Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET/MR
Mi-Ae Park, Vlad G. Zaha, Ramsey D. Badawi, Spencer L. Bowen

TL;DR
This paper introduces a novel attenuation correction method for cardiac PET/MR that uses a small external source and physics-based reconstruction, achieving accuracy comparable to CT-based methods without prior image data.
Contribution
The proposed sTX-MLAA algorithm uniquely combines external source coincidences with patient data for attenuation correction, eliminating the need for prior images or assumptions.
Findings
Less than 5% bias in activity concentrations compared to CT-AC.
Significant reduction in background variability.
Achieves PET quantification comparable to PET/CT.
Abstract
Quantitative PET attenuation correction (AC) for combined cardiac PET/MR is a challenging problem. We propose and evaluate an AC approach that uses coincidences from a relatively weak and physically fixed sparse external source, in combination with that from the patient, to correct for PET attenuation based on physics principles alone. The low 30 ml volume of the source makes it easy to fill and place, and the method does not use prior image data or attenuation map assumptions. Our supplemental transmission aided maximum likelihood reconstruction of attenuation and activity (sTX-MLAA) algorithm contains an attenuation map update that maximizes the likelihood of terms representing coincidences originating from tracer in the patient and a weighted expression of counts segmented from the external source alone. Both external source and patient scatter and randoms are fully corrected. We…
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Taxonomy
TopicsMedical Imaging Techniques and Applications · Radiomics and Machine Learning in Medical Imaging · Advanced X-ray and CT Imaging
