4D Dynamic Brain PET Prediction Using Anatomical and Statistical Models

Motivation: The reduction in PET scan duration not only improves the efficiency of the scanning process but also contributes to a more comfortable experience for patients. Goal(s): Leveraging the temporal models in conjunction with previously predicted weights of PCs, we aim to reconstruct entire 4D dynamic PET frames using an inverse PCA method. Approach: A novel technique has been developed to generate pseudo-T1 images from noisy 4D PET data, as well as the reverse process, obtaining the initial components of 4D dynamic PET images from MRI data. Results: The results endorsed that only 5 minutes observation is enough to predict whole 70 minute data. Impact: We predicted later PET frames from noisy initial frames using a novel approach combining anatomical and statistical temporal PCs from MRI data. This method has clinical potential for insights into dynamic processes, radiation reduction, and identifying abnormalities in medical imaging.