Abstract
Standard clinical reconstructions usually require several minutes to complete, and this time is mostly independent of the duration of the data being reconstructed. Applications such as data-driven motion estimation, which require many short frames over the duration of the scan, become unfeasible with such long reconstruction times. In this work, we present an infrastructure whereby ultra-fast list-mode reconstructions of very short frames (#1 s) are performed. With this infrastructure, it is possible to have a dynamic series of frames that can be used for various applications, such as data-driven motion estimation, whole-body surveys, quick reconstructions of gated data to select the optimal gate for a given attenuation map, and, if the infrastructure runs simultaneously with the scan, real-time display of the reconstructed data during the scan and automated alerts for patient motion. Methods: A fast ray-tracing time-of-flight projector was implemented and parallelized. The reconstruction parameters were optimized to allow for fast performance: only a few iterations are performed, without point-spread-function modeling, and scatter correction is not used. The resulting reconstructions are thus not quantitative but are acceptable for motion estimation and visualization purposes. Data-driven motion can be estimated using image registration, with the resultant motion data being used in a fully motion-corrected list-mode reconstruction. Results: The infrastructure provided images that can be used for visualization and gating purposes and for motion estimation using image registration. Several case studies are presented, including data-driven motion estimation and correction for brain studies, abdominal studies in which respiratory and cardiac motion is visible, and a whole-body survey. Conclusion: The presented infrastructure provides the capability to quickly create a series of very short frames for PET data that can be used in a variety of applications.
| Original language | English |
|---|---|
| Pages (from-to) | 287-292 |
| Number of pages | 6 |
| Journal | Journal of Nuclear Medicine |
| Volume | 62 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 1 2021 |
| Externally published | Yes |
Funding
Timothy Deller and Floris Jansen are employees of GE Healthcare. Matthew Spangler-Bickell was funded by GE Healthcare at the time of this study. No other potential conflict of interest relevant to this article was reported.
Keywords
- Data-driven motion correction
- Fast reconstruction
- List-mode reconstruction
- Real-time reconstruction