Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network

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Abstract

<p class="first" id="P1">Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction algorithms are one of the most promising way to compensate for the increased noise due to reduction of photon flux. Most iterative reconstruction algorithms incorporate manually designed prior functions of the reconstructed image to suppress noises while maintaining structures of the image. These priors basically rely on smoothness constraints and cannot exploit more complex features of the image. The recent development of artificial neural networks and machine learning enabled learning of more complex features of image, which has the potential to improve reconstruction quality. In this work, K-sparse autoencoder (KSAE) was used for unsupervised feature learning. A manifold was learned from normal-dose images and the distance between the reconstructed image and the manifold was minimized along with data fidelity during reconstruction. Experiments on 2016 Low-dose CT Grand Challenge were used for the method verification, and results demonstrated the noise reduction and detail preservation abilities of the proposed method. </p>

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Most cited references 19

Record: found
Abstract: not found
Article: not found

$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

M. Aharon, M Elad, A. Bruckstein (2006)

0 comments Cited 1010 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

Extracting and composing robust features with denoising autoencoders

Pascal Vincent, Hugo Larochelle, Yoshua Bengio … (2008)

0 comments Cited 787 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Deep Convolutional Neural Network for Inverse Problems in Imaging.

Kyong Hwan Jin, Michael Unser, Emmanuel Froustey … (2017)

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise nonlinearity) when the normal operator ( H*H where H* is the adjoint of the forward imaging operator, H ) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 x 512 image on the GPU.