Abstract
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.
Highlights
An important technical challenge of integrated PET/MR is achieving an accurate PET attenuation correction (AC) comparable to transmission-based computed tomography (CT)
The mean Dice Similarity Coefficient (DSC) in the validation set between bone structures was 0.786 ± 0.05 on ACmapZTE compared with CT, and 0.81 ± 0.03 between ACmapU-net and CT-based AC (CTAC) maps (p < 0.05)
All MRAC techniques yielded a voxel distribution around the identity line. As measured with both the coefficient of determination R2 and Root Mean Square Error (RMSE) that reflects how well uptakes were estimated by the different AC methods, was better with the Unet compared to both Zero Echo Time (ZTE)- and Atlas-AC
Summary
An important technical challenge of integrated PET/MR is achieving an accurate PET attenuation correction (AC) comparable to transmission-based computed tomography (CT). In brain PET imaging, it is supplemented by the use of a single-atlas generated from an average of normal CT scans (atlas-based AC), or replaced by an ultrashort or zero echo time (UTE/ZTE) MRI to capture air and bone information. In the former, the single atlas is warped and co-registered to each new subject. In the latter, discrete coefficients are assigned to classes of voxels segmented through image processing or, more recently, through machine learning [3]
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