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OtherClinical Investigations (Human)
Open Access

Conditional Generative Adversarial Networks (cGANs) aided motion correction of dynamic 18F-FDG PET brain studies

Lalith Kumar Shiyam Sundar, David Iommi, Otto Muzik, Zacharias Chalampalakis, Eva-Maria Klebermass, Marius Hienert, Lucas Rischka, Rupert Lanzenberger, Andreas Hahn, Ekaterina Pataraia, Tatjana Traub-Weidinger and Thomas Beyer
Journal of Nuclear Medicine November 2020, jnumed.120.248856; DOI: https://doi.org/10.2967/jnumed.120.248856
Lalith Kumar Shiyam Sundar
1 Medical University of Vienna;
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David Iommi
1 Medical University of Vienna;
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Otto Muzik
2 Wayne State University, United States;
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Zacharias Chalampalakis
3 University Paris-Sud
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Eva-Maria Klebermass
1 Medical University of Vienna;
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Marius Hienert
1 Medical University of Vienna;
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Lucas Rischka
1 Medical University of Vienna;
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Rupert Lanzenberger
1 Medical University of Vienna;
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Andreas Hahn
1 Medical University of Vienna;
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Ekaterina Pataraia
1 Medical University of Vienna;
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Tatjana Traub-Weidinger
1 Medical University of Vienna;
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Thomas Beyer
1 Medical University of Vienna;
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Abstract

This work set out to develop a motion correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable, data-driven determination of involuntary subject motion during dynamic 18F-FDG brain studies. Methods: Ten healthy volunteers (5M/5F, 27 ± 7 years, 70 ± 10 kg) underwent a test-retest 18F-FDG PET/MRI examination of the brain (N = 20). The imaging protocol consisted of a 60-min PET list-mode acquisition contemporaneously acquired with MRI, including MR navigators and a 3D time-of-flight MR-angiography sequence. Arterial blood samples were collected as a reference standard representing the arterial input function (AIF). Training of the cGAN was performed using 70% of the total data sets (N = 16, randomly chosen), which was corrected for motion using MR navigators. The resulting cGAN mappings (between individual frames and the reference frame (55-60min p.i.)) were then applied to the test data set (remaining 30%, N = 6), producing artificially generated low-noise images from early high-noise PET frames. These low-noise images were then co-registered to the reference frame, yielding 3D motion vectors. Performance of cGAN-aided motion correction was assessed by comparing the image-derived input function (IDIF) extracted from a cGAN-aided motion corrected dynamic sequence against the AIF based on the areas-under-the-curves (AUCs). Moreover, clinical relevance was assessed through direct comparison of the average cerebral metabolic rates of glucose (CMRGlc) values in grey matter (GM) calculated using the AIF and the IDIF. Results: The absolute percentage-difference between AUCs derived using the motion-corrected IDIF and the AIF was (1.2 ± 0.9) %. The GM CMRGlc values determined using these two input functions differed by less than 5% ((2.4 ± 1.7) %). Conclusion: A fully-automated data-driven motion compensation approach was established and tested for 18F-FDG PET brain imaging. cGAN-aided motion correction enables the translation of non-invasive clinical absolute quantification from PET/MR to PET/CT by allowing the accurate determination of motion vectors from the PET data itself.

  • Image Processing
  • Neurology
  • PET
  • Research Methods
  • Deep learning
  • Head-motion correction
  • Patlak analysis
  • [18F]FDG brain
  • absolute quantification

Footnotes

  • Immediate Open Access: Creative Commons Attribution 4.0 International License (CC BY) allows users to share and adapt with attribution, excluding materials credited to previous publications. License: https://creativecommons.org/licenses/by/4.0/. Details: http://jnm.snmjournals.org/site/misc/permission.xhtml.

  • Copyright © 2020 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

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Journal of Nuclear Medicine: 62 (4)
Journal of Nuclear Medicine
Vol. 62, Issue 4
April 1, 2021
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Conditional Generative Adversarial Networks (cGANs) aided motion correction of dynamic 18F-FDG PET brain studies
Lalith Kumar Shiyam Sundar, David Iommi, Otto Muzik, Zacharias Chalampalakis, Eva-Maria Klebermass, Marius Hienert, Lucas Rischka, Rupert Lanzenberger, Andreas Hahn, Ekaterina Pataraia, Tatjana Traub-Weidinger, Thomas Beyer
Journal of Nuclear Medicine Nov 2020, jnumed.120.248856; DOI: 10.2967/jnumed.120.248856

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Conditional Generative Adversarial Networks (cGANs) aided motion correction of dynamic 18F-FDG PET brain studies
Lalith Kumar Shiyam Sundar, David Iommi, Otto Muzik, Zacharias Chalampalakis, Eva-Maria Klebermass, Marius Hienert, Lucas Rischka, Rupert Lanzenberger, Andreas Hahn, Ekaterina Pataraia, Tatjana Traub-Weidinger, Thomas Beyer
Journal of Nuclear Medicine Nov 2020, jnumed.120.248856; DOI: 10.2967/jnumed.120.248856
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Keywords

  • Image Processing
  • Neurology
  • PET
  • Research Methods
  • deep learning
  • Head-motion correction
  • Patlak analysis
  • [18F]FDG brain
  • absolute quantification
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