Positron emission tomography (PET) combined with computed tomography (CT), referred to as PET/CT, is an indispensable malignancy diagnostic device in hospital radiology departments(1–4). The occurrence of malignant tumor is not localized, but often a systemic disease(5–7). Therefore, PET-CT generally takes whole-body scan, which can discover not only the primary site lesion, but also the presence of metastatic lesions in soft tissue organs and bones in various parts of the body, which is very helpful for the staging of tumor and determining the scope of metabolically active lesions, and provides accurate information on the site of puncture or tissue biopsy. It provides more reasonable and accurate positioning for radiation therapy (especially precision radiotherapy) and reduces the side effects of treatment(4). PET uses radioactive tracers, special cameras and computers to image tracer distribution and evaluate organ and tissue functions. Typically, the tracer administration activity and event signal acquisition time are positively correlated with the imaging quality(8, 9). However, highly active tracers will increase the risk of secondary cancer in patients, and longer acquisition times may introduce autonomic or involuntary patient motion artifacts into the images. Therefore, the activity of radiotracer administration and the time duration of data collection are often restricted by radiation safety and tolerability(3, 10–12). Compared with adults, children are more sensitive to ionizing radiation, and the effective dose absorbed can reach 4-5 times that of adults(10). Pediatric patients who are exposed to radiation at an early stage have a higher risk of developing malignant tumors because their bodies are developing and their life expectancy is longer(11, 13, 14). In addition, the lack of self-control ability in children is also a problem. The PET data acquisition process is relatively time-consuming compared to CT. The movement of the child during the event signal acquisition will cause image artifacts, can blur the image, and cause difficulty in making the diagnosis(15). Therefore, in pediatric nuclear medicine, it is important to minimize the dose of the radiotracer that is administered as well as the acquisition time of the event signal.
The typical PET axial field-of-view (FOV) is 20 cm, and when performing a whole-body PET scan, data needs to be collected from multiple locations, over 85% of the body at each location lies outside the scanner’s FOV, and signals from these areas of the body cannot be collected, yielding less than 1% sensitivity to the signal, making it very difficult to achieve an overall dose reduction(16, 17). Ultra-long axial FOV is regarded as a new generation of PET technology that can fully improve signal sensitivity(17). Recently, a long-axis FOV PET scanner was introduced (3, 18, 19). This PET scanner, called uEXPLORER (uEXPLORER, United Imaging Healthcare, Shanghai, China), has an axial FOV of 194 cm and can record coupled photons from the entire body simultaneously, hence the name total-body PET imaging. It increases the effective sensitivity by a factor of approximately 40 with respect to the 20 cm axial FOV, thus allowing conventional image quality to be achieved using lower injection concentrations and shorter acquisition times(11, 16, 20). Based on this platform, artificial intelligence techniques are used to explore the low-dose limit of rapid scanning achievable by current PET devices, which is of clinical importance, especially for pediatric PET diagnosis.
In this retrospective study of pediatric data, we aim to use artificial intelligence (AI) techniques (CNNs, convolutional neural networks) to further perform ultralow-dose image recovery on a total-body PET/CT system that already has the advantage of using a low dose. CNNs based on multimodal data fusion have been shown to combine the advantages of the data of each modality, which can effectively and significantly reduce the dose(21–24). We will use the accompanying CT images as a prior knowledge to enhance the anatomical information of the images. The enhanced synthetic network we adopted takes the residual module as the main framework, introduces the high-dimensional information of the prior CT at different scales, and uses the perceptual loss to ensure the effective restoration of the structure, and uses the simulated annealing training strategy to speed up the training process(25–27). The experimental results show that the CNN can effectively synthesize images from the ultralow-dose images and can reach the clinical diagnosis level, and the network model has an improved performance in the anatomical structure recovery with the introduction of CT prior image information.