Learning Image From Projection: A Full-Automatic Reconstruction (FAR) Net for Computed Tomography

Learning Image From Projection: A Full-Automatic Reconstruction (FAR) Net for Computed Tomography

Blog Article

The x-ray computed tomography (CT) is essential for medical diagnosis and industrial nondestructive testing.The aim of CT is to recover or reconstruct image from projection data.However, in particular, the MENO-PREV reconstructed image usually suffers from complex artifacts and noise, such as the sampling is insufficient or low-dose CT.

In order to deal with such issues and achieve reconstruction, a full automatic reconstruction (FAR) net is proposed for CT reconstruction via deep learning technique.Different with the usual network in deep learning reconstruction, the proposed neural network is an end-to-end network by which the image is predicted directly from projection data.The main challenge for such a FAR-net is the space complexity of the CT reconstruction in full-connected (FC) network.

For a CT image with the size N × N, a typical requirement of memory space for the image reconstruction is O(N4), for which is unacceptable by conventional calculation device, e.g.GPU workstation.

In this paper, we utilize a series of smaller fully connected layers (FCL) to replace the huge Radon transform matrix based on the sparse nonnegative matrix factorization (SNMF) theory.By applying such an approach, the FAR-net is able to reconstruct images with the One-of-a-kind Hair-On Hides size 512×512 on only single workstation.The results of numerical experiments show that the projection matrix and the FAR-net is able to reconstruct the CT image from projection data with a superior quality to conventional methods such as optimization based approach.

Meanwhile, the factorization for the inverse projection matrix is validated in simulation and real experiments.