Objective: Multiple different algorithms have been proposed for Data Driven Gating (DDG) in Single Photon Emission Computed Tomography (SPECT) and have successfully been applied to Myocardial Perfusion Imaging (MPI). Application of DDG to acquisition types other than SPECT MPI has not been demonstrated so far, as the limitations and pitfalls of current methods are unknown.

Approach: We create a comprehensive set of phantoms that allow the characterization of DDG algorithms and perform Monte Carlo simulations, simulating the influence of different motion artifacts, view angles, moving feature diameters, contrasts, and count levels. We derive quantitative metrics from the data and evaluate the Center of Light (COL) and Laplacian Eigenmaps (LE) methods as sample DDG algorithms.

Main results: View angle, feature size, count rate density, and contrast influence the accuracy of both DDG methods. Moreover, the ability to extract the respiratory motion in the phantom was shown to correlate with the contrast of the moving feature to the background, the Signal to Noise ratio, and the noise in the data.

Significance: We showed that reporting the average correlation to an external physical reference signal per acquisition is not sufficient to characterize DDG methods. Assessing DDG methods on a view-by-view basis using the simulations and metrics from this work could enable the identification of pitfalls of current methods, and extend their application to acquisitions beyond SPECT MPI. 

This dataset contains the raw Monte Carlo simulations that were used in the work "Method for comparison of Data Driven Gating Algorithms in Emission Tomography" by Reymann et al. 

You will need python installed on your system and the module mentioned in the requirements.txt installed