Point-spread function (PSF) modeling offers the ability to account for resolution degrading phenomena within the PET image generation framework. PSF modeling improves resolution and enhances contrast, but at the same time significantly alters image noise properties and induces edge overshoot effect. Thus, studying the effect of PSF modeling on quantitation task performance can be very important. Frameworks explored in the past involved a dichotomy of PSF versus no-PSF modeling. By contrast, the present work focuses on quantitative performance evaluation of standard uptake value (SUV) PET images, while incorporating a wide spectrum of PSF models, including those that under- and over-estimate the true PSF, for the potential of enhanced quantitation of SUVs. The developed framework first analytically models the true PSF, considering a range of resolution degradation phenomena (including photon non-collinearity, inter-crystal penetration and scattering) as present in data acquisitions with modern commercial PET systems. In the context of oncologic liver FDG PET imaging, we generated 200 noisy datasets per image-set (with clinically realistic noise levels) using an XCAT anthropomorphic phantom with liver tumours of varying sizes. These were subsequently reconstructed using the OS-EM algorithm with varying PSF modelled kernels. We focused on quantitation of both SUV and SUV, including assessment of contrast recovery coefficients, as well as noise-bias characteristics (including both image roughness and coefficient of-variability), for different tumours/iterations/PSF kernels. It was observed that overestimated PSF yielded more accurate contrast recovery for a range of tumours, and typically improved quantitative performance. For a clinically reasonable number of iterations, edge enhancement due to PSF modeling (especially due to over-estimated PSF) was in fact seen to lower SUV bias in small tumours. Overall, the results indicate that exactly matched PSF modeling does not offer optimized PET quantitation, and that PSF overestimation may provide enhanced SUV quantitation. Furthermore, generalized PSF modeling may provide a valuable approach for quantitative tasks such as treatment-response assessment and prognostication.
Background Currently, there is no established non-invasive imaging approach to directly evaluate myocardial microcirculatory function in order to diagnose microvascular disease independent of co-existing epicardial disease. In this work, we developed a methodological framework for quantification of intramyocardial blood volume (IMBV) as a novel index of microcirculatory function with SPECT/CT imaging of 99mTc-labeled red blood cells (RBCs). Methods Dual-gated myocardial SPECT/CT equilibrium imaging of 99mTc-RBCs was performed on twelve canines under resting conditions. Five correction schemes were studied: cardiac gating with no other corrections (CG), CG with attenuation correction (CG 1 AC), CG 1 AC with scatter correction (CG 1 AC 1 SC), dual cardiorespiratory gating with AC 1 SC (DG 1 AC 1 SC), and DG 1 AC 1 SC with partial volume correction (DG 1 AC 1 SC 1 PVC). Quantification of IMBV using each approach was evaluated in comparison to those obtained from all corrections. The in vivo SPECT estimates of IMBV values were validated against those obtained from ex vivo microCT imaging of the casted hearts. Results The estimated IMBV with all corrections was 0.15 ± 0.03 for the end-diastolic phase and 0.11 ± 0.03 for the end-systolic phase. The cycle-dependent change in IMBV (ΔIMBV) with all corrections was 23.9 ± 8.6%. Schemes that applied no correction or partial correction resulted in significant over-estimation of IMBV and significant under-underestimation of ΔIMBV. Estimates of IMBV and ΔIMBV using all corrections were consistent with values reported in the literature using invasive techniques. In vivo SPECT estimates of IMBV strongly correlated (R2 ≥ 0.70) with ex vivo measures for the various correction schemes, while the fully corrected scheme yielded the smallest bias. Conclusions Non-invasive quantification of IMBV is feasible using 99mTc-RBCs SPECT/CT imaging, however, requires full compensation of physical degradation factors.
Quantitative myocardial perfusion (MP) PET has the potential to enhance detection of early stages of atherosclerosis or microvascular dysfunction, characterization of flow-limiting effects of coronary artery disease (CAD), and identification of balanced reduction of flow due to multivessel stenosis. We aim to enable quantitative MP-PET at the individual voxel level, which has the potential to allow enhanced visualization and quantification of myocardial blood flow (MBF) and flow reserve (MFR) as computed from uptake parametric images. This framework is especially challenging for the (82)Rb radiotracer. The short half-life enables fast serial imaging and high patient throughput; yet, the acquired dynamic PET images suffer from high noise-levels introducing large variability in uptake parametric images and, therefore, in the estimates of MBF and MFR. Robust estimation requires substantial post-smoothing of noisy data, degrading valuable functional information of physiological and pathological importance. We present a feasible and robust approach to generate parametric images at the voxel-level that substantially reduces noise without significant loss of spatial resolution. The proposed methodology, denoted physiological clustering, makes use of the functional similarity of voxels to penalize deviation of voxel kinetics from physiological partners. The results were validated using extensive simulations (with transmural and non-transmural perfusion defects) and clinical studies. Compared to post-smoothing, physiological clustering depicted enhanced quantitative noise versus bias performance as well as superior recovery of perfusion defects (as quantified by CNR) with minimal increase in bias. Overall, parametric images obtained from the proposed methodology were robust in the presence of high-noise levels as manifested in the voxel time-activity-curves.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.