Med Phys. 2022 Jul 26. doi: 10.1002/mp.15867. Online ahead of print.
ABSTRACT
PURPOSE: Recently, deep learning-based methods have been established to denoise the low-count 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-minute 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 lessons were used for evaluation on standard-count and VHC PET images, with real-high-count PET image of 90 minutes 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 virtual high-count 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 radiologists, 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-rank test also indicated that the image quality evaluation between standard-count and VHC images had significant difference.
CONCLUSIONS: A deep learning method is proposed to convert the standard-count images to the VHC images. The VHC images have reduced noise level. No significant difference in mean/max SUV to the standard-count images is observed. VHC images improve image quality for better lesion detectability and clinical diagnosis. This article is protected by copyright. All rights reserved.
PMID:35880541 | DOI:10.1002/mp.15867
