Experimental comparison of lesion detectability for four fully-3D PET reconstruction schemes

The objective of this work was to evaluate the lesion detectability performance of four fully-3D PET reconstruction schemes using experimentally acquired data. A multicompartment anthropomorphic phantom was setup to mimic whole-body FDG cancer imaging and scanned twelve times in 3D mode. Eight of the scans had twenty-six 68Ge "shell-less" lesions (6, 8, 10, 12, 16 mm diam.) placed throughout the phantom with various target:background ratios. This provided lesion-present and lesion-absent datasets with known truth appropriate for evaluating lesion detectability by localization receiver operating characteristics (LROC) methods. Four reconstruction schemes were studied: (1) Fourier rebinning (FORE) followed by 2D attenuation-weighted ordered-subsets expectation-maximization; (2) fully-3D AW-OSEM; (3) fully-3D ordinary-Poisson line-of- response (LOR-)OSEM; and (4) fully-3D LOR-OSEM with an accurate point-spread function (PSF) model. Two forms of LROC analysis were performed. First, a channelized non-prewhitened (CNPW) observer was used to optimize processing parameters (number of iterations, post-reconstruction filter) for the human observer study. Human observers then rated each image and selected the most-likely lesion location. The area under the LROC curve (ALROC) and the probability of correct localization were used as figures-of-merit. The results of the human observer study found no statistically significant difference between FORE and AW-OSEM3D (ALROC=0.41 and 0.36, respectively), an increase in lesion detection performance for LOR-OSEM3D (ALROC=0.45, p=0.076), and additional improvement with the use of the PSF model (ALROC=0.55, p=0.024). The numerical CNPW observer provided the same rankings in performance among algorithms, but obtained different values of ALROC. These results show improved lesion detection performance for the reconstruction algorithms with more sophisticated statistical and imaging models as compared to the previous-generation algorithms.

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