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A Physics-Motivated DNN for X-Ray CT Scatter Correction
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A Physics-Motivated DNN for X-Ray CT Scatter Correction
The scattering of photons by the imaged object in X-ray computed tomography (CT) produces degradations of the reconstructions in the form of streaks, cupping, shading artifacts and decreased contrast. We describe a new physics-motivated deep-learning-based method to estimate scatter and correct for it in the acquired projection measurements. The method incorporates both an initial reconstruction and the scatter-corrupted measurements using a specific deep neural network architecture and a cost function tailored to the problem. Numerical experiments show significant improvement over a recent projection-based deep neural network method.
The scattering of photons by the imaged object in X-ray computed tomography (CT) produces degradations of the reconstructions in the form of streaks, cupping, shading artifacts and decreased contrast. We describe a new physics-motivated deep-learning-based method to estimate scatter and correct for it in the acquired projection measurements. The method incorporates both an initial reconstruction and the scatter-corrupted measurements using a specific deep neural network architecture and a cost function tailored to the problem. Numerical experiments show significant improvement over a recent projection-based deep neural network method.
