliferation or blood flow). The standardized uptake value (SUV) obtained at a single time point is widely employed in clinical practice. However, well beyond this simple static uptake measure, more detailed metabolic information may be recovered from dynamic PET scanning with multiple time frames. Assuming a tracer’s interaction with the tissue is linear and time-invariant, the tissue time course can be expressed as a convolution between arterial input function (AIF) and the tissue impulse response/residue function. Kinetic analysis is concerned with the estimation of residue and associated physiological parameters such as flow, flux and volume of distribution. Some traditional methods including Patlak and compartmental modeling are well-established with a given form of residue function (constant or mixture of exponentials), but they are not flexible to represent data in which in-vivo biochemistry is not clear, especially for the whole-body imaging on the long axial field of view (LAFOV) PET systems. The main goals of this thesis are to develop novel statistical approaches for improving and evaluating parametric imaging extracted from dynamic PET scans. The non-parametric residue mapping (NPRM) procedure has been constructed by a fully automatic process incorporating data-adaptive segmentation, non-parametric residue analysis of segment data and voxel-level kinetic mapping scheme. Based on this approach, the benefits of pooling data in multiple injection PET scans are investigated. Spatial and temporal patterns of residuals recovered by model diagnostics exhibit a non-Gaussian structure, which defines a bootstrap data generation process (DGP) in the image domain. The proposed bootstrap method has been used to assess the uncertainty (standard errors) in kinetic information and more complex regional summaries. We also examine its potential to improve the mean square error (MSE) characteristics of kinetic maps generated from either compartmental modeling or NPRM approach by averaging results from individual bootstrap samples. Dynamic breast cancer studies on the early, recent and latest LAFOV PET scanners are presented to illustrate these techniques. The performance of above models and schemes has been evaluated in a series of one and two-dimensional numerical simulations. Both direct filtered backprojection (FBP) and iterative maximum likelihood (ML) reconstructions are considered. The proposed NPRM approach has some important features like the flexibility for diverse tissue environments and consideration of delays for different parts, which make it promising to be applied to the emerging total-body PET imaging. The developed image-domain bootstrap provides a practical way to quantify the uncertainties of biomarkers. This mechanism has the potential to further support clinical decision-making and enhance personalized medicine.
Dynamic PET , Non-parametric residue mapping , Multiple-injection scans , Total-body PET quantitation , Image-domain bootstrapping , Assessment of uncertainty in biomarkers
Gu, F. 2023. Improved statistical quantitation of dynamic PET scans. PhD Thesis, University College Cork.