In a recent paper published in EJNMMI Physics, our AI team at the University of Wisconsin proposed a deep learning algorithm to address the challenge for simultaneous accurate and dose-saving positron emission tomography (PET) imaging.
PET is a non-invasive imaging modality that directly provides biomarkers for physiology. Accurate PET activity is calculated by reconstructing the photon signal emitted from the PET tracer after correcting scattering and signal decay when photons travel through tissue. The conventional method to perform such a correction requires additional transmission images, such as computed tomography (CT) images, thus leading to additional radiation to patients. In this work, our AI team invented a data-driven method to correct PET signals using a pseudo-CT image generated from the PET image itself using deep learning. This novel technique avoids acquiring additional transmission images, reduces the radiation dose, and increases imaging robustness against the subject motion. Meanwhile, our experiment in brain imaging demonstrated almost no significant difference between our deep learning method and clinical standard methods, with an average deviation of less than 1% in most brain regions.
| Raw PET image | Deep learning pseudo-CT image for PET correction | Real CT image |