One of the main technical challenges of PET/MRI is to achieve an accurate PET attenuation correction (AC) estimation. In current systems, AC is accomplished by generating an MRI-based surrogate computed tomography (CT) from which AC-maps are derived. Nevertheless, all techniques currently implemented in clinical routine suffer from bias. We present here a convolutional neural network (CNN) that generated AC-maps from Zero Echo Time (ZTE) MR images. Seventy patients referred to our institution for 18FDG-PET/MR exam (SIGNA PET/MR, GE Healthcare) as part of the investigation of suspected dementia, were included. 23 patients were added to the training set of the manufacturer and 47 were used for validation. Brain computed tomography (CT) scan, two-point LAVA-flex MRI (for atlas-based AC) and ZTE-MRI were available in all patients. Three AC methods were evaluated and compared to CT-based AC (CTAC): one based on a single head-atlas, one based on ZTE-segmentation and one CNN with a 3D U-net architecture to generate AC maps from ZTE MR images. Impact on brain metabolism was evaluated combining voxel and regions-of-interest based analyses with CTAC set as reference. The U-net AC method yielded the lowest bias, the lowest inter-individual and inter-regional variability compared to PET images reconstructed with ZTE and Atlas methods. The impact on brain metabolism was negligible with average errors of -0.2% in most cortical regions. These results suggest that the U-net AC is more reliable for correcting photon attenuation in brain FDG-PET/MR than atlas-AC and ZTE-AC methods.

Objective: PET imaging of tau deposition using [18F]-MK6240 often involves long acquisitions in older subjects, many of whom exhibit dementia symptoms. The resulting unavoidable head motion can greatly degrade image quality. Motion increases the variability of PET quantitation for longitudinal studies across subjects, resulting in larger sample sizes in clinical trials of Alzheimer’s disease (AD) treatment.
Approach: After using an ultra-short frame-by-frame motion detection method based on the list-mode data, we applied an event-by-event list-mode reconstruction to generate the motion-corrected images from 139 scans acquired in 65 subjects. This approach was initially validated in two phantoms experiments against optical tracking data. We developed a motion metric based on the average voxel displacement in the brain to quantify the level of motion in each scan and consequently evaluate the effect of motion correction on images from studies with substantial motion. We estimated the rate of tau accumulation in longitudinal studies (51 subjects) by calculating the difference in the ratio of standard uptake values in key brain regions for AD. We compared the regions’ standard deviations across subjects from motion and non-motion corrected images.
Main Results: Individually, 14% of the scans exhibited notable motion quantified by the proposed motion metric, affecting 48% of the longitudinal datasets with three time points and 25% of all subjects. Motion correction decreased the blurring in images from scans with notable motion and improved the accuracy in quantitative measures. Motion correction reduced the standard deviation of the rate of tau accumulation by -49%, -24%, -18%, and -16% in the entorhinal, inferior temporal, precuneus, and amygdala regions, respectively. 
Significance: The list-mode-based motion correction method is capable of correcting both fast and slow motion during brain PET scans. It leads to improved brain PET quantitation, which is crucial for imaging AD.