SU-E-J-163: Suppressing Motion Related Artifacts in 4D-CT Or 4D-CBCT Reconstruction Using Projections and Digitally Reconstructed Projections: Proof of Principle




It has been always challenging to reconstruct good quality 4D-CT or 4D-CBCT when the data are under-sampled for each individual phase. For example, CBCT data is often acquired with limited number of projections and while it is sufficient to reconstruct decent motion averaged images; reconstructing a 4D image from this limited data leads to motion induced artifacts. However, inherent in the projection data are both components that capture the moving and non-moving parts of the image. In this work, we test the feasibility of using such a dataset to separate out the moving and non-moving components and then reconstruct individual phase images to acquire a complete 4D reconstruction with reduced artifacts.


To test our theory, we simulated a simple 2D+time respiratory phantom, and demonstrated the feasibility using a simple filtered backprojection algorithm along with the proposed motion suppression technique to reconstruct 2D+time images. However, the concepts can be expanded to 3D+time (i.e. 4D) datasets both using conventional CT or CBCT acquisition. The core of the technique rests on separating out the motion components from individual projection data by selecting the minimum intensity between projections and digitally reconstructed projections. This information is then later added back into the final images reconstructed at each individual phase to suppress motion artifacts.


Initial results on a 2D+time simulated phantom proves that severe under-sampling artifacts can be eliminated and individual phase images can be reconstructed with high fidelity when compared to standard under-sampled filtered backprojection.


The technique is successfully demonstrated using simple backprojection, and hence computational complexity is comparable to standard backprojection (for CT) or FDK (for CBCT). Computational complexity will be significantly better when compared to traditional iterative methods that use regularization and penalty functions.

NIH R01CA133539