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APPARATUS AND METHOD FOR DUAL-ENERGY COMPUTED TOMOGRAPHY (CT) IMAGE RECONSTRUCTION USING SPARSE KVP-SWITCHING AND DEEP LEARNING
APPARATUS AND METHOD FOR DUAL-ENERGY COMPUTED TOMOGRAPHY (CT) IMAGE RECONSTRUCTION USING SPARSE KVP-SWITCHING AND DEEP LEARNING
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机译:使用稀疏KVP切换和深度学习重建双能CT图像的设备和方法
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摘要
A deep learning (DL) network reduces artifacts in computed tomography (CT) images based on complementary sparse-view projection data generated from a sparse kilo-voltage peak (kVp)-switching CT scan. The DL network is trained using input images exhibiting artifacts and target images exhibiting little to no artifacts. Another DL network can be trained to perform image-domain material decomposition of the artifact-mitigated images by being trained using target images in which beam hardening is corrected and spatial variations in the X-ray beam are accounted for. Further, material decomposition and artifact mitigation can be integrated in a single DL network that is trained using as inputs reconstructed images having artifacts and as targets material images without artifacts with beam-hardening corrections, etc. Further, the target material images can be transformed using a whitening transform to decorrelate noise.
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