Feature aware deep learning CT image reconstruction

In conventional CT, it is difficult to generate consistent organ specific noise and resolution with a single reconstruction kernel. Therefore, it is necessary in principle to reconstruct a single scan multiple times using different kernels in order to obtain clinical diagnosis information for different anatomies. In this paper, we provide a deep learning solution which can obtain organ specific noise and resolution balance with one single reconstruction. We propose image reconstruction using a deep convolution neural network (DCNN) trained by a specific feature aware reconstruction target. It integrates desirable features from multiple reconstructions each of which provides optimal noise and resolution tradeoff for one specific anatomy. The performance of our proposed method has been verified with actual clinical data. The results show that our method can outperform standard model based iterative reconstruction (MBIR) by offering consistent noise and resolution properties across different organs using only one single image reconstruction.