Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic 18 F-FDG PET Brain Studies.

Autor:	Shiyam Sundar LK; QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria., Iommi D; QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria., Muzik O; Department of Pediatrics, Children's Hospital of Michigan, The Detroit Medical Center, Wayne State University School of Medicine, Detroit, Michigan otto@pet.wayne.edu., Chalampalakis Z; Service Hospitalier Frédéric Joliot, CEA, Inserm, CNRS, Univ. Paris Sud, Université Paris Saclay, Orsay, France., Klebermass EM; Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria., Hienert M; Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; and., Rischka L; Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; and., Lanzenberger R; Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; and., Hahn A; Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; and., Pataraia E; Department of Neurology, Medical University of Vienna, Austria., Traub-Weidinger T; Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria., Hummel J; QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria., Beyer T; QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
Jazyk:	angličtina
Zdroj:	Journal of nuclear medicine : official publication, Society of Nuclear Medicine [J Nucl Med] 2021 Jun 01; Vol. 62 (6), pp. 871-879. Date of Electronic Publication: 2020 Nov 27.
DOI:	10.2967/jnumed.120.248856
Abstrakt:	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 18 F-FDG brain studies. Methods: Ten healthy volunteers (5 men/5 women; mean age ± SD, 27 ± 7 y; weight, 70 ± 10 kg) underwent a test-retest 18 F-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 3-dimensional 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 datasets ( n = 16, randomly chosen), which was corrected for motion using MR navigators. The resulting cGAN mappings (between individual frames and the reference frame [55-60 min after injection]) were then applied to the test dataset (remaining 30%, n = 6), producing artificially generated low-noise images from early high-noise PET frames. These low-noise images were then coregistered to the reference frame, yielding 3-dimensional 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 with 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 gray matter 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 gray matter CMRGlc values determined using these 2 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 18 F-FDG PET brain imaging. cGAN-aided motion correction enables the translation of noninvasive clinical absolute quantification from PET/MR to PET/CT by allowing the accurate determination of motion vectors from the PET data itself. (© 2021 by the Society of Nuclear Medicine and Molecular Imaging.)
Databáze:	MEDLINE
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