Evaluation of bias and variance in low-count OSEM list mode reconstruction

Abstract

Statistical algorithms have been widely used in PET image reconstruction. The maximum likelihood expectation maximization (MLEM) reconstruction has been shown to produce bias in applications where images are reconstructed from a relatively small number of counts. In this study, image bias and variability in low-count OSEM reconstruction are investigated on images reconstructed with MOLAR (motion-compensation OSEM list-mode algorithm for resolution-recovery reconstruction) platform. A human brain ([11C]AFM) and a NEMA phantom are used in the simulation and real experiments respectively, for the HRRT and Biograph mCT. Image reconstructions were repeated with different combination of subsets and iterations. Regions of interest (ROIs) were defined on low-activity and high-activity regions to evaluate the bias and noise at matched effective iteration numbers (iterations x subsets). Minimal negative biases and no positive biases were found at moderate count levels and less than 5% negative bias was found using extremely low levels of counts (0.2 M NEC). At any given count level, other factors, such as subset numbers and frame-based scatter correction may introduce small biases (1–5%) in the reconstructed images. The observed bias was substantially lower than that reported in the literature, perhaps due to the use of point spread function and/or other implementation methods in MOLAR.

Investigating the limits of PET/CT imaging at very low true count rates and high random fractions in ion‐beam therapy monitoring

“…As a consequence, scatter is overestimated and scatter corrected activity negatively biased, as hypothesized also by other authors. 30,31 Inclusion of TOF and PSF information makes the reconstruction less sensitive to inaccurate scatter estimates 32 and thereby results in an improved quantification under high RFs and small true numbers. Further reconstruction attempts including a scatter correction without scaling of the simulated scatter estimate to the measured emission data and/or with an increased number of iterations of the underlying single scatter simulation have been investigated, but could not improve quantification by the OSEM algorithm in the late measurement frames.…”

Quantitative Accuracy and Lesion Detectability of Low-Dose 18F-FDG PET for Lung Cancer Screening

“…Many previous works have focused on the behavior of statistical PET reconstructions in low-count conditions (18,(20)(21)(22)(23)(24)(25) and typically characterized image quality in terms of noise and bias. However, more work is needed to translate the implications of these findings for specific clinical tasks.…”

Cardiac‐gated parametric images from 82Rb PET from dynamic frames and direct 4D reconstruction

“…Previous work has demonstrated that MOLAR can produce images with minimal bias, even at very low counts. 38 While the kinetic parameters estimated by the indirect method were not free from bias, the CoV versus bias curves of the parameters estimated by the indirect method were largely overlapping for the two count levels assessed in this work, suggesting that bias in kinetic parameters is not strictly related to the number of counts in a frame. However, fewer subsets were used than is standard practice (12 vs. 21), and the counts per subset may be the more important determining factor in low-count bias.…”