3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions

In this paper, we introduced a novel deep learning based reconstruction technique using the correlations of all 3 dimensions with each other by taking into account the correlation between 2-dimensional low-dose CT images. Sparse or noisy sinograms are back projected to the image domain with FBP operation, then denoising process is applied with a U-Net like 3 dimensional network called 3D U-NetR. Proposed network is trained with synthetic and real chest CT images, and 2D U-Net is also trained with the same dataset to prove the importance of the 3rd dimension. Proposed network shows better quantitative performance on SSIM and PSNR. More importantly, 3D U-NetR captures medically critical visual details that cannot be visualized by 2D network.

Self-Supervised Training For Low Dose CT Reconstruction

Ionizing radiation has been the biggest concern in CT imaging. To reduce the dose level without compromising the image quality, low dose CT reconstruction has been offered with the availability of compressed sensing based reconstruction methods. Recently, data-driven methods got attention with the rise of deep learning, the availability of high computational power, and big datasets. Deep learning based methods have also been used in low dose CT reconstruction problem in different manners. Usually, the success of these methods depends on clean labeled data. However, recent studies showed that training can be achieved successfully without clean datasets. In this study, we defined a training scheme to use low dose sinograms as their own training targets. We applied the self-supervision principle in the projection domain where the noise is element-wise independent as required in these methods. Using the self-supervised training, the filtering part of the FBP method and the parameters of a denoiser neural network are optimized. We demonstrate that our method outperforms both conventional and compressed sensing based iterative reconstruction methods qualitatively and quantitatively in the reconstruction of analytic CT phantoms and real-world CT images in low dose CT reconstruction task.