High Temporal-Resolution Dynamic PET Image Reconstruction Using A New Spatiotemporal Kernel Method

Abstract

Abstract-Current clinical dynamic PET has an effective temporal resolution of 5-10 seconds, which can be adequate for traditional compartmental modeling but is inadequate for exploiting the benefit of more advanced tracer kinetic modeling. There is a need to improve dynamic PET to allow fine temporal sampling of 1-2 seconds. However, reconstruction of these shorttime frames from tomographic data is extremely challenging as the count level of each frame is very low and high noise presents in both spatial and temporal domains. Previously the kernel framework has been developed and demonstrated as a statistically efficient approach to utilizing image prior for lowcount PET image reconstruction. Nevertheless, the existing kernel methods mainly explore spatial correlations in the data and only have a limited ability in suppressing temporal noise. In this paper, we propose a new kernel method which extends the previous spatial kernel method to the general spatiotemporal domain. The new kernelized model encodes both spatial and temporal correlations obtained from image prior information and is incorporated into the PET forward projection model to improve the maximum likelihood (ML) image reconstruction. Computer simulations and an application to real patient scan have shown that the proposed approach can achieve effective noise reduction in both spatial and temporal domains and outperform the spatial kernel method and conventional ML reconstruction method for improving high temporal-resolution dynamic PET imaging.

(An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods

“…The reconstruction then becomes an optimisation problem in terms of ζ. This approach has been used in MMI : see [76,77,78] (PET with MRI), [79] (SPECT with PET), [80] (fluorescence molecular tomography with CT or MRI), [81] (DOT with CT), but also for MCMI : PET dynamic imaging using static images as guide [76] or temporal features derived from the raw data [82].…”

Applications of artificial intelligence in nuclear medicine image generation

“…Nevertheless, the existing kernel methods only consider the spatial correlation. Wang (145) extended the spatial kernel method to the spatial-temporal domain, which can effectively reduce noise in the space and temporal domain for dynamic PET imaging.…”

Technical Note: A preliminary study of dual‐tracer PET image reconstruction guided by FDG and/or MR kernels

“…Ellis et al proposed to use a weighted quadratic maximum a posteriori (MAP) method to reduce the noise of poorer‐quality PET images under the guidance of higher‐quality datasets 16,20 . In recent studies, 21 some researchers have also compared the MAP approach with the kernel method 22‐27 that has recently become popular. The comparison showed that for low‐count simulated datasets, the MAP‐related method performed poorly due to the inclusion of noisy PET information in the regularization term.…”