Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET

Previous studies have demonstrated the feasibility of reducing noise with deep learning-based methods for low-dose fluorodeoxyglucose (FDG) positron emission tomography (PET). This work aimed to investigate the feasibility of noise reduction for tracers without sufficient training datasets using a deep transfer learning approach, which can utilize existing networks trained by the widely available FDG datasets. In this study, the deep transfer learning strategy based on a fully 3D patch-based U-Net was investigated on a(18)F-fluoromisonidazole (F-18-FMISO) dataset using single-bed scanning and a(68)Ga-DOTATATE dataset using whole-body scanning. The datasets of(18)F-FDG by single-bed scanning and whole-body scanning were used to obtain pre-trained U-Nets separately for subsequent cross-tracer and cross-protocol transfer learning. The full-dose PET images were used as the labels while low-dose PET images from 10% counts were used as the inputs. Three types of U-Nets were obtained: a U-Net trained by FDG dataset, a pre-trained FDG U-Net fine-tuned by another less-available tracer (FMISO/DOATATE), and a U-Net completely trained by a large number of less-available tracer datasets (FMISO/DOATATE), used as the reference U-Net. The denoising performance of the three types of U-Nets was evaluated on single-bed(18)F-FMISO and whole-body(68)Ga-DOTATATE separately and compared using normalized root-mean-square error (NRMSE), signal-to-noise ratio (SNR), and relative bias of region of interest (ROI). For cross-tracer transfer learning, all the U-Nets provided denoised images with similar quality for both tracers. There was no significant difference in terms of NRMSE and SNR when comparing the former two U-Nets with the reference U-Net. The ROI biases for these U-Nets were similar. For cross-tracer and cross-protocol transfer learning, the pre-trained single-bed FDG U-Net fine-tuned by whole-body DOTATATE data provided the most consistent images with the reference U-Net. Fine-tuning significantly reduced the NRMSE and the ROI bias and improved the SNR when comparing the fine-tuned U-Net with the U-Net trained by single-bed FDG only (NRMSE: 96.3% +/- 21.1% versus 120.6% +/- 18.5%, ROI bias: -10.5% +/- 13.0% versus -14.7% +/- 6.4%, SNR: 4.2 +/- 1.4 versus 3.9 +/- 1.6, for fine-tuned U-Net and the U-Net trained by single-bed FDG, respectively, withp< 0.01 in all cases). This work demonstrated that it is feasible to utilize existing networks well-trained by FDG datasets to reduce the noise for other less-available tracers and other scanning protocols by using the fine-tuning strategy.