![]() High-energy data captured using PCDs have been shown to reduce metal artifacts in reconstructions due to reduced beam hardening. PCDs offer the ability to distinguish the energy of incident x-rays and sort them in a set number of energy bins. Metal artifact reduction (MAR) methods continue to be proposed and photon-counting detectors (PCDs) have recently been the subject of research towards this purpose. Metal artifacts have been an outstanding issue in computed tomography (CT) since its first uses in the clinic and continue to interfere. 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). ![]()
0 Comments
Leave a Reply. |