Task-based assessment of binned and list-mode SPECT systems

TL;DR

This study demonstrates that using list-mode data with energy attributes in SPECT imaging improves the accuracy of absolute quantification of regions of interest compared to traditional binned data processing, especially in a Parkinson's disease context.

Contribution

The paper provides a quantitative evaluation showing that list-mode data with energy information enhances SPECT quantification performance over binned data, using a realistic simulation and a Parkinson's disease protocol.

Findings

List-mode data improves quantification accuracy.

Including energy attributes enhances performance.

Results are based on realistic 2-D SPECT simulation.

Abstract

In SPECT, list-mode (LM) format allows storing data at higher precision compared to binned data. There is significant interest in investigating whether this higher precision translates to improved performance on clinical tasks. Towards this goal, in this study, we quantitatively investigated whether processing data in LM format, and in particular, the energy attribute of the detected photon, provides improved performance on the task of absolute quantification of region-of-interest (ROI) uptake in comparison to processing the data in binned format. We conducted this evaluation study using a DaTscan brain SPECT acquisition protocol, conducted in the context of imaging patients with Parkinson's disease. This study was conducted with a synthetic phantom. A signal-known exactly/background-known-statistically (SKE/BKS) setup was considered. An ordered-subset expectation-maximization algorithm was used to reconstruct images from data acquired in LM format, including the scatter-window data, and including the energy attribute of each LM event. Using a realistic 2-D SPECT system simulation, quantification tasks were performed on the reconstructed images. The results demonstrated improved quantification performance when LM data was used compared to binning the attributes in all the conducted evaluation studies. Overall, we observed that LM data, including the energy attribute, yielded improved performance on absolute quantification tasks compared to binned data.