Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal
This work addresses cone-beam artifact removal in CT imaging, offering a faster alternative to iterative methods, though it is incremental as it builds on existing deep learning and domain-specific techniques.
The paper tackles cone-beam artifact removal in CT imaging by developing a deep learning approach that operates in the differentiated backprojection domain, combining reconstruction results with spectral blending to outperform existing iterative methods while reducing runtime complexity.
Conebeam CT using a circular trajectory is quite often used for various applications due to its relative simple geometry. For conebeam geometry, Feldkamp, Davis and Kress algorithm is regarded as the standard reconstruction method, but this algorithm suffers from so-called conebeam artifacts as the cone angle increases. Various model-based iterative reconstruction methods have been developed to reduce the cone-beam artifacts, but these algorithms usually require multiple applications of computational expensive forward and backprojections. In this paper, we develop a novel deep learning approach for accurate conebeam artifact removal. In particular, our deep network, designed on the differentiated backprojection domain, performs a data-driven inversion of an ill-posed deconvolution problem associated with the Hilbert transform. The reconstruction results along the coronal and sagittal directions are then combined using a spectral blending technique to minimize the spectral leakage. Experimental results show that our method outperforms the existing iterative methods despite significantly reduced runtime complexity.