Does Prior Knowledge in the Form of Multiple Low-dose PET Images (at Different Dose Levels) Improve Standard-dose PET Prediction?

To acquire a high-quality PET image, a standard dose of radioactive tracer is injected into the patient which may pose a high risk of radiation exposure damage. On the other hand, reducing the injected dose increases the statistical noise within the PET images. To improve the image quality of low-dose PET (L-PET) images, deep learning methods have been introduced to denoise the L-PET images, wherein the relationship between the L-PET images and the standard-dose PET (S-PET) images is learned by the model to predict the SPET images from their low-dose counterparts. The existing deep learning-based approaches solely focus on a single level of L-PET imaging to predict the S-PET images. In this work, we investigate the benefits of exploiting multiple PET images at lower dose levels (in addition to the target low-dose level) as prior knowledge to predict the S-PET images. To this end, a high-resolution residual deep learning network was employed to develop two S-PET prediction models. First, the network was trained using a single input channel for 8% L-PET images. In the second model, multiple L-PET images (6% and 4%, in addition to 8% L-PET) were considered as inputs to the network. The performance of the two models was evaluated using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE) of standard uptake value (SUV) within the entire head region. Moreover, mean SUV bias (SUVmean) was calculated for the malignant lesions. The quantitative analysis of 20 patients in the external validation dataset demonstrated the superior performance of the multi-input model. The RMSE within the entire head region reduced from 0.12±0.04 in 8% L-PET, to 0.09±0.03 and 0.06±0.02 for the single- and multi-input models, respectively. Moreover, the SUVmean bias reduced from -4.18±1.14% in the single-input model to -1.44±0.56% in the multi-input model. This study demonstrated the benefits of using multiple L-PET images to estimate the S-PET images.