![]() Aggarwal HK Mani MP Jacob M Modl: Model-based deep learning architecture for inverse problems IEEE Trans Med Imaging 2018 38 2 394 405 10.1109/TMI.2018.2865356 Google Scholar Abella M MartÃnez C Desco M Vaquero JJ Fessler JA Simplified statistical image reconstruction for x-ray ct with beam-hardening artifact compensation IEEE Trans Med Imaging 2020 39 1 111 118 10.1109/TMI.2019.2921929 Google Scholar Abdurahman S Frysch R Bismark R Melnik S Beuing O Rose G Beam hardening correction using cone beam consistency conditions IEEE Trans Med Imaging 2018 37 10 2266 2277 10.1109/TMI.2018.2840343 Google Scholar It has been observed that the CDNN has improved the reconstruction quality by reducing streak, ring artifacts, and beam hardening artifacts and also preserving the profound structures. The performance of the proposed CDNN has been tested with real-life data having beam hardening artifacts. It has been found from the experiments that the CDNN suppresses the artifacts and improves the reconstruction. The proposed approach has improved the image quality as compared to U-Net and the other state-of-the-art methods. The proposed approach is comparable to other hardware/software solutions for aforesaid purpose and does not require any extra hardware. A novel approach for reduction of beam-hardening artifacts in case of limited-angles computed tomography using CDNN has been presented. Image reconstructed from Fourier transform-based approach has been used as a prior. The stochastic gradient descent optimization method has been used for training the network. The network has been designed as a forward model. The network has skip-connections for better learning of features between input and output. The CDNN architecture has convolution neural network blocks that include convolution layers, rectified linear units ReLU, and batch normalization layers. This manuscript has presented a cascaded encoder-decoder architecture named cascaded deep neural network for image reconstruction (CDNN). The present manuscript proposes artificial intelligence based software solution for the beam hardening artifacts removal. Most of the solutions are hardware based and need extra hardware to remove the beam hardening artifacts. The state-of-the-art approaches available in the literature have proposed the solutions for beam-hardening artifacts correction in full span computed tomography. Also, the poly-chromatic nature of the X-ray adds beam-hardening artifacts in the reconstruction. Besides, image reconstruction with limited-angles projection data distorts the image, thus emasculating the efficiency of diagnosis. The amount of radiation associated with CT induces health implications to the patient. Image reconstruction with limited angles projection data is a challenging task in computed tomography (CT).
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