title: Technical Note: Fast respiratory motion estimation using sorted singles without unlist processing: A feasibility study.
abstract: OBJECTIVE
  The study aims to demonstrate the feasibility of fast respiratory motion estimation using singles data available as a sorted format in list-mode files acquired in an integrated positron emission tomography/magnetic resonance imaging (PET/MRI) system for a proof-of-concept.
  
  
  METHODS
  The derivation of singles-driven respiratory motion (SDRM) is enabled by singles recorded and binned by second for each detector crystal in PET list-mode data acquired in a SIGNA PET/MR. The proposed method is to derive a SDRM trace by summing up all singles from all detectors through the PET data acquisition. To assess the feasibility of SDRM for data-driven gating (DDG), SDRM traces were derived from the list-mode data acquired in five liver-focused68Ga-DOTA-TOC PET/MRI scans, and compared with the traces derived from bellows (pressure belt). Pearson's correlation coefficients and trigger time differences at peak-inhalation phases between SDRM and bellows traces were measured for quantitative evaluation.
  
  
  RESULTS
  The method presented the average processing time of 4.2 ± 0.42 s (range: 3.9 ~ 4.7 s) for the derivation of SDRM traces. The majority of the time was spent for reading singles data from a list-mode file (3.1 ± 0.40 s, range: 2.7 ~ 3.7s). On average, the correlation coefficient of SDRM and bellows traces was 0.69 ± 0.16 (range: 0.41 ~ 0.80) and the time offset of SDRM-driven triggers from bellows-driven triggers was 0.25 ± 0.39 s (range: -0.85 ~ 2.69 s later than bellows triggers), demonstrating the similar patterns and phases of SDRM and bellows traces.
  
  
  CONCLUSIONS
  We introduced PET singles-driven respiratory motion (SDRM) estimation as a proof-of-principle, using sorted singles ready for immediate processing in list-mode data. The results demonstrated the feasibility of SDRM and its potential use for gated PET with fast processing time.
authors.affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
authors.forename: Jaewon
authors.initials: J
authors.lastname: Yang
authors.affiliation: Applied Science Lab, GE Healthcare, Menlo Park, CA, USA.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Khalighi
authors.affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.|Department of Radiology, San Francisco VA Medical Center, San Francisco, CA, USA.
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Hope
authors.affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
authors.forename: Karen
authors.initials: K
authors.lastname: Ordovas
authors.affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.|Joint Graduate Group in Bioengineering, University of California, San Francisco and Berkeley, CA, USA.|Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA.
authors.forename: Youngho
authors.initials: Y
authors.lastname: Seo
mesh_terms: D000465:Algorithms; D005240:Feasibility Studies; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D009038:Motion; D049268:Positron-Emission Tomography; D012119:Respiration
keywords: PET/MRI; data-driven gating; respiratory motion; singles