An unsupervised patient-specific metal artifact reduction framework for proton therapy

Proton therapy can benefit chordoma treatment due to its capability of delivering highly conformal doses to the target area. However, clinical observations show that poor outcomes have been correlated to patients with metallic implants due to artifact contaminated computed tomography (CT) images. This study proposes a deep learning (DL)-based metal artifact reduction (MAR) framework to unsupervisely combine DL and k-means clustering algorithm and to obtain artifact-reduced images to support clinical applications. The framework includes three modules: data preprocessing, DL, and machine learning (ML) modules. An in-house material-based CT simulator was developed to augment the artifact training datasets. The DL module can adopt an unsupervised model to alleviate the need for labeled training data, which increases the clinical applicability. The ML module is used to classify tissue types from implant-free images around the adjacent treatment site, and the results can be used to further correct metal artifacts from DL images. We hypothesize that the artifact-reduced images should have similar CT number distributions to the adjacent CT images without surgical implants at the same treatment site. The Kullback-Leibler divergence (KLD) is used to evaluate the dissimilarity between these two distributions. The results indicate that the proposed method is comparative to Siemens iMAR and the current clinical procedure and can generate images with smaller KLD values than the images obtained by only using a DL model. The proposed method has the potential to reduce the treatment reserved margin that can decrease the radiation dose to normal tissues and accelerate the treatment planning time by revealing clear tumor structures.