Attenuation correction using 3D deep convolutional neural network for brain 18F FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction
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.
PloS one