Reinforcement Learning for Object Detection in PET Imaging

Nuclear Medicine (NM) Imaging serves as a powerful technique in visualizing radio-pharmaceutically targeted physiological processes allowing non-invasive monitoring of disease, it's progression and therapy response. Positron Emission Tomography (PET) imaging has become a cornerstone in oncology and is utilized in disease staging, monitoring response to therapy, detecting recurrence and predicting prognosis. Artificial intelligence is increasingly being adopted to assist radiological interpretation of PET images. In this work, we for the first time set instance object detection in PET imaging in a reinforcement learning (RL) paradigm. Q learning, a model free RL learning algorithm, is used to learn a policy which tells an agent what actions to take to maximize a reward in an environment in order to achieve a specific task. In this work Q learning with a novel reward function defined by the Kullback-Leibler (KL) divergence is used to detect instances of an object in PET images. The RL agent is tested using a phantom study and accurately identifies the location of all five spheres; producing a mean error of 1.5 voxels. Testing is also performed on two 18F-fluorodeoxyglucose investigations imaged for oesophageal cancer. The location of the cancer lesion determined by the RL agent on a sagittal cross section is in excellent correspondence to the location defined by an expert radiologist, with a mean error of 2 voxels. The RL framework thus proves promise for automated object detection in PET imaging and providers the first example of it's use in Nuclear Medicine imaging studies.