AUTHOR=Pan Bolin , Marsden Paul K. , Reader Andrew J. 

TITLE=Deep learned triple-tracer multiplexed PET myocardial image separation

JOURNAL=Frontiers in Nuclear Medicine

VOLUME=Volume 4 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/nuclear-medicine/articles/10.3389/fnume.2024.1379647

DOI=10.3389/fnume.2024.1379647

ISSN=2673-8880

ABSTRACT=In multiplexed positron emission tomography (mPET) imaging, physiological and pathological information from different radiotracers can be observed simultaneously in a single dynamic PET scan. The separation of mPET signals within a single PET scan is challenging due to the fact that the PET scanner measures the sum of the PET signals of all the tracers. The conventional multi-tracer compartment modeling method (MTCM) requires staggered injections and assumes that the arterial input functions (AIFs) of each tracer are known. In this work, we propose a deep learning-based method to separate triple-tracer PET images without explicitly knowing the AIFs. Dynamic triple-tracer noisy MLEM reconstruction was used as the network input and dynamic single-tracer noisy MLEM reconstructions were used as the training labels. A simulation study was performed to evaluate the performance of the proposed framework on triple-tracer ([ 18F]FDG+ 82Rb+[ 94mTc]sestamibi) PET myocardial imaging. The results show that the proposed methodology substantially reduced the noise level compared to the results obtained from single-tracer imaging. Additionally, it achieved lower bias and standard deviation in the separated single-tracer images compared to the MTCM-based method at both the voxel and ROI levels.