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A novel deep learning-based method for monochromatic image synthesis from spectral CT using photon-counting detectors

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Document pages: 9 pages

Abstract: With the growing technology of photon-counting detectors (PCD), spectral CTis a widely concerned topic which has the potential of materialdifferentiation. However, due to some non-ideal factors such as cross talk andpulse pile-up of the detectors, direct reconstruction from detected spectrumwithout any corrections will get a wrong result. Conventional methods try tomodel these factors using calibration and make corrections accordingly, butdepend on the preciseness of the model. To solve this problem, in this paper,we proposed a novel deep learning-based monochromatic image synthesis methodworking in sinogram domain. Different from previous deep learning-based methodsaimed at this problem, we designed a novel network architecture according tothe physical model of cross talk, and it can solve this problem better in aningenious way. Our method was tested on a cone-beam CT (CBCT) system equippedwith a PCD. After using FDK algorithm on the corrected projection, we got quitemore accurate results with less noise, which showed the feasibility ofmonochromatic image synthesis by our method.

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