Improving PET Imaging by Correcting Motion and Attenuation
Author Information
Author(s): Elhamiasl Masoud, Jolivet Frederic, Rezaei Ahmadreza, Fieseler Michael, Schäfers Klaus, Nuyts Johan, Schramm Georg, Boada Fernando
Primary Institution: Cornell University
Hypothesis
Can new data-driven methods improve the quality of PET imaging by correcting for respiratory motion and attenuation artifacts?
Conclusion
The proposed methods significantly enhance image quality in PET imaging by effectively correcting for motion and attenuation artifacts.
Supporting Evidence
- The proposed methods improved lesion-to-background contrast from 2.0 to 5.2 for a liver dome lesion.
- In patient datasets, the methods reduced motion artifacts in lung and liver lesions.
- The methods provided image quality closely matching the reference reconstruction from a static acquisition.
Takeaway
This study found new ways to make pictures from PET scans clearer by fixing problems caused by breathing during the scan.
Methodology
The study evaluated two new methods using data from a phantom and clinical datasets, comparing image quality and lesion uptake values.
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