Dual-tracer PET image direct reconstruction and separation based on three-dimensional encoder-decoder network

Zeng, Fuzhen; Liu, Huafeng

doi:10.1117/12.2573439

Cited by 2 publications

(2 citation statements)

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“…In deep learning for dual-tracer signal separation, the designed network is usually trained to simultaneously infer the separated images of each tracer, which can be considered as a multi-task learning problem [44][45][46]. Our proposed architecture is based on the hard parameter sharing approach commonly employed in multi-task learning [51], where a common space representation is shared for all tasks, with additional layers that are specific to each task.…”

Section: Deep Learning Dual-tracer Separationmentioning

confidence: 99%

“…The indirect strategy employs a neural network to separate the dynamic dual-tracer images reconstructed using traditional reconstruction algorithms into dynamic single-tracer images [37][38][39][40][41][42][43]. Alternatively, the direct strategy incorporates the reconstruction task with the separation task to perform an end-to-end learning from dual-tracer dynamic sinograms to separated single-tracer dynamic images using a neural network [44][45][46]. In [47], an unsupervised joint method based on a GAN (pix2pix [48]) is presented to separate simultaneous dual-tracer PET signals and perform ROI segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study

2023

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Multiplexed positron emission tomography (PET) imaging provides perfectly registered simultaneous functional and molecular imaging of more than one biomarker. However, the separation of the multiplexed PET signals within a single PET scan is challenging due to the fact that all PET tracers emit positrons, which, after annihilating with a nearby electron, give rise to 511 keV photon pairs that are detected in coincidence. Compartment modelling can separate single-tracer PET signals from multiplexed signals based on the differences in bio-distribution kinetics and radioactive decay. However, the compartment-modelling-based method requires staggered injections and assumes that each tracer’s input function is known. In this paper, we propose a deep-learning-based method to simultaneously separate dual-tracer PET signals without explicitly knowing the input functions. We evaluate the proposed deep-learning-based separation method on dual-tracer [18F]FDG and [11C]MET PET simulations and compare its separation performance to that of the compartment-modelling-based method, assessing performance dependence on the time interval between tracer injections as well as on the amount of training data. It is shown that the proposed method implicitly denoises the separated images and offers reduced variance in the separated images compared to compartment modelling.

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