A deep learning-based whole-body solution for PET/MRI attenuation correction.

Background: Deep convolutional neural networks have demonstrated robust and reliable PET attenuation correction (AC) as an alternative to conventional AC methods in integrated PET/MRI systems. However, its whole-body implementation is still challenging due to anatomical variations and the limited MRI field of view. The aim of this study is to investigate a deep learning (DL) method to generate voxel-based synthetic CT (sCT) from Dixon MRI and use it as a whole-body solution for PET AC in a PET/MRI system.
Materials and Methods: Fifteen patients underwent PET/CT followed by PET/MRI with whole-body coverage from skull to feet. We performed MRI truncation correction and employed co-registered MRI and CT images for training and leave-one-out cross-validation. The network was pretrained with region-specific images. The accuracy of the AC maps and reconstructed PET images were assessed by performing a voxel-wise analysis and calculating the quantification error in SUV obtained using DL-based sCT (PET _sCT ) and a vendor-provided atlas-based method (PET _Atlas ), with the CT-based reconstruction (PET _CT ) serving as the reference. In addition, region-specific analysis was performed to compare the performances of the methods in brain, lung, liver, spine, pelvic bone, and aorta.
Results: Our DL-based method resulted in better estimates of AC maps with a mean absolute error of 62 HU, compared to 109 HU for the atlas-based method. We found an excellent voxel-by-voxel correlation between PET _CT and PET _sCT (R ²  = 0.98). The absolute percentage difference in PET quantification for the entire image was 6.1% for PET _sCT and 11.2% for PET _Atlas . The regional analysis showed that the average errors and the variability for PET _sCT were lower than PET _Atlas in all regions. The largest errors were observed in the lung, while the smallest biases were observed in the brain and liver.
Conclusions: Experimental results demonstrated that a DL approach for whole-body PET AC in PET/MRI is feasible and allows for more accurate results compared with conventional methods. Further evaluation using a larger training cohort is required for more accurate and robust performance.
(© 2022. The Author(s).)