A non-stationary time-series modeling approach for CT image reconstruction from truncated data

Truncated data problems are encountered in computed tomographic (CT) scanning scenarios where it is desirable to restrict the radiation dosage to a region-of-interest (ROI) of the object cross-section being imaged. In this paper, we propose a new image reconstruction technique for handling truncated data based on projection data extrapolation using a non-stationary time-series modeling approach. A special case of the autoregressive integrated moving average (ARIMA) modeling is investigated and a new algorithmic approach for completing truncated data is proposed. The proposed scheme allows easy incorporation of parallel-beam data consistency conditions to improve the reconstructed image quality. We evaluate the performance of the proposed schemes against existing data completion techniques and also illustrate the validity of the approaches for clinically relevant images.