Objective. Deep learning denoising networks are typically trained with images that are representative of the testing data. Due to the large variability of the noise levels in PET images, it is challenging to develop a proper training set for general clinical use. Our work aims to develop a personalized denoising strategy for the low-count PET images at various noise levels. Approach. We first investigated the impact of the noise level in the training images on the model performance. Five 3D U-Net models were trained on five groups of images at different noise levels, and a one-size-fits-all model was trained on images covering a wider range of noise levels. We then developed a personalized weighting method by linearly blending the results from two models trained on 20%-count level images and 60%-count level images to balance the trade-off between noise reduction and spatial blurring. By adjusting the weighting factor, denoising can be conducted in a personalized and task-dependent way. Main results. The evaluation results of the six models showed that models trained on noisier images had better performance in denoising but introduced more spatial blurriness, and the one-size-fits-all model did not generalize well when deployed for testing images with a wide range of noise levels. The personalized denoising results showed that noisier images require higher weights on noise reduction to maximize the structural similarity (SSIM) and mean squared error (MSE). And model trained on 20%-count level images can produce the best liver lesion detectability. Significance. Our study demonstrated that in deep learning-based low dose PET denoising, noise levels in the training input images have a substantial impact on the model performance. The proposed personalized denoising strategy utilized two training sets to overcome the drawbacks introduced by each individual network and provided a series of denoised results for clinical reading.

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Purpose Recently, deep learning‐based methods have been established to denoise the low‐count positron emission tomography (PET) images and predict their standard‐count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U‐Net network to reduce the noise of standard‐count PET images to obtain the virtual‐high‐count (VHC) PET images for identifying the potential benefits of the obtained VHC PET images. Methods The training datasets, including down‐sampled standard‐count PET images as the network input and high‐count images as the desired network output, were derived from 27 whole‐body PET datasets, which were acquired using 90‐min dynamic scan. The down‐sampled standard‐count PET images were rebinned with matched noise level of 195 clinical static PET datasets, by matching the normalized standard derivation (NSTD) inside 3D liver region of interests (ROIs). Cross‐validation was performed on 27 PET datasets. Normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and standard uptake value (SUV) bias of lesions were used for evaluation on standard‐count and VHC PET images, with real‐high‐count PET image of 90 min as the gold standard. In addition, the network trained with 27 dynamic PET datasets was applied to 195 clinical static datasets to obtain VHC PET images. The NSTD and mean/max SUV of hypermetabolic lesions in standard‐count and VHC PET images were evaluated. Three experienced nuclear medicine physicians evaluated the overall image quality of randomly selected 50 out of 195 patients' standard‐count and VHC images and conducted 5‐score ranking. A Wilcoxon signed‐rank test was used to compare differences in the grading of standard‐count and VHC images. Results The cross‐validation results showed that VHC PET images had improved quantitative metrics scores than the standard‐count PET images. The mean/max SUVs of 35 lesions in the standard‐count and true‐high‐count PET images did not show significantly statistical difference. Similarly, the mean/max SUVs of VHC and true‐high‐count PET images did not show significantly statistical difference. For the 195 clinical data, the VHC PET images had a significantly lower NSTD than the standard‐count images. The mean/max SUVs of 215 hypermetabolic lesions in the VHC and standard‐count images showed no statistically significant difference. In the image quality evaluation by three experienced nuclear medicine physicians, standard‐count images and VHC images received scores with mean and standard deviation of 3.34±0.80 and 4.26 ± 0.72 from Physician 1, 3.02 ± 0.87 and 3.96 ± 0.73 from Physician 2, and 3.74 ± 1.10 and 4.58 ± 0.57 from Physician 3, respectively. The VHC images were consistently ranked higher than the standard‐count images. The Wilcoxon signed‐...

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