Oncotarget

Research Papers:

4D-CT-based motion correction of PET images using 3D iterative deconvolution

Lena Thomas _, Thomas Schultz, Vesna Prokic, Matthias Guckenberger, Stephanie Tanadini-Lang, Melanie Hohberg, Markus Wild, Alexander Drzezga and Ralph A. Bundschuh

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Oncotarget. 2019; 10:2987-2995. https://doi.org/10.18632/oncotarget.26862

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Abstract

Lena Thomas1, Thomas Schultz2, Vesna Prokic3,4, Matthias Guckenberger5, Stephanie Tanadini-Lang5, Melanie Hohberg6, Markus Wild6, Alexander Drzezga6 and Ralph A. Bundschuh1

1Klinik und Poliklinik für Nuklearmedizin, Universitaetsklinikum Bonn, Bonn, Germany

2B-IT and Department of Computer Science, Universitaet Bonn, Bonn, Germany

3University Koblenz-Landau, Department of Physics, Koblenz, Germany

4University of Applied Sciences Koblenz, Koblenz, Germany

5Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland

6Department of Nuclear Medicine Universitaetsklinikum Köln, Cologne, Germany

Correspondence to:

Lena Thomas, email: LenaThomas.klinik@gmail.com

Keywords: PET/CT; motion correction;deblurring

Received: January 08, 2019     Accepted: March 23, 2019     Published: April 26, 2019

ABSTRACT

Objectives: Positron emission tomography acquisition takes several minutes representing an image averaged over multiple breathing cycles. Therefore, in areas influenced by respiratory movement, PET-positive lesions occur larger, but less intensive than they actually are, resulting in false quantitative assessment. We developed a motion-correction algorithm based on 4D-CT without the need to adapt PET-acquisition.

Methods: The algorithm is based on a full 3D iterative Richardson-Lucy-Deconvolution using a point-spread-function constructed using the motion information obtained from the 4D-CT. In a motion phantom study (3 different hot spheres in background activity), optimal parameters for the algorithm in terms of number of iterations and start image were estimated. Finally, the correction method was applied to 3 patient data sets. In phantom and patient data sets lesions were delineated and compared between motion corrected and uncorrected images for activity uptake and volume.

Results: Phantom studies showed best results for motion correction after 6 deconvolution steps or higher. In phantom studies, lesion volume improved up to 23% for the largest, 43% for the medium and 49% for the smallest sphere due to the correction algorithm. In patient data the correction resulted in a significant reduction of the tumor volume up to 33.3 % and an increase of the maximum and mean uptake of the lesion up to 62.1 and 19.8 % respectively.

Conclusion: In conclusion, the proposed motion correction method showed good results in phantom data and a promising reduction of detected lesion volume and a consequently increasing activity uptake in three patients with lung lesions.


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