Principal component analysis assisted deep learning method for motion artifacts correction in MR thermometry

Motivation: To improve the performance of convolutional neural network (CNN) for motion artifacts correction and demonstrate the feasibility of using motion-related information provided by principal component analysis (PCA). Goal(s): To achieve high accuracy of temperature mapping for motion existing organs like abdominal and thus expand a wide range of MR-thermometry in clinical applications. Approach: We proposed a combination method of PCA and basic CNN model to correct artifacts in abdominal MR-thermometry. Results: Preliminary results showed that the proposed method outperforms conventional CNN in terms of temperature mapping accuracy. The new method reduces the motion-related phase errors by leveraging PCA. Impact: Our proposed method has a high potential to handle motion organs with non-rigid motion. The PCA method in combination of CNN for its efficient reduction of motion induced errors may improve the feasibility and accessibility of MR-thermometry in abdominal applications.