Experimental Comparison of Lesion Detectability for Four Fully-3D PET Reconstruction Schemes

Abstract

The objective of this work was to evaluate the lesion detection performance of four fully-3D positron emission tomography (PET) reconstruction schemes using experimentally acquired data. A multi-compartment anthropomorphic phantom was set up to mimic whole-body 18F-fluorodeoxyglucose (FDG) cancer imaging and scanned 12 times in 3D mode, obtaining count levels typical of noisy clinical scans. Eight of the scans had 26 68Ge “shell-less” lesions (6, 8-, 10-, 12-, 16-mm diameter) 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 characteristic (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 nonprewhitened (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 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.

Resolution modeling enhances PET imaging

“…It is well appreciated that two common tasks in clinical PET imaging include detection and quantification. In specific circumstances, resolution modeling in PET can genuinely improve hot-feature detection 4 and quantification. 5 Methods that enhance an image rarely provide improvement in all metrics and tasks-for example, detection may improve at the expense of quantitative accuracy.…”

“…At the risk of oversimplifying the issue in the interest of a terse argument, my view is that most resolution modeling techniques provide some contrast enhancement with some apparent noise reduction (although true noise is minimally changed). 6 Both trends lead to demonstrably better detection performance, 4 leaving little room to question the assertion that resolution modeling enhances detection.…”

“…A detection performance evaluation by Kadrmas et al 4 demonstrated, through observer studies on over 400 measured phantom images, that the area under the ROC increases by ∼30% when using better RM. While this was a statistically significant improvement, the authors acknowledged that they could not conclude about the clinical significance of such a gain.…”

“…What my esteemed colleague refers to as true noise [i.e., σ ensemble , or the coefficient-of-variation (COV) when expressed as a percentage] may change little with RM, 2 or instead be amplified twofold 4 or even more 6 in small ROIs for some RM implementations. As such, the issue of reproducibility in quantitative imaging tasks merits special attention.…”

Time Course of Tumor SUV in 18F-FDG PET of Breast Cancer: Presentation of a Simple Model Using a Single Reference Point for Time Corrections of Tumor SUVs

“…The changes in SUV ref between the 2 protocols appear plausible in that high-definition reconstruction generally yielded higher SUVs than did attenuation-weighted OSEM reconstruction (Fig. 2): high-definition reconstruction has better spatial resolution than attenuation-weighted OSEM (12) and presumably higher image noise because of the higher effective iteration (3/24 vs. 4/8). Thus, any highly metabolically active inhomogeneities within the tumors or …”