Hybrid Head motion correction in PET/MR Brian Imaging Alaleh Rashidnasab1, Benjamin A. Thomas1, Richard Manber1, Marilena Rega1, Ilaria Boscolo Galazzo2, Francesco Fraioli1, Anna Barnes1, Brian F. Hutton1,3 and Kris Thielemans1 1Institute of Nuclear Medicine, University College London, UK 2Department of Neuroradiology, University Hospital Verona, Italy 3Centre for Medical Radiation Physics, University of Wollongong, Australia

Head motion correction PET-only with fixed time frames using registration of non-attenuation corrected (NAC) images: problems with intra-frame motion and registration. PET Data-driven motion detection: potentially faster and reduces intra-frame motion. However, registration can be problematic during early time frames due to rapid changes in uptake and low count levels. Continuous dynamic MR scan: precluding the acquisition of other MR sequences.

Clinically practical approach? Use MR sequences to estimate motion in dynamic PET In the beginning of dynamic PET changes are fast and contrast is low Acquire fast MR images interleaved with clinical MR to find rigid transformation Use raw PET list-mode data to detect motion using PCA in areas with sufficient contrast in the tracer uptake during dynamic PET Implement a pipeline using STIR to reconstruct motion-corrected PET images incorporating motion from MR

Aim A simple hybrid method to correct dynamic PET data for head motion during simultaneous PET/MR using routinely available MR and the PET data. Fast volumetric time frames PET-only with fixed time frames using registration of non-attenuation corrected (NAC) images: problems with intra-frame motion and registration. PET Data-driven motion detection: potentially faster and reduces intra-frame motion. However, registration can be problematic during early time frames due to rapid changes in uptake and low count levels. Continuous dynamic MR scan: precluding the acquisition of other MR sequences.

Overall Framework μ-map MR-based motion estimation in the beginning of the scan Motion detection using PCA on list-mode PET-based motion estimation in later frames Motion correction MR motion estimates* PCA PET motion estimates* PET Realign NAC recon.+ recon. frames ASL MRAC μ-map Data-driven time frames AC recon.+ ** * Rigid registration using Niftyreg ** Syncing MR and PET + Reconstruction using STIR with attenuation, scatter and randoms correction ASL PET μ-map PCA signal time frames MR motion estimates PCA Realign Data-driven time frames AC recon. NAC recon. Realign recon. frames PET motion estimates

Overall Framework μ-map MR-based motion estimation in the beginning of the scan Motion detection using PCA on list-mode PET-based motion estimation in later frames Motion correction MR motion estimates* PCA PET motion estimates* PET Realign NAC recon.+ recon. frames ASL MRAC μ-map Data-driven time frames AC recon.+ ** * Rigid registration using Niftyreg ASL PET μ-map PCA signal time frames MR motion estimates PCA Realign Data-driven time frames AC recon. NAC recon. Realign recon. frames PET motion estimates

Overall Framework μ-map MR-based motion estimation in the beginning of the scan Motion detection using PCA on list-mode PET-based motion estimation in later frames Motion correction MR motion estimates* PCA PET motion estimates* PET Realign NAC recon.+ recon. frames ASL MRAC μ-map Data-driven time frames AC recon.+ ** * Rigid registration using Niftyreg ** Syncing MR and PET ASL PET μ-map PCA signal time frames MR motion estimates PCA Realign Data-driven time frames AC recon. NAC recon. Realign recon. frames PET motion estimates

Overall Framework μ-map MR-based motion estimation in the beginning of the scan Motion detection using PCA on list-mode PET-based motion estimation in later frames Motion correction MR motion estimates* PCA PET motion estimates* PET Realign NAC recon.+ recon. frames ASL MRAC μ-map Data-driven time frames AC recon.+ ** * Rigid registration using Niftyreg ** Syncing MR and PET + Reconstruction using STIR with scatter and randoms correction ASL PET μ-map PCA signal time frames MR motion estimates PCA Realign Data-driven time frames AC recon. NAC recon. Realign recon. frames PET motion estimates

Overall Framework μ-map MR-based motion estimation in the beginning of the scan Motion detection using PCA on list-mode PET-based motion estimation in later frames Motion correction MR motion estimates* PCA PET motion estimates* PET Realign NAC recon.+ recon. frames ASL MRAC μ-map Data-driven time frames AC recon.+ ** * Rigid registration using Niftyreg ** Syncing MR and PET + Reconstruction using STIR with attenuation, scatter and randoms correction ASL PET μ-map PCA signal time frames MR motion estimates PCA Realign Data-driven time frames AC recon. NAC recon. Realign recon. frames PET motion estimates

Method MR-based motion estimation PET-based motion detection Fast volumetric MR at beginning of dynamic PET with low contrast Rigid registration of MR volumes using NiftyReg Record time intervals and start time PET-based motion detection PCA on list-mode data (4s frames leaving first 100) Define motion-minimised time frames, discard motion period Sync MR and PET by accounting for difference in start times. PET-based motion estimation Rigid registration of NAC PET frames for rest of scan using NiftyReg

Motion Correction List-mode data unlisted using PCA-driven time frames Reconstruct with attenuation, scatter and randoms correction. MRAC μ-map transformed to each dynamic frame prior to reconstruction using inverse registration transforms. Reconstructed images transformed to the reference position using MR-based transforms at the start NAC PET-based for the rest

Data-driven time frames Software issues Niftyreg reg-aladin rigOnly: registration not good for small changes Syncing MR and PET start time based on scanner time stamps Offset for first time stamp for first timing event derived from list-mode (list_lm_events --num-events-to-list) Offset for injection time delay, derived from list-mode (list_lm_countrates), threshold difference between prompt and delayed events Patient orientation/rotation from Siemens= HFS -> added into STIR Interfile Subject mu-map using e7tool: convert to 1/cm divide mu-map values by 10000. Remove offset keywords in mumap.hv as STIR recon don’t understand patient position Values output from STIR (proportional to counts) need scaling for KBq/ml related to global calibration factor ASL PET μ-map PCA signal time frames MR motion estimates PCA Realign Data-driven time frames AC recon. NAC recon. Realign recon. frames PET motion estimates

Patient data Epilepsy study using dynamic PET and perfusion-ASL Acquired on a Siemens Biograph mMR scanner. 4 ASL sequences, 100 brain volumes each with a temporal resolution of 2.8 s

Results NAC reconstructed frame with motion blur Reconstructed frames before and after motion occurrence Aligned motion frame using MR motion estimates Registered motion frame using NAC PET motion estimation Difference image of two motion corrected frames Fig. 2. (a) NAC reconstructed frame with motion blur; reconstructed frame (b) before and (c) after motion occurrence; (d) aligned frame in (c) to frame in (b) using MR-based rigid motion estimation; (e) registered frame in (c) to frame in (b) using NAC PET-based rigid motion estimation; (f) difference image of (d) and (e). Patient 2: Left Frontal Right Frontal Left Temporal Right Temporal

Evaluation: Time Activity Curves MPRAGE MR image segmented and parcellated using GIF. Selected ROIs: left/right temporal/frontal lobe Compared TAC extracted from ROIs on: FDG time frames reconstructed PET frames without motion correction (No-MoCo) Proposed method motion corrected reconstructed PET frames (MoCo)

Results Left Frontal Right Temporal Parcellated MPRAGE MR Patient 1 MPRAGE MR image segmented and parcellated using GIF. Selected ROIs: left/right temporal/frontal lobe Compared TAC extracted from ROIs on: FDG time frames reconstructed PET frames without motion correction (No-MoCo) Proposed method motion corrected reconstructed PET frames (MoCo) Patient 2 Fig. 2. (a) NAC reconstructed frame with motion blur; reconstructed frame (b) before and (c) after motion occurrence; (d) aligned frame in (c) to frame in (b) using MR-based rigid motion estimation; (e) registered frame in (c) to frame in (b) using NAC PET-based rigid motion estimation; (f) difference image of (d) and (e). Patient 2: Left Frontal Right Frontal Left Temporal Right Temporal

Future work Kinetic modelling for images with and without motion correction Use pCT for mu-map and MPRAGE as reference position.