Fast and Accurate Gamma Imaging System Calibration Based on Deep Denoising Networks and Self-Adaptive Data Clustering.

Gamma imagers play a key role in both industrial and medical applications. Modern gamma imagers typically employ iterative reconstruction methods in which the system matrix (SM) is a key component to obtain high-quality images. An accurate SM could be acquired from an experimental calibration step with a point source across the FOV, but at a cost of long calibration time to suppress noise, posing challenges to real-world applications. In this work, we propose a time-efficient SM calibration approach for a 4π-view gamma imager with short-time measured SM and deep-learning-based denoising. The key steps include decomposing the SM into multiple detector response function (DRF) images, categorizing DRFs into multiple groups with a self-adaptive K-means clustering method to address sensitivity discrepancy, and independently training separate denoising deep networks for each DRF group. We investigate two denoising networks and compare them against a conventional Gaussian filtering method. The results demonstrate that the denoised SM with deep networks faithfully yields a comparable imaging performance with the long-time measured SM. The SM calibration time is reduced from 1.4 h to 8 min. We conclude that the proposed SM denoising approach is promising and effective in enhancing the productivity of the 4π-view gamma imager, and it is also generally applicable to other imaging systems that require an experimental calibration step.

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 18F-fluoromisonidazole (18F-FMISO) dataset using single-bed scanning and a 68Ga-DOTATATE dataset using whole-body scanning. The datasets of 18F-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 18F-FMISO and whole-body 68Ga-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, with p < 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.

An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SNR), subsequently affecting diagnostic and quantitative accuracies. Deep learning methods have shown a great potential to reduce the noise and improve the SNR in low dose PET data. In this work, we comprehensively investigated the quantitative accuracy of small lung nodules, in addition to visual image quality, using deep learning based denoising methods for oncological PET imaging. We applied and optimized an advanced deep learning method based on the U-net architecture to predict the standard dose PET image from 10% low-dose PET data. We also investigated the effect of different network architectures, image dimensions, labels and inputs for deep learning methods with respect to both noise reduction performance and quantitative accuracy. Normalized mean square error (NMSE), SNR, and standard uptake value (SUV) bias of different nodule regions of interest (ROIs) were used for evaluation. Our results showed that U-net and GAN are superior to CAE with smaller SUVmean and SUVmax bias at the expense of inferior SNR. A fully 3D U-net has optimal quantitative performance compared to 2D and 2.5D U-net with less than 15% SUVmean bias for all the ten patients. U-net outperforms Residual U-net (r-U-net) in general with smaller NMSE, higher SNR and lower SUVmax bias. Fully 3D U-net is superior to several existing denoising methods, including Gaussian filter, anatomical-guided non-local mean (NLM) filter, and MAP reconstruction with Quadratic prior and relative difference prior, in terms of superior image quality and trade-off between noise and bias. Furthermore, incorporating aligned CT images has the potential to further improve the quantitative accuracy in multi-channel U-net. We found the optimal architectures and parameters of deep learning based methods are different for absolute quantitative accuracy and visual image quality. Our quantitative results demonstrated that fully 3D U-net can both effectively reduce image noise and control bias even for sub-centimeter small lung nodules when generating standard dose PET using 10% low count down-sampled data.