MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

Philip Novosad, Andrew Jonathan Reader

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)
140 Downloads (Pure)

Abstract

Recent advances in dynamic positron emission tomography (PET)
reconstruction have demonstrated that it is possible to achieve markedly
improved end-point kinetic parameter maps by incorporating a temporal
model of the radiotracer directly into the reconstruction algorithm. In this work
we have developed a highly constrained, fully dynamic PET reconstruction
algorithm incorporating both spectral analysis temporal basis functions and
spatial basis functions derived from the kernel method applied to a co-registered
T1-weighted magnetic resonance (MR) image. The dynamic PET image is
modelled as a linear combination of spatial and temporal basis functions,
and a maximum likelihood estimate for the coefficients can be found using
the expectation-maximization (EM) algorithm. Following reconstruction,
kinetic fitting using any temporal model of interest can be applied. Based on a
BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]
FDG simulation study with two noise levels, and investigated the quantitative
performance of the proposed reconstruction algorithm, comparing it with
reconstructions incorporating either spectral analysis temporal basis functions
alone or kernel spatial basis functions alone, as well as with conventional
frame-independent reconstruction. Compared to the other reconstruction
algorithms, the proposed algorithm achieved superior performance, offering
a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in
the tumours when they were present on the co-registered MR image. When the
tumours were not visible in the MR image, reconstruction with the proposed
algorithm performed similarly to reconstruction with spectral temporal
basis functions and was superior to both conventional frame-independent
reconstruction and frame-independent reconstruction with kernel spatial basis
functions. Furthermore, we demonstrate that a joint spectral/kernel model can
also be used for effective post-reconstruction denoising, through the use of an
EM-like image-space algorithm. Finally, we applied the proposed algorithm
to reconstruction of real high-resolution dynamic [11C]SCH23390 data,
showing promising results.
Original languageEnglish
Pages (from-to)4624-4645
Number of pages22
JournalPhysics in Medicine and Biology
Volume61
Issue number12
DOIs
Publication statusPublished - 16 May 2016

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