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Computed tomography for region-of-interest (ROI) reconstruction hasadvantages of reducing X-ray radiation dose and using a small detector.However, standard analytic reconstruction methods suffer from severe cuppingartifacts, and existing model-based iterative reconstruction methods requireextensive computations. Recently, we proposed a deep neural network to learnthe cupping artifact, but the network is not well generalized for differentROIs due to the singularities in the corrupted images. Therefore, there is anincreasing demand for a neural network that works well for any ROI sizes. Inthis paper, two types of neural networks are designed. The first type learnsROI size-specific cupping artifacts from the analytic reconstruction images,whereas the second type network is to learn to invert the finite Hilberttransform from the truncated differentiated backprojection (DBP) data. Theirgeneralizability for any ROI sizes is then examined. Experimental results showthat the new type of neural network significantly outperforms the existingiterative methods for any ROI size in spite of significantly reduced run-timecomplexity. Since the proposed method consistently surpasses existing methodsfor any ROIs, it can be used as a general CT reconstruction engine for manypractical applications without compromising possible detector truncation.

One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection