Effect of Varying Number of OSEM Subsets on PET Lesion Detectability - Morey, A. Michael (2013)
Iterative reconstruction has become the standard for routine clinical positron emission tomography (PET) imaging. However, iterative reconstruction is computationally expensive, especially for time-of-flight (TOF) data. Block-iterative algorithms such as ordered-subsets expectation-maximization (OSEM) are commonly used to accelerate the reconstruction. There is a tradeoff between the number of subsets and reconstructed image quality. The objective of this work was to evaluate the effect of varying the number of OSEM subsets upon lesion-detection for general oncologic PET imaging. METHODS: Experimental phantom data were taken from the Utah PET Lesion Detection Database resource, modeling whole-body oncologic PET imaging of a 92 Kg patient with [18]F-fluorodeoxyglucose. The experiment consisted of 24 scans over 4 days on a TOF PET/CT scanner, with up to 23 lesions (diameter 6–16mm) distributed throughout the thorax, abdomen, and pelvis. Images were reconstructed with maximum-likelihood expectation-maximization (MLEM) and with OSEM using 2–84 subsets. The reconstructions were repeated both with and without TOF. Localization receiver operating characteristics (LROC) analysis was applied using the channelized non-prewhitened observer. The observer was first used to optimize the number of iterations and smoothing filter for each case that maximized lesion-detection performance for these data; this was done to ensure that fair comparisons were made with each test case operating near its optimal performance. The probability of correct localization (P(LOC)) and the area under the LROC curve (A(LROC)) were then analyzed as functions of the number of subsets to characterize the effect of OSEM on lesion-detection performance. RESULTS: Compared to the baseline MLEM algorithm, lesion-detection performance with OSEM declined as the number of subsets increased. The decline was moderate out to about 12–14 subsets, and then became progressively steeper as the number of subsets increased. Comparing TOF with non-TOF results, the magnitude of the performance drop was larger for TOF reconstructions. CONCLUSION: PET lesion-detection performance is degraded when using OSEM with a large number of subsets. This loss of image quality can be controlled by using a moderate number of subsets (e.g. 12–14 or fewer), retaining a large degree of acceleration while maintaining high image quality. The use of more aggressive subsetting can result in image quality degradations that offset the benefits of using TOF or longer scan times.