Using statistical textural features in PET/CT is futile without strict exposure control

Ziel/Aim:

In quantitative PET/CT or when used in computer-aided diagnosis, textural feature analysis must base on reproducible values. This work investigated exposure dependency of second-order statistical textural features derived from grey level co-occurrence matrices (GLCMs). Exposure E was defined as phantom activity concentration times acquisition duration.

Methodik/Methods:

A homogeneous cylindrical Ge-68 phantom with 9.6 kBq/ml activity concentration was imaged on a Siemens Biograph mCT with acquisitions ranging from 3 s to 10861 s. Images with differing isometric voxel sizes were reconstructed with filtered back-projection (FBP), ordered subset expectation maximization (OSEM) and the Siemens TrueX algorithm. Eleven GLCM derived features – taken from a 50 mm wide cube at the centre of the phantom – were calculated and plotted as functions of exposure f(E). Feature stability was defined for df/dE -> 0.

Ergebnisse/Results:

Feature values from FBP reconstructions with 4 mm sized voxels were the most stable, whereas values from TrueX reconstructions with 1.5 mm sized voxels varied up to three orders of magnitude across different exposures. Some features shared common islands of stability showing exposure invariant metrics. Stability in ten features was reached in 4 mm FBP and OSEM acquisitions after around ½ min; 1.5 mm TrueX acquisitions took more than 30 min to achieve stability in nine features.

Schlussfolgerungen/Conclusions:

Textural features derived from GLCMs vary strongly with exposure, but islands of stability exist for some. This exposure invariance results in comparable and reproducible feature values, provided that any such stable regions are determined prior to the actual PET/CT measurements. Adjusting exposures to expected activity concentrations will prevent image count statistics from dominating texture values over true object inhomogeneity. Only within stable regions of exposure will textural features analysis make sense in PET/CT.