Reconstructing volumetric Computed Tomography data from 2D X-ray projections with deep learning
However, patients are always exposed to a higher dose of radiation in a CT machine and it's generally more costly than a common X-ray exam.
Given the fact human anatomy are well constrained, we are interested to extract the maximum amount of organ information from a 2D X-ray projection with the help of machine learning.
Efforts have been made to reconstruct 3D CT scans from single- and biplanar X-rays with machine learning approaches.
Nevertheless, the paired Xray-CT datasets of all established approaches are expensive to collect and always misaligned.
In our project, we will investigate the feasibility of transferring 2D X-ray data to 3D CT volumes without paired information.
We would like to test the feasibility of transferring 2D X-ray data to 3D CT volumes without paired information.
Such a transition will be accomplished by training a deep neural network.
A large CT dataset, i.e., NLST, is a must to sufficiently train all parameters of our deep neural networks.
Dr. med. Dipl.-Phys. Daniel Truhn, UKA
Prof. Dr.-Ing. Volkmar Schulz, RWTH