Dynamic PET image reconstruction integrating temporal regularization associated with respiratory motion correction for applications in oncology

Thibaut Merlin, Dimitris Visvikis, Philippe Fernandez, Frédéric Lamare
Phys. Med. Biol.. 2018-02-13; 63(4): 045012
DOI: 10.1088/1361-6560/aaa86a

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1. Phys Med Biol. 2018 Feb 13;63(4):045012. doi: 10.1088/1361-6560/aaa86a.

Dynamic PET image reconstruction integrating temporal regularization associated
with respiratory motion correction for applications in oncology.

Merlin T(1), Visvikis D, Fernandez P, Lamare F.

Author information:
(1)INSERM, UMR1101, LaTIM, Université de Bretagne Occidentale, CHRU de Brest,
Brest, France.

Respiratory motion reduces both the qualitative and quantitative accuracy of PET
images in oncology. This impact is more significant for quantitative applications
based on kinetic modeling, where dynamic acquisitions are associated with limited
statistics due to the necessity of enhanced temporal resolution. The aim of this
study is to address these drawbacks, by combining a respiratory motion correction
approach with temporal regularization in a unique reconstruction algorithm for
dynamic PET imaging. Elastic transformation parameters for the motion correction
are estimated from the non-attenuation-corrected PET images. The derived
displacement matrices are subsequently used in a list-mode based OSEM
reconstruction algorithm integrating a temporal regularization between the 3D
dynamic PET frames, based on temporal basis functions. These functions are
simultaneously estimated at each iteration, along with their relative
coefficients for each image voxel. Quantitative evaluation has been performed
using dynamic FDG PET/CT acquisitions of lung cancer patients acquired on a GE
DRX system. The performance of the proposed method is compared with that of a
standard multi-frame OSEM reconstruction algorithm. The proposed method achieved
substantial improvements in terms of noise reduction while accounting for loss of
contrast due to respiratory motion. Results on simulated data showed that the
proposed 4D algorithms led to bias reduction values up to 40% in both tumor and
blood regions for similar standard deviation levels, in comparison with a
standard 3D reconstruction. Patlak parameter estimations on reconstructed images
with the proposed reconstruction methods resulted in 30% and 40% bias reduction
in the tumor and lung region respectively for the Patlak slope, and a 30% bias
reduction for the intercept in the tumor region (a similar Patlak intercept was
achieved in the lung area). Incorporation of the respiratory motion correction
using an elastic model along with a temporal regularization in the reconstruction
process of the PET dynamic series led to substantial quantitative improvements
and motion artifact reduction. Future work will include the integration of a
linear FDG kinetic model, in order to directly reconstruct parametric images.

DOI: 10.1088/1361-6560/aaa86a
PMID: 29339575 [Indexed for MEDLINE]

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