%0 Journal Article %A Samuel Hurley %A Matthew Spangler-Bickell %A Timothy Deller %A Timothy Skloss %A Floris Jansen %A Tyler Bradshaw %A Alan McMillan %T Evaluation of marker-based correction of head motion with integrated PET/MR using PET list mode reconstruction %D 2019 %J Journal of Nuclear Medicine %P 1357-1357 %V 60 %N supplement 1 %X 1357Introduction: Motion during PET acquisition will result in blurring, which may reduce the diagnostic value of the images and cause inaccurate quantitation. Many methods have been proposed in the literature, but no commercial solution exists for arbitrary rigid body motion correction in PET/MR, only basic correction of respiratory and cardiac motion. Frame-based image registration techniques have been demonstrated for brain imaging, but have low temporal resolution and can be inaccurate. PET/MR can estimate head motion from MR data, but this requires continuous MR acquisition, limiting the time available for diagnostic MR imaging. Commercial optical systems are available to correct for motion artifacts in MR imaging. In this work, we extend the application of an optical MR motion correction system to head FDG PET imaging. Data are acquired such that prospective correction of MR data and retrospective correction of PET list mode data can be accomplished from the same dataset of motion estimates. Methods: PET data were acquired on a GE SIGNA PET/MR (GE Healthcare, Waukesha, WI, USA). An optical camera (HobbitView Inc., San Jose, CA, USA) was attached to the 8-channel head coil with a 3D printed adapter, and a custom connector located in the scanner bore, allowing fast installation and removal of the camera and thus straightforward integration with existing patient workflow. The marker consists of an 8.5 x 4 cm curved piece of plastic with a pattern of unique symbols, enabling the camera to identify vertices, and is positioned on the patient’s forehead. Image processing is run in real time on a dedicated Linux server synchronized with the scanner’s clock. Rigid body motion estimates are recorded at 50 frames per second, and used to retrospectively correct list mode data during reconstruction while also prospectively updating the MR acquisition. A single subject was scanned with informed consent, in accordance with all local ethics policies, after injection of 18F-FDG and a short uptake duration. A 10-minute time of flight (TOF) PET scan of the head was acquired, along with MR based attenuation correction. The subject was instructed to hold still for 5 mins (static reference data), then gently rock their head from left to right for 5 mins to simulate non-compliant patient motion (Fig 1). TOF-OSEM reconstructions with were performed in MATLAB (R2018b, Mathworks, Natick, MA, USA) using GE Healthcare’s list mode reconstruction software with standard clinical reconstruction settings. Three 5-minute frames were reconstructed for comparison: non-motion reference (static), without motion correction (NoMC), motion correction from camera data (MC). Results: The motion-corrected PET image (MC) exhibited substantial recovery of image fidelity, was of diagnostic quality, and was visually indistinguishable from the static image (Fig. 2b), while the uncorrected motion PET image (NoMC) exhibited significant blurring, and was of non-diagnostic quality (Fig 2c). Root mean squared error (RMSE) of the PET values in the brain were 2402 Bq/cc for MC and 3054 Bq/cc for NoMC, a 27.1% improvement. Peak signal to noise ratio (PSNR) was 27.0 for MC and 24.9 for NoMC, a 7.7% improvement. Structural similarity index (SSIM) was 0.988 for MC and 0.980 for NoMC, a 0.81% improvement. Conclusions: In this work, we demonstrate a clinically viable method for retrospective correction of rigid body motion for head PET imaging in a single subject. Additional volunteers will be performed and presented at the conference. This method can enable PET imaging of patients with movement disorders (e.g. Parkinson’s disease) where motion is slow and continuous and cannot be broken into discrete frames, eliminate the need to anesthetize pediatric patients for brain scans, and improve the alignment of PET with MR for attenuation correction and anatomical localization. Furthermore, all counts are used in the list mode reconstruction, resulting in high SNR images. %U