Impact of Image Noise on Gamma Index Calculation

Abstract

Purpose: The Gamma Index defines an asymmetric metric between the evaluated image and the reference image. It provides a quantitative comparison that can be used to indicate sample-wised pass/fail on the agreement of the two images. The Gamma passing/failing rate has become an important clinical evaluation tool. However, the presence of noise in the evaluated and/or reference images may change the Gamma Index, hence the passing/failing rate, and further, clinical decisions. In this work, we systematically studied the impact of the image noise on the Gamma Index calculation. Methods: We used both analytic formulation and numerical calculations in our study. The numerical calculations included simulations and clinical images. Three different noise scenarios were studied in simulations: noise in reference images only, in evaluated images only, and in both. Both white and spatially correlated noises of various magnitudes were simulated. For clinical images of various noise levels, the Gamma Index of measurement against calculation, calculation against measurement, and measurement against measurement, were evaluated. Results: Numerical calculations for both the simulation and clinical data agreed with the analytic formulations, and the clinical data agreed with the simulations. For the Gamma Index of measurement against calculation, its distribution has an increased mean and an increased standard deviation as the noise increases. On the contrary, for the Gamma index of calculation against measurement, its distribution has a decreased mean and stabilized standard deviation as the noise increases. White noise has greater impact on the Gamma Index than spatially correlated noise. Conclusions: The noise has significant impact on the Gamma Index calculation and the impact is asymmetric. The Gamma Index should be reported along with the noise levels in both reference and evaluated images. Reporting of the Gamma Index with switched roles of the images as reference and evaluated images or some composite metrics would be a good practice.