A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction

Abstract

Purpose: Due to the potential risk of inducing cancer, radiation exposure by X‐ray CT devices should be reduced for routine patient scanning. However, in low‐dose X‐ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis. Thus, a high‐quality reconstruction method from low‐dose X‐ray CT data has become a major research topic in the CT community. Conventional model‐based de‐noising approaches are, however, computationally very expensive, and image‐domain de‐noising approaches cannot readily remove CT‐specific noise patterns. To tackle these problems, we want to develop a new low‐dose X‐ray CT algorithm based on a deep‐learning approach. Method: We propose an algorithm which uses a deep convolutional neural network (CNN) which is applied to the wavelet transform coefficients of low‐dose CT images. More specifically, using a directional wavelet transform to extract the directional component of artifacts and exploit the intra‐ and inter‐ band correlations, our deep network can effectively suppress CT‐specific noise. In addition, our CNN is designed with a residual learning architecture for faster network training and better performance. Results: Experimental results confirm that the proposed algorithm effectively removes complex noise patterns from CT images derived from a reduced X‐ray dose. In addition, we show that the wavelet‐domain CNN is efficient when used to remove noise from low‐dose CT compared to existing approaches. Our results were rigorously evaluated by several radiologists at the Mayo Clinic and won second place at the 2016 “Low‐Dose CT Grand Challenge.” Conclusions: To the best of our knowledge, this work is the first deep‐learning architecture for low‐dose CT reconstruction which has been rigorously evaluated and proven to be effective. In addition, the proposed algorithm, in contrast to existing model‐based iterative reconstruction (MBIR) methods, has considerable potential to benefit from large data sets. Therefore, we believe that the proposed algorithm opens a new direction in the area of low‐dose CT research.