Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction

Respiratory motion has to be corrected in PET/CT imaging for precise tumor detection and quantification. The optimal motion correction methods for regular breathers and irregular breathers could be different. In this study, we developed deep learning based methods to automatically classify patient breathing patterns and investigated the impact of breathing pattern variability on gating performance. We implemented a hybrid neural network consisting of convolutional (Conv) layers, recurrent layers (LSTM, long short-term memory) and a linear classifier to differentiate breathing patterns. 1295 respiratory traces collected using RPM (Real-time Position Management) system were used for training and testing, as well as additional traces acquired using the Anzai system. We optimized the deep neural network with respect to data preprocessing, augmentation, weighted loss function and generalization capability. The results showed that the proposed deep learning model has reached a high prediction accuracy, with a sensitivity of 92.0% and a specificity of 91.8%. Using phase gating approach, for regular breathers, end-expiration phase gating can effectively reduce the respiratory motion. In contrast, for irregular breathers, larger amount of intra-gate motion was present in the gated PET/CT images and more sophisticated motion correction methods are required.