Theoretical Analysis of Penalized Maximum-Likelihood Patlak Parametric Image Reconstruction in Dynamic PET for Lesion Detection

Abstract

Detecting cancerous lesions is a major clinical application of emission tomography. In a previous work, we studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by first reconstructing a sequence of dynamic PET images, and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in Patlak parametric images. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize detection performance. The proposed method is validated using computer-based Monte Carlo simulations. Good agreements between the theoretical predictions and the Monte Carlo results are observed. Both theoretical predictions and Monte Carlo simulation results show the benefit of the indirect and direct methods under optimized regularization parameters in dynamic PET reconstruction for lesion detection, when compared with the conventional static PET reconstruction.

Total-Body Dynamic Reconstruction and Parametric Imaging on the uEXPLORER

“…It is easy to implement but usually results in high noise in the parametric images because the noise distribution in reconstructed images is difficult to model (13). Penalized maximum-likelihood image reconstruction methods can be used to improve the indirect parametric images (14). Regularization using kernelbased methods has also been developed to enhance the dynamic PET reconstruction by incorporating anatomic or dynamic features in the reconstruction through a kernel matrix (15)(16)(17).…”

Direct Patlak Reconstruction From Dynamic PET Data Using the Kernel Method With MRI Information Based on Structural Similarity

“…Here we combine the above two approaches to further improve the quality of dynamic PET reconstruction. Compared with static PET, dynamic PET provides additional temporal information of the tracer kinetics, which can be useful for tumor detection and treatment monitoring [29]–[31]. Conventionally dynamic PET images are reconstructed frame by frame, and time activity curves (TAC) from regions of interest (ROI) or individual pixels are fitted to a linear or nonlinear kinetic model.…”

Relative Patlak plot for dynamic PET parametric imaging without the need for early-time input function

“…However, there are many applications that do not require absolute quantification but utilize the contrast information in the parametric image of K i . Examples include, but are not limited to, lesion detection (e.g., [Li et al, 2009, Yang et al, 2016]), and metabolic tumor volume segmentation (e.g., [Visser et al, 2008]) using parametric map of tracer influx rate. The target-to-background contrast is often higher in the parametric images than in the SUV images, implying that parametric imaging can offer higher lesion detectability and better boundary differentiation (Fig.…”

“…Examples include, but are not limited to, lesion detection (e.g. Li et al (2009) and Yang et al (2016)), and metabolic tumor volume segmentation (e.g. Visser et al (2008)) using parametric map of tracer influx rate.…”