The potential for reduced radiation dose from deep learning-based CT image reconstruction

Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional image iterative reconstruction methods are limited by the sampling theorem and also the blurring of projection data will propagate unhampered artifact in the reconstructed image. To overcome these problems, image restoration techniques should be developed to accurately correct a wide variety of image degrading effects in order to effectively improve image reconstruction. In this paper, a blind image restoration technique is embedded in the compressive sensing CT image reconstruction, which can result in a high-quality reconstruction image using fewer projection data. Because a small amount of data can be obtained by radiation in a shorter time, high-quality image reconstruction with less data is equivalent to reducing radiation dose. Technically, both the blurring process and the sparse representation of the sharp CT image are first modeled as a serial of parameters. The sharp CT image will be obtained from the estimated sparse representation. Then, the model parameters are estimated by a hierarchical Bayesian maximum posteriori formulation. Finally, the estimated model parameters are optimized to obtain the final image reconstruction. We demonstrate the effectiveness of the proposed method with the simulation experiments in terms of the peak signal to noise ratio (PSNR), and structural similarity index (SSIM).

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A Deep Recurrent Neural Network with Gated Momentum Unit for CT Image Reconstruction

10.36227/techrxiv.15066138 ◽

2021 ◽

Author(s):

Masaki Ikuta

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Reconstruction ◽

Image Data ◽

Observation Data ◽

Ct Image ◽

Image Study ◽

Data Set ◽

Image Domain ◽

Ct Image Reconstruction

<div><div><div><p>Many algorithms and methods have been proposed for Computed Tomography (CT) image reconstruction, partic- ularly with the recent surge of interest in machine learning and deep learning methods. The majority of recently proposed methods are, however, limited to the image domain processing where deep learning is used to learn the mapping from a noisy image data set to a true image data set. While deep learning-based methods can produce higher quality images than conventional model-based post-processing algorithms, these methods have lim- itations. Deep learning-based methods used in the image domain are not sufficient for compensating for lost information during a forward and a backward projection in CT image reconstruction especially with a presence of high noise. In this paper, we propose a new Recurrent Neural Network (RNN) architecture for CT image reconstruction. We propose the Gated Momentum Unit (GMU) that has been extended from the Gated Recurrent Unit (GRU) but it is specifically designed for image processing inverse problems. This new RNN cell performs an iterative optimization with an accelerated convergence. The GMU has a few gates to regulate information flow where the gates decide to keep important long-term information and discard insignificant short- term detail. Besides, the GMU has a likelihood term and a prior term analogous to the Iterative Reconstruction (IR). This helps ensure estimated images are consistent with observation data while the prior term makes sure the likelihood term does not overfit each individual observation data. We conducted a synthetic image study along with a real CT image study to demonstrate this proposed method achieved the highest level of Peak Signal to Noise Ratio (PSNR) and Structure Similarity (SSIM). Also, we showed this algorithm converged faster than other well-known methods.</p></div></div></div>

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Deep Learning with Adaptive Hyper-parameters for Low-Dose CT Image Reconstruction

IEEE Transactions on Computational Imaging ◽

10.1109/tci.2021.3093003 ◽

2021 ◽

pp. 1-1

Author(s):

Qiaoqiao Ding ◽

Yuesong Nan ◽

Hao Gao ◽

Hui Ji

Keyword(s):

Deep Learning ◽

Image Reconstruction ◽

Low Dose ◽

Ct Image ◽

Low Dose Ct ◽

Ct Image Reconstruction

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2.5D Deep Learning For CT Image Reconstruction Using A Multi-GPU Implementation

2018 52nd Asilomar Conference on Signals, Systems, and Computers ◽

10.1109/acssc.2018.8645364 ◽

2018 ◽

Cited By ~ 2

Author(s):

Amirkoushyar Ziabari ◽

Dong Hye Ye ◽

Somesh Srivastava ◽

Ken D. Sauer ◽

Jean-Baptiste Thibault ◽

...

Keyword(s):

Deep Learning ◽

Image Reconstruction ◽

Ct Image ◽

Ct Image Reconstruction ◽

Gpu Implementation

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Deep Encoder-Decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-View Data

Bioengineering ◽

10.3390/bioengineering6040111 ◽

2019 ◽

Vol 6 (4) ◽

pp. 111 ◽

Cited By ~ 4

Author(s):

Huidong Xie ◽

Hongming Shan ◽

Ge Wang

Keyword(s):

Image Reconstruction ◽

Radiation Dose ◽

Image Data ◽

Ct Image ◽

3D Ct ◽

X Ray ◽

3D Network ◽

Ct Image Reconstruction ◽

3D Information ◽

3D Volume

X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizing X-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dose has been an important topic in recent years. Few-view CT image reconstruction is one of the main ways to minimize radiation dose and potentially allow a stationary CT architecture. In this paper, we propose a deep encoder-decoder adversarial reconstruction (DEAR) network for 3D CT image reconstruction from few-view data. Since the artifacts caused by few-view reconstruction appear in 3D instead of 2D geometry, a 3D deep network has a great potential for improving the image quality in a data driven fashion. More specifically, our proposed DEAR-3D network aims at reconstructing 3D volume directly from clinical 3D spiral cone-beam image data. DEAR is validated on a publicly available abdominal CT dataset prepared and authorized by Mayo Clinic. Compared with other 2D deep learning methods, the proposed DEAR-3D network can utilize 3D information to produce promising reconstruction results.

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