서울의대 / 황동휘, 이재성*
Abstract
We propose a new deep learning-based approach to provide more accurate whole-body PET/MRI attenuation correction than is possible with the Dixon-based 4-segment method. We use activity and attenuation maps estimated using the maximum-likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a 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 men and 62 women; age, 57.3 ± 14.1 y) were 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). We used 1.3 million patches derived from 60 patients' data 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 4-segment method (μ-segment) were compared with the μ-CT, a ground truth. We also compared the voxelwise correlation between the activity images reconstructed using ordered-subset expectation maximization with the μ-maps, and the SUVs 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 in 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 4-segment method currently used in whole-body PET/MRI studies.
Author information
Hwang D1,2, Kang SK1,2, Kim KY1,2, Seo S3, Paeng JC2,4, Lee DS5,4,6, Lee JS7,2,4.
1
Department of Biomedical Sciences, Seoul National University, Seoul, Korea.
2
Department of Nuclear Medicine, Seoul National University, Seoul, Korea.
3
Department of Neuroscience, College of Medicine, Gachon University, Incheon, Korea jaes@snu.ac.kr.
4
Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Korea; and.
5
Department of Nuclear Medicine, Seoul National University, Seoul, Korea jaes@snu.ac.kr.
6
Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Korea.
7
Department of Biomedical Sciences, Seoul National University, Seoul, Korea jaes@snu.ac.kr.
편집위원
deep neural network를 이용해 기존보다 더 향상된 attenuation map을 PET/MRI에 적용한 점이 인상깊음
2019-09-27 15:09:43