3.5D dynamic PET image reconstruction incorporating kinetics-based clusters

Lijun Lu, Nicolas A. Karakatsanis, Jing Tang, Wufan Chen, Arman Rahmim

Research output: Contribution to journalArticle

Abstract

Standard 3D dynamic positron emission tomographic (PET) imaging consists of independent image reconstructions of individual frames followed by application of appropriate kinetic model to the time activity curves at the voxel or region-of-interest (ROI). The emerging field of 4D PET reconstruction, by contrast, seeks to move beyond this scheme and incorporate information from multiple frames within the image reconstruction task. Here we propose a novel reconstruction framework aiming to enhance quantitative accuracy of parametric images via introduction of priors based on voxel kinetics, as generated via clustering of preliminary reconstructed dynamic images to define clustered neighborhoods of voxels with similar kinetics. This is then followed by straightforward maximum a posteriori (MAP) 3D PET reconstruction as applied to individual frames; and as such the method is labeled 3.5D image reconstruction. The use of cluster-based priors has the advantage of further enhancing quantitative performance in dynamic PET imaging, because: (a) there are typically more voxels in clusters than in conventional local neighborhoods, and (b) neighboring voxels with distinct kinetics are less likely to be clustered together. Using realistic simulated 11C-raclopride dynamic PET data, the quantitative performance of the proposed method was investigated. Parametric distribution-volume (DV) and DV ratio (DVR) images were estimated from dynamic image reconstructions using (a) maximum-likelihood expectation maximization (MLEM), and MAP reconstructions using (b) the quadratic prior (QP-MAP), (c) the Green prior (GP-MAP) and (d, e) two proposed cluster-based priors (CP-U-MAP and CP-W-MAP), followed by graphical modeling, and were qualitatively and quantitatively compared for 11 ROIs. Overall, the proposed dynamic PET reconstruction methodology resulted in substantial visual as well as quantitative accuracy improvements (in terms of noise versus bias performance) for parametric DV and DVR images. The method was also tested on a 90min 11C-raclopride patient study performed on the high-resolution research tomography. The proposed method was shown to outperform the conventional method in visual as well as quantitative accuracy improvements (in terms of noise versus regional DVR value performance).

Original languageEnglish (US)
Pages (from-to)5035-5055
Number of pages21
JournalPhysics in Medicine and Biology
Volume57
Issue number15
DOIs
StatePublished - Aug 7 2012

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Computer-Assisted Image Processing
Electrons
Raclopride
Noise
Cluster Analysis
Tomography
Research

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

3.5D dynamic PET image reconstruction incorporating kinetics-based clusters. / Lu, Lijun; Karakatsanis, Nicolas A.; Tang, Jing; Chen, Wufan; Rahmim, Arman.

In: Physics in Medicine and Biology, Vol. 57, No. 15, 07.08.2012, p. 5035-5055.

Research output: Contribution to journalArticle

Lu, Lijun ; Karakatsanis, Nicolas A. ; Tang, Jing ; Chen, Wufan ; Rahmim, Arman. / 3.5D dynamic PET image reconstruction incorporating kinetics-based clusters. In: Physics in Medicine and Biology. 2012 ; Vol. 57, No. 15. pp. 5035-5055.
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