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

  • Martin Lyngby LassenEmail author
  • Thomas Beyer
  • Alexander Berger
  • Dietrich Beitzke
  • Sazan Rasul
  • Florian Büther
  • Marcus Hacker
  • Jacobo Cal-González
Original Article

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 (TBRLV) in the myocardium and the noise in the liver (CoVliver) 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). TBRLV 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.

Keywords

Respiratory gating listmode motion correction cardiac PET 

Abbreviations

CoV

Coefficient of variation

DDG

Data-driven gating

DPR-MoCo

Data-driven, projection based motion detection and compensation

LV

Left ventricle

MoCo

Motion compensation

MRI

Magnetic resonance imaging

MVS

Myocardial viability studies

RTA

Reconstruction transform average

Notes

Acknowledgments

We thank Piotr Slomka (Cedars-Sinai Medical Center) and Amir Fatemi (Medical University of Vienna) for helpful discussions. We also thank Klaus Schaefers for providing us with the experimental phantom data and for helpful discussions.

Disclosures

MH, TB and FB all have grants with Siemens Healthineers. None of the disclosures influenced the current study. In addition, TB has received royalties from Henry Stewart Lectutes. This disclosure did not influence the current study. MH also has given lectures sponsored by GE Healthcare, Bayer Healthcare and Siemens Healthineers. Furthermore MH, hold grants from Eli Lilly, Roche, BMS, Ipsen, ITM and EZAG. None of these disclosures influenced this study. MLL, AB, DB, SR, JCG has no conflict of interest or any disclosures relevant to this paper.

Supplementary material

12350_2019_1613_MOESM1_ESM.docx (22 kb)
Supplementary material 1 (DOCX 22 kb)
12350_2019_1613_MOESM2_ESM.docx (3.6 mb)
Supplementary material 2 (DOCX 3677 kb)
12350_2019_1613_MOESM3_ESM.pptx (1.8 mb)
Supplementary material 3 (PPTX 1805 kb)

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Copyright information

© American Society of Nuclear Cardiology 2019

Authors and Affiliations

  • Martin Lyngby Lassen
    • 1
    • 2
    Email author
  • Thomas Beyer
    • 1
  • Alexander Berger
    • 1
  • Dietrich Beitzke
    • 3
  • Sazan Rasul
    • 4
  • Florian Büther
    • 5
  • Marcus Hacker
    • 4
  • Jacobo Cal-González
    • 1
  1. 1.QIMP Team, Center for Medical Physics and Biomedical EngineeringMedical University of ViennaViennaAustria
  2. 2.Artificial Intelligence in Medicine programCedars-Sinai Medical CenterLos AngelesUSA
  3. 3.Division of Cardiovascular and Interventional Radiology, Department of Biomedical Engineering and Image-guided TherapyMedical University of ViennaViennaAustria
  4. 4.Division of Nuclear Medicine, Department of Biomedical Engineering and Image-guided TherapyMedical University of ViennaViennaAustria
  5. 5.Department of Nuclear MedicineUniversity Hospital MünsterMünsterGermany

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