Novel Method to Estimate Kinetic Microparameters from Dynamic Whole-Body Imaging in Regular-Axial Field-of-View PET Scanners
Abstract
Purpose: For Whole-Body (WB) kinetic modeling based on a typical PET scanner, a multi-pass multi-bed scanning protocol is necessary given the limited axial field-of-view. Such a protocol introduces loss of early-dynamics in Time-Activity Curves (TACs) and sparsity in TAC measurements, inducing uncertainty in parameter estimation when using Least-Squares Estimation (LSE) (i.e., common standard), especially for kinetic microparameters. We present a method to reliably estimate microparameters, enabling accurate parametric imaging, on regular-axial field-of-view PET scanners
Materials and Methods: Our method, denoted Parameter Combination-Driven Estimation (PCDE), relies on the generation of reference truth TAC database, and subsequently selected, the best parameter combination as the one arriving at TAC with the highest Total Similarity Score (TSS), focusing on the general image quality, overall visibility, and tumor detectability metrics. Our technique has two distinctive characteristics: 1) improved probability of having one-on-one mapping between early and late dynamics in TACs (the former missing from typical protocols), and 2) use of multiple aspects of TACs in the selection of best fits. To evaluate our method against conventional LSE, we plotted trade-off curves for noise and bias. In addition, the overall Signal-to-Noise Ratio (SNR) and spatial noise were calculated and compared. Furthermore, the Contrast-to-Noise Ratio (CNR) and Tumor-to-Background Ratio (TBR) were also calculated. We also tested our proposed method on patient data (18F-DCFPyL PSMA PET/CT scans) to further verify clinical applicability.
Results: Significantly improved general image quality performance was verified in microparametric images (e.g. noise-bias trade-off performance). The overall visibility and tumor detectability were also improved. Finally, for our patient studies, improved overall visibility and tumor detectability were demonstrated in mico parametric images, compared to the use of conventional parameter estimation.
Conclusion: The proposed method provides improved microkinetic parametric images compared to the common standard in terms of general image quality, overall visibility, and tumor detectability.
