Richard Manber1, Kris Thielemans2, Brian F Hutton3, Anna Barnes2, Sébastien Ourselin4, Simon Arridge4, Celia O'Meara5, Simon Wan2, David Atkinson6. 1. Division of Medicine, Centre for Medical Imaging, University College London, London, United Kingdom r.manber@ucl.ac.uk. 2. Institute of Nuclear Medicine, UCL and UCL Hospitals, London, United Kingdom. 3. Institute of Nuclear Medicine, UCL and UCL Hospitals, London, United Kingdom Centre for Medical Radiation Physics, University of Wollongong, New South Wales, Australia. 4. Centre for Medical Imaging Computing, Faculty of Engineering, University College London, London, United Kingdom; and. 5. Sidra Medical and Research Center, Doha, Qatar. 6. Division of Medicine, Centre for Medical Imaging, University College London, London, United Kingdom.
Abstract
UNLABELLED: Respiratory motion during PET acquisition may lead to blurring in resulting images and underestimation of uptake parameters. The advent of integrated PET/MR scanners allows us to exploit the integration of modalities, using high spatial resolution and high-contrast MR images to monitor and correct PET images degraded by motion. We proposed a practical, anatomy-independent MR-based correction strategy for PET data affected by respiratory motion and showed that it can improve image quality both for PET acquired simultaneously to the motion-capturing MR and for PET acquired up to 1 h earlier during a clinical scan. METHODS: To estimate the respiratory motion, our method needs only an extra 1-min dynamic MR scan, acquired at the end of the clinical PET/MR protocol. A respiratory signal was extracted directly from the PET list-mode data. This signal was used to gate the PET data and to construct a motion model built from the dynamic MR data. The estimated motion was then incorporated into the PET image reconstruction to obtain a single motion-corrected PET image. We evaluated our method in 2 steps. The PET-derived respiratory signal was compared with an MR measure of diaphragmatic displacement via a pencil-beam navigator. The motion-corrected images were compared with uncorrected images with visual inspection, line profiles, and standardized uptake value (SUV) in focally avid lesions. RESULTS: We showed a strong correlation between the PET-derived and MR-derived respiratory signals for 9 patients, with a mean correlation of 0.89. We then showed 4 clinical case study examples ((18)F-FDG and (68)Ga-DOTATATE) using the motion-correction technique, demonstrating improvements in image sharpness and reduction of respiratory artifacts in scans containing pancreatic, liver, and lung lesions as well as cardiac scans. The mean increase in peak SUV (SUV(peak)) and maximum SUV (SUV(max)) in a patient with 4 pancreatic lesions was 23.1% and 34.5% in PET acquired simultaneously with motion-capturing MR, and 17.6% and 24.7% in PET acquired 50 min before as part of the clinical scan. CONCLUSION: We showed that a respiratory signal can be obtained from raw PET data and that the clinical PET image quality can be improved using only a short additional PET/MR acquisition. Our method does not need external respiratory hardware or modification of the normal clinical MR sequences.
UNLABELLED: Respiratory motion during PET acquisition may lead to blurring in resulting images and underestimation of uptake parameters. The advent of integrated PET/MR scanners allows us to exploit the integration of modalities, using high spatial resolution and high-contrast MR images to monitor and correct PET images degraded by motion. We proposed a practical, anatomy-independent MR-based correction strategy for PET data affected by respiratory motion and showed that it can improve image quality both for PET acquired simultaneously to the motion-capturing MR and for PET acquired up to 1 h earlier during a clinical scan. METHODS: To estimate the respiratory motion, our method needs only an extra 1-min dynamic MR scan, acquired at the end of the clinical PET/MR protocol. A respiratory signal was extracted directly from the PET list-mode data. This signal was used to gate the PET data and to construct a motion model built from the dynamic MR data. The estimated motion was then incorporated into the PET image reconstruction to obtain a single motion-corrected PET image. We evaluated our method in 2 steps. The PET-derived respiratory signal was compared with an MR measure of diaphragmatic displacement via a pencil-beam navigator. The motion-corrected images were compared with uncorrected images with visual inspection, line profiles, and standardized uptake value (SUV) in focally avid lesions. RESULTS: We showed a strong correlation between the PET-derived and MR-derived respiratory signals for 9 patients, with a mean correlation of 0.89. We then showed 4 clinical case study examples ((18)F-FDG and (68)Ga-DOTATATE) using the motion-correction technique, demonstrating improvements in image sharpness and reduction of respiratory artifacts in scans containing pancreatic, liver, and lung lesions as well as cardiac scans. The mean increase in peak SUV (SUV(peak)) and maximum SUV (SUV(max)) in a patient with 4 pancreatic lesions was 23.1% and 34.5% in PET acquired simultaneously with motion-capturing MR, and 17.6% and 24.7% in PET acquired 50 min before as part of the clinical scan. CONCLUSION: We showed that a respiratory signal can be obtained from raw PET data and that the clinical PET image quality can be improved using only a short additional PET/MR acquisition. Our method does not need external respiratory hardware or modification of the normal clinical MR sequences.
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