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

Md Ashequr Rahman; Abhinav K. Jha

doi:10.1117/12.2582266

15 February 2021 Task-based assessment of binned and list-mode SPECT systems

Md Ashequr Rahman, Abhinav K. Jha

Proceedings Volume 11599, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment; 1159904 (2021) https://doi.org/10.1117/12.2582266
Event: SPIE Medical Imaging, 2021, Online Only

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.

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Md Ashequr Rahman and Abhinav K. Jha "Task-based assessment of binned and list-mode SPECT systems", Proc. SPIE 11599, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, 1159904 (15 February 2021); https://doi.org/10.1117/12.2582266

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