Advancements in data-driven respiratory motion extraction methods for clinical list-mode 18F-FDG PET datasets acquired from a commercial PET scanner

Taek-Soo Lee, Jizhe Wang, Jingyan Xu, Patrick Olivier, Amy E. Perkins, Chi Hua Tung, Benjamin Tsui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We have significantly improved four data-driven respiratory motion (RM) extraction methods for various activity distributions in clinical myocardial perfusion (MP) and ^{\mathbf {18}} F-FDG PET datasets. They are activity distributions: (1) with high myocardial uptake, (2) same as (1) but with portion of the heart outside the image, and with high image intensity (3) in the liver and (4) in the lung area without attenuation compensation. In Method #1, a 3D volume-of-interest (VOI) was placed over the heart region of the PET image obtained from the total acquisition time period. The surrogate RM signals were obtained from the centroids of the image intensity of the myocardial activity uptake within the same VOI of PET images obtained from rebinned listmode data in short time intervals. The Fourier Transform (FT) of the time sequence of surrogate RM signals and smoothing reveal the RM peak and its average period, \text{P}-{\mathbf {av}}. In Methods #2, #3, and #4, specially-shaped 3D VOIs were placed over the heart, the top of the liver, and the bottom of the lungs, respectively. Then, the same procedures used in Method #1 were employed except using the total counts within the corresponding VOI. The location, sizes and shapes of the VOIs were optimized for the highest signal-to-noise (S/N) in the RM peak extraction. The improved RM extraction methods were evaluated using 14 patient datasets. Method #1 was shown to work well for 79% of the datasets, and Pav showing high S/N and excellent agreement (Pearson correlation coefficient 0.997) with those obtained from an external RM monitoring belt system. Method #2 was applied successfully to 14%, and Methods #3 and #4 to the rest of datasets. Excellent agreements were also found in cross comparison between the methods. We conclude that the improved data-driven RM extraction methods which showed successful results in various PET image datasets will provide an important first step for the motion compensation application in commercial PET scanners.

Original languageEnglish (US)
Title of host publication2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538622827
DOIs
StatePublished - Nov 12 2018
Event2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Atlanta, United States
Duration: Oct 21 2017Oct 28 2017

Other

Other2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017
CountryUnited States
CityAtlanta
Period10/21/1710/28/17

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Keywords

  • Myocardial perfusion
  • pET
  • respiratory motion extraction

ASJC Scopus subject areas

  • Instrumentation
  • Radiology Nuclear Medicine and imaging
  • Nuclear and High Energy Physics

Cite this

Lee, T-S., Wang, J., Xu, J., Olivier, P., Perkins, A. E., Tung, C. H., & Tsui, B. (2018). Advancements in data-driven respiratory motion extraction methods for clinical list-mode 18F-FDG PET datasets acquired from a commercial PET scanner. In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings [8533107] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NSSMIC.2017.8533107