Generation of PET attenuation map for whole-body time-of-flight F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps.

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Abstract

We propose a new deep learning based approach to provide more accurate whole-body PET/MRI attenuation correction in comparison to the Dixon-based four-segment method. We use activity and attenuation maps estimated using the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to convolutional neural network (CNN) to learn a CT-derived attenuation map. Methods: The whole-body 18F-FDG PET/CT scan data of 100 cancer patients (38 males and 62 females, age = 57.3 ± 14.1 years) was retrospectively used for training and testing the CNN. A modified U-net was trained to predict a CT-derived μ-map (μ-CT) from the MLAA-generated activity distribution (λ-MLAA) and μ-map (μ-MLAA). One point three million patches derived from sixty patients’ data were used for training the CNN, data of 20 others were used as a validation set to prevent overfitting, and the data of the other 20 were used as a test set for the CNN performance analysis. The attenuation maps generated using the proposed method (μ-CNN), μ-MLAA, and four-segment method (μ-segment) were compared with the μ-CT, a ground truth. We also compared the voxel-wise correlation between the activity images reconstructed using OSEM with the μ-maps, and the standard uptake values of primary and metastatic bone lesions obtained by drawing regions of interest on the activity images. Results: The CNN generates less noisy attenuation maps and achieves better bone identification than MLAA. The average Dice similarity coefficient for bone regions between μ-CNN and μ-CT was 0.77, which was significantly higher than that between μ-MLAA and μ-CT (0.36). Also, the CNN result showed the best pixel-by-pixel correlation with the CT-based results and remarkably reduced differences of activity maps in comparison to CT-based attenuation correction. Conclusion: The proposed deep neural network produced a more reliable attenuation map for 511 keV photons than the four-segment method currently used in whole-body PET/MRI studies.
Copyright © 2019 by the Society of Nuclear Medicine and Molecular Imaging, Inc.