TY - GEN
T1 - Advancements in data-driven respiratory motion extraction methods for clinical list-mode 18F-FDG PET datasets acquired from a commercial PET scanner
AU - Lee, Taek Soo
AU - Wang, Jizhe
AU - Xu, Jingyan
AU - Olivier, Patrick
AU - Perkins, Amy E.
AU - Tung, Chi Hua
AU - Tsui, Benjamin M.W.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/11/12
Y1 - 2018/11/12
N2 - 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.
AB - 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.
KW - Myocardial perfusion
KW - pET
KW - respiratory motion extraction
UR - http://www.scopus.com/inward/record.url?scp=85058461622&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058461622&partnerID=8YFLogxK
U2 - 10.1109/NSSMIC.2017.8533107
DO - 10.1109/NSSMIC.2017.8533107
M3 - Conference contribution
AN - SCOPUS:85058461622
T3 - 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings
BT - 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017
Y2 - 21 October 2017 through 28 October 2017
ER -