  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.
