Deep-learning-derived input function in dynamic [18F]FDG PET imaging of mice.

Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep-learning-based prediction model (DLIF) that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [18F]Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the performance of a trained DLIF model on an external dataset consisting of 8 mouse scans where the input function was measured by continuous arterial blood sampling. The results showed that the predicted DLIF and image-derived targets were similar, and the net influx rate constants following from Patlak modeling using DLIF as input function were strongly correlated to the corresponding values obtained using the image-derived input function. There were somewhat larger discrepancies when evaluating the model on the external dataset, which could be attributed to systematic differences in the experimental setup between the two datasets. In conclusion, our non-invasive DLIF prediction method may be a viable alternative to arterial blood sampling in small animal [18F]FDG imaging. With further validation, DLIF could overcome the need for arterial cannulation and allow fully quantitative and longitudinal experiments in PET imaging studies of mice.

Theranostics in Neuroblastoma.

Theranostics combines diagnosis and targeted therapy, achieved by the use of the same or similar molecules labeled with different radiopharmaceuticals or identical with different dosages. One of the best examples is the use of metaiodobenzylguanidine (MIBG). In the management of neuroblastoma-the most common extracranial solid tumor in children. MIBG has utility not only for diagnosis, risk-stratification, and response monitoring but also for cancer therapy, particularly in the setting of relapsed/refractory disease. Improved techniques and new emerging radiopharmaceuticals likely will strengthen the role of nuclear medicine in the management of neuroblastoma.