MR-assisted PET respiratory motion correction using deep-learning based short-scan motion fields

Sihao Chen, Tyler J. Fraum, Cihat Eldeniz, Joyce Mhlanga, Weijie Gan, Thomas Vahle, Uday B. Krishnamurthy, David Faul, H. Michael Gach, Michael M. Binkley, Ulugbek S. Kamilov, Richard Laforest, Hongyu An

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: We evaluated the impact of PET respiratory motion correction (MoCo) in a phantom and patients. Moreover, we proposed and examined a PET MoCo approach using motion vector fields (MVFs) from a deep-learning reconstructed short MRI scan. Methods: The evaluation of PET MoCo was performed in a respiratory motion phantom study with varying lesion sizes and tumor to background ratios (TBRs) using a static scan as the ground truth. MRI-based MVFs were derived from either 2000 spokes (MoCo2000, 5–6 min acquisition time) using a Fourier transform reconstruction or 200 spokes (MoCoP2P200, 30–40 s acquisition time) using a deep-learning Phase2Phase (P2P) reconstruction and then incorporated into PET MoCo reconstruction. For six patients with hepatic lesions, the performance of PET MoCo was evaluated using quantitative metrics (SUVmax, SUVpeak, SUVmean, lesion volume) and a blinded radiological review on lesion conspicuity. Results: MRI-assisted PET MoCo methods provided similar results to static scans across most lesions with varying TBRs in the phantom. Both MoCo2000 and MoCoP2P200 PET images had significantly higher SUVmax, SUVpeak, SUVmean and significantly lower lesion volume than non-motion-corrected (non-MoCo) PET images. There was no statistical difference between MoCo2000 and MoCoP2P200 PET images for SUVmax, SUVpeak, SUVmean or lesion volume. Both radiological reviewers found that MoCo2000 and MoCoP2P200 PET significantly improved lesion conspicuity. Conclusion: An MRI-assisted PET MoCo method was evaluated using the ground truth in a phantom study. In patients with hepatic lesions, PET MoCo images improved quantitative and qualitative metrics based on only 30–40 s of MRI motion modeling data.

Original languageEnglish
Pages (from-to)676-690
Number of pages15
JournalMagnetic resonance in medicine
Volume88
Issue number2
DOIs
StateAccepted/In press - 2022

Keywords

  • CAPTURE
  • P2P
  • PET/MRI
  • deep learning
  • free-breathing
  • respiratory motion correction

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