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Abstract
Purpose: This study was to develop a convolutional neural network (CNN) model with a residual learning framework to predict the full-time 18F-florbetaben (18F-FBB) PET/CT images from corresponding short-time scans.

Methods: In this retrospective study, we enrolled 22 cognitively normal subjects, 20 patients with mild cognitive impairment, and 42 patients with Alzheimer disease. Twenty minutes of list-mode PET/CT data were acquired and reconstructed as the ground-truth images. The short-time scans were made in either 1, 2, 3, 4, or 5 minutes. The CNN with a residual learning framework was implemented to predict the ground-truth images of 18F-FBB PET/CT using short-time scans with either a single-slice or a 3-slice input layer. Model performance was evaluated by quantitative and qualitative analyses. Additionally, we quantified the amyloid load in the ground-truth and predicted images using the SUV ratio.

Results: On quantitative analyses, with increasing scan time, the normalized root-mean-squared error and the SUV ratio differences between predicted and ground-truth images gradually decreased, and the peak signal-to-noise ratio increased. On qualitative analysis, the predicted images from the 3-slice CNN model showed better image quality than those from the single-slice model. The 3-slice CNN model with a short-time scan of at least 2 minutes achieved comparable, quantitative prediction of full-time 18F-FBB PET/CT images, with adequate to excellent image quality.

Conclusions: The 3-slice CNN model with a residual learning framework is promising for the prediction of full-time 18F-FBB PET/CT images from short-time scans.

Affiliations

Sangwon Lee  1 , Jin Ho Jung  2 , Dongwoo Kim  1 , Hyun Keong Lim  2 , Mi-Ae Park  3 , Garam Kim  2 , Minjae So  4 , Sun Kook Yoo  5 , Byoung Seok Ye  6 , Yong Choi  2 , Mijin Yun  1
1 From the Department of Nuclear Medicine, Yonsei University College of Medicine.
2 Department of Electronic Engineering, Sogang University, Seoul, Korea.
3 Department of Radiology, Brigham and Women's Hospital & Harvard Medical School, Boston, MA.
4 Yonsei University College of Medicine.
5 Departments of Medical Engineering.
6 Neurology, Yonsei University College of Medicine, Seoul, Korea.