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01-12-2020 | Original Article

Data-driven, projection-based respiratory motion compensation of PET data for cardiac PET/CT and PET/MR imaging

Authors: Martin Lyngby Lassen, PhD, Thomas Beyer, PhD, Alexander Berger, MSc, Dietrich Beitzke, MD, Sazan Rasul, MD, PhD, Florian Büther, PhD, Marcus Hacker, MD, Jacobo Cal-González, PhD

Published in: Journal of Nuclear Cardiology | Issue 6/2020

Abstract

Background

Respiratory patient motion causes blurring of the PET images that may impact accurate quantification of perfusion and infarction extents in PET myocardial viability studies. In this study, we investigate the feasibility of correcting for respiratory motion directly in the PET-listmode data prior to image reconstruction using a data-driven, projection-based, respiratory motion compensation (DPR-MoCo) technique.

Methods

The DPR-MoCo method was validated using simulations of a XCAT phantom (Biograph mMR PET/MR) as well as experimental phantom acquisitions (Biograph mCT PET/CT). Seven patient studies following a dual-tracer (¹⁸F-FDG/¹³N-NH₃) imaging-protocol using a PET/MR-system were also evaluated. The performance of the DPR-MoCo method was compared against reconstructions of the acquired data (No-MoCo), a reference gate method (gated) and an image-based MoCo method using the standard reconstruction-transform-average (RTA-MoCo) approach. The target-to-background ratio (TBR_LV) in the myocardium and the noise in the liver (CoV_liver) were evaluated for all acquisitions. For all patients, the clinical effect of the DPR-MoCo was assessed based on the end-systolic (ESV), the end-diastolic volumes (EDV) and the left ventricular ejection fraction (EF) which were compared to functional values obtained from the cardiac MR.

Results

The DPR-MoCo and the No-MoCo images presented with similar noise-properties (CoV) (P = .12), while the RTA-MoCo and reference-gate images showed increased noise levels (P = .05). TBR_LV values increased for the motion limited reconstructions when compared to the No-MoCo reconstructions (P > .05). DPR-MoCo results showed higher correlation with the functional values obtained from the cardiac MR than the No-MoCo results, though non-significant (P > .05).

Conclusion

The projection-based DPR-MoCo method helps to improve PET image quality of the myocardium without the need for external devices for motion tracking.

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Galazka P, Di Carli MF. Cardiac PET/CT and prognosis. Cardiovasc Innov Appl 2016;2:47-59.

Büther F, Dawood M, Stegger L, et al. List mode-driven cardiac and respiratory gating in PET. J Nucl Med 2009;50:674-81. https://doi.org/10.2967/jnumed.108.059204.CrossRefPubMed

Dawood M, Büther F, Stegger L, et al. Optimal number of respiratory gates in positron emission tomography: A cardiac patient study. Med Phys 2009;36:1775-84. https://doi.org/10.1118/1.3112422.CrossRefPubMed

Giraud P, Houle A. Respiratory gating for radiotherapy: main technical aspects and clinical benefits. ISRN Pulmonol. 2013.

Lassen ML, Rasmussen T, Christensen TE, Kjær A, Hasbak P. Respiratory gating in cardiac PET: Effects of adenosine and dipyridamole. J Nucl Cardiol 2016. https://doi.org/10.1007/s12350-016-0631-z.CrossRefPubMed

Liu C, Pierce LA II, Alessio AM, Kinahan PE. The impact of respiratory motion on tumor quantification and delineation in static PET/CT imaging. Phys Med Biol 2009;54:7345.CrossRef

Kesner AL, Bundschuh RA, Detorie NC, et al. Respiratory gated PET derived in a fully automated manner from raw PET data. IEEE Trans Nucl Sci 2009;56:677-86. https://doi.org/10.1109/TNS.2009.2016341.CrossRef

Grimm R, Fürst S, Souvatzoglou M, et al. Self-gated MRI motion modeling for respiratory motion compensation in integrated PET/MRI. Med Image Anal 2014;19:110-20. https://doi.org/10.1016/j.media.2014.08.003.CrossRefPubMed

Munoz C, Kolbitsch C, Reader AJ, Marsden P, Schaeffter T, Prieto C. MR-based cardiac and respiratory motion-compensation techniques for PET-MR imaging. PET Clin 2016;11:179-91. https://doi.org/10.1016/j.cpet.2015.09.004.CrossRefPubMed

10.

Kesner AL, Kuntner C. A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods. Med Phys 2010;37:5550-9. https://doi.org/10.1118/1.3483784.CrossRefPubMed

11.

Feng T, Wang J, Tang J, Wang X, Gao X. Impact of motion compensation and partial volume correction for 18F-NaF PET/CT imaging of coronary plaque. Phys Med Biol 2018;63:015005.CrossRef

12.

Munoz C, Kunze KP, Neji R, et al. Motion-corrected whole-heart PET-MR for the simultaneous visualisation of coronary artery integrity and myocardial viability: An initial clinical validation. Eur J Nucl Mol Imaging 2018. https://doi.org/10.1007/s00259-018-4047-7.CrossRef

13.

Nehmeh SA. Respiratory motion correction strategies in thoracic PET-CT imaging. PET Clin 2013;8:29-36. https://doi.org/10.1016/j.cpet.2012.10.004.CrossRefPubMed

14.

Slomka PJ, Rubeaux M, Le Meunier L, et al. Dual-gated motion-frozen cardiac PET with flurpiridaz F18. J Nucl Med 2015;56:1876-81. https://doi.org/10.2967/jnumed.115.164285.CrossRefPubMed

15.

Picard Y, Thompson CJ. Motion correction of PET images using multiple acquisition frames. IEEE Trans Med Imaging 1997;16:137-44. https://doi.org/10.1109/42.563659.CrossRefPubMed

16.

Manber R, Thielemans K, Hutton BF, et al. Practical PET respiratory motion correction in clinical PET/MR. J Nucl Med 2015;56:890-6.CrossRef

17.

Rahmim A, Qi J, Sossi V. Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls. Med Phys 2013;40:064301. https://doi.org/10.1118/1.4800806.CrossRefPubMedPubMedCentral

18.

Tsoumpas C, Polycarpou I, Thielemans K, et al. The effect of regularization in motion compensated PET image reconstruction: A realistic numerical 4D simulation study. Phys Med Biol 2013;58:1759-73. https://doi.org/10.1088/0031-9155/58/6/1759.CrossRefPubMed

19.

Kolbitsch C, Ahlman MA, Davies-Venn C, et al. Cardiac and respiratory motion correction for simultaneous cardiac PET/MR. J Nucl Med 2017;58:846-52.CrossRef

20.

Segars WP, Sturgeon G, Mendonca S, Grimes J, Tsui BMW. 4D XCAT phantom for multimodality imaging research. Med Phys 2010;37:4902-15. https://doi.org/10.1118/1.3480985.CrossRefPubMedPubMedCentral

21.

Tsoumpas C, Buerger C, Mollet P, Marsden PK. Fast analytic simulation toolkit for generation of 4D PET-MR data from real dynamic MR acquisitions. J Phys 2011;317:12020.

22.

Cal-González J, Tsoumpas C, Lassen ML, et al. Impact of motion compensation and partial volume correction for 18F-NaF PET/CT imaging of coronary plaque. Phys Med Biol 2017;63:15005.CrossRef

23.

Thielemans K, Tsoumpas C, Mustafovic S, et al. STIR: Software for tomographic image reconstruction release 2. Phys Med Biol 2012;57:867.CrossRef

24.

Fieseler M, Kugel H, Gigengack F, et al. A dynamic thorax phantom for the assessment of cardiac and respiratory motion correction in PET/MRI: A preliminary evaluation. Nucl Instruments Methods Phys Res Sect A Accel Spectrom, Detect Assoc Equip 2013;702:59-63. https://doi.org/10.1016/j.nima.2012.09.039.CrossRef

25.

Dilsizian V, Bacharach SL, Beanlands RS, et al. ASNC imaging guidelines/SNMMI procedure standard for positron emission tomography (PET). Nucl Cardiol Proc 2016. https://doi.org/10.1007/s12350-016-0522-3.CrossRef

26.

Martinez-Möller A, Souvatzoglou M, Delso G, et al. Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: Evaluation with PET/CT data. J Nucl Med 2009;50:520-6.CrossRef

27.

Dawood M, Buther F, Lang N, Schober O, Schafers KP. Respiratory gating in positron emission tomography: A quantitative comparison of different gating schemes. Med Phys 2007;34:3067-76. https://doi.org/10.1118/1.2748104.CrossRefPubMed

28.

Di Carli MF, Dorbala S, Meserve J, El Fakhri G, Sitek A, Moore SC. Clinical myocardial perfusion PET/CT. J Nucl Med 2007;48:783-93. https://doi.org/10.2967/jnumed.106.032789.CrossRefPubMed

29.

Myronenko A, Song X. Intensity-based image registration by minimizing residual complexity. IEEE Trans Med Imaging 2010;29:1882-91. https://doi.org/10.1109/TMI.2010.2053043.CrossRefPubMed

30.

Dunnett CW. A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc 1955;50:1096-121.CrossRef

31.

Carr JC, Simonetti O, Bundy J, Li D, Pereles S, Finn JP. Cine MR angiography of the heart with segmented true fast imaging with steady-state precession. Radiology 2001;219:828-34.CrossRef

32.

Joshi NV, Vesey AT, Williams MC, et al. 18F-fluoride positron emission tomography for identification of ruptured and high-risk coronary atherosclerotic plaques: A prospective clinical trial. Lancet 2014;383:705-13. https://doi.org/10.1016/S0140-6736(13)61754-7.CrossRefPubMed

Title: Data-driven, projection-based respiratory motion compensation of PET data for cardiac PET/CT and PET/MR imaging
Authors: Martin Lyngby Lassen, PhD
Thomas Beyer, PhD
Alexander Berger, MSc
Dietrich Beitzke, MD
Sazan Rasul, MD, PhD
Florian Büther, PhD
Marcus Hacker, MD
Jacobo Cal-González, PhD
Publication date: 01-12-2020
Publisher: Springer International Publishing
Published in: Journal of Nuclear Cardiology / Issue 6/2020
Print ISSN: 1071-3581
Electronic ISSN: 1532-6551
DOI: https://doi.org/10.1007/s12350-019-01613-2

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Methods

Results

Conclusion

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