AI-based data corrections for attenuation and scatter in PET and SPECT
Alan B. McMillan, Tyler J. Bradshaw
TL;DR
This paper reviews AI-driven methods for improving attenuation and scatter correction in PET and SPECT imaging, highlighting their potential to enhance image accuracy, speed, and scanner capabilities without additional CT imaging.
Contribution
It surveys current AI-based techniques for attenuation and scatter correction in PET and SPECT, discussing their implementation, benefits, and limitations.
Findings
AI improves image correction accuracy
AI enables CT-free PET/SPECT imaging
AI accelerates image reconstruction processes
Abstract
Recent developments in artificial intelligence technology have enabled new developments that can improve attenuation and scatter correction in PET and SPECT. These technologies will enable the use of accurate and quantitative imaging without the need to acquire a CT image, greatly expanding the capability of PET/MRI, PET-only, and SPECT-only scanners. The use of AI to aid in scatter correction will lead to improvements in image reconstruction speed, and improve patient throughput. This paper outlines the use of these new tools, surveys contemporary implementation, and discusses their limitations.
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Taxonomy
TopicsMedical Imaging Techniques and Applications · Advanced X-ray and CT Imaging · Radiomics and Machine Learning in Medical Imaging