Non-local texture learning approach for CT imaging problems using convolutional neural network

Sui, Li; Yao, Lisha; Zhu, Manman; Li, Danyang; Gao, Qi; Zhang, Xinyu; Zhong, Rikui; Bian, Zhaoying; Ma, Jianhua

doi:10.1117/12.2548949

Cited by 2 publications

(4 citation statements)

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“…It allows playback of scans as a video so that internal movement can be tracked. In this case, 3D [32,34,49,89,97,98,[117][118][119][120][121][122][123] and even 4D [59,120,[124][125][126] applications have been proposed. For example, in [120], the end-to-end DeepOrganNet framework was based on the three-variable tensor product deformation technology.…”

Section: Applications In Different Dimensionsmentioning

confidence: 99%

“…Gain spatial and temporal information in CT images [32,34,49,89,97,98,[117][118][119][120][121][122][123], [59, 120, 124- [180] proposed a Discriminative feature representation (DFR) approach with good adaptability to various CT systems because it can be directly applied to DICOM image without the need for raw measurement data. DFR outperformed iterative TV reconstruction in visual and quantitative results which showed its good robustness and performance.…”

Section: D/4dmentioning

confidence: 99%

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The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Zhang

Yang

Shi

2022

Complex Intell. Syst.

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Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative reconstruction (IR), which have been utilised widely in the image reconstruction process of computed tomography (CT) are not suitable in the case of low-dose CT applications, because of the unsatisfying quality of the reconstructed image and inefficient reconstruction time. Therefore, as the demand for CT radiation dose reduction continues to increase, the use of artificial intelligence (AI) in image reconstruction has become a trend that attracts more and more attention. This systematic review examined various deep learning methods to determine their characteristics, availability, intended use and expected outputs concerning low-dose CT image reconstruction. Utilising the methodology of Kitchenham and Charter, we performed a systematic search of the literature from 2016 to 2021 in Springer, Science Direct, arXiv, PubMed, ACM, IEEE, and Scopus. This review showed that algorithms using deep learning technology are superior to traditional IR methods in noise suppression, artifact reduction and structure preservation, in terms of improving the image quality of low-dose reconstructed images. In conclusion, we provided an overview of the use of deep learning approaches in low-dose CT image reconstruction together with their benefits, limitations, and opportunities for improvement.

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Section: Applications In Different Dimensionsmentioning

confidence: 99%

Section: D/4dmentioning

confidence: 99%

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Zhang

Yang

Shi

2022

Complex Intell. Syst.

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“…[2][3][4] Among them, the deep learning (DL)-based CT reconstruction methods have been widely developed, and achieved superior performance than the statistical iterative reconstruction methods. [5][6][7][8][9][10][11][12][13][14] The deep learning methods can be generally classified into categories, such as sinogram-domain DL-based methods, [5][6][7] image-domain DL-based methods [8][9][10] and dual-domain DL-based methods. [11][12][13][14] The sinogram-domain DL-based methods directly suppress noise in the sinogram domain, and then reconstruct the CT images from the filtered sinogram.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Chen et al 8 proposed a residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging. Li et al 10 proposed a convolutional neural network for low-dose CT reconstruction with non-local texture learning approach, which took into consideration the non-local features within the adjacent slices in 3D CT images. The dual-domain DL-based methods usually map the low-dose CT sinogram to the high-quality CT images via Radon transformation framework or statistical iterative reconstruction framework.…”

Section: Introductionmentioning

confidence: 99%

Dual-domain modulation for high-performance multi-geometry low-dose CT image reconstruction

Chen

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

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Computed Tomography (CT) is one of the most used imaging modality in clinics. However, the associated high radiation is a major concern, and then the tradeoff between clinically accepted CT images and radiation dose is desired. In recent years, inspired by deep learning techniques, various deep learning-based methods have been developed for low-dose CT imaging, and they have potential to improve image quality of low dose CT. Meanwhile, most of these methods are trained on the CT datasets with the specific imaging geometry, and they could fail to successfully reconstruct the CT images from the other imaging geometries simultaneously, which might lead to poor generalization ability. In this work, to address this issue, we propose a dual-domain modulation for high-performance multi-geometry low-dose CT image reconstruction (DM-MG) method. Specific, the proposed DM-MG consists of two sub-networks, i.e., multilayer perceptron controlling the multiple imaging geometries, and inverse Radon transform approximation sub-network reconstructing the CT images from the different geometries. Moreover, the inverse Radon transform approximation sub-network contains sinogram-domain filtering module, back-projection module, and image-domain filtering module, which maps the Radon transformation to the network. Finally, the proposed DM-MG can reconstruct the CT images from the different imaging geometries and dose levels simultaneously. In this work, the CT data from 2016 NIHAAPM-Mayo Clinic Low Dose CT Grand Challenge are used to validate and evaluate the reconstruction performance of the proposed DM-MG. Experimental results demonstrate that the presented DM-MG method can produce high-quality CT images from multiple geometries simultaneously and outperform the competing method in the quantitative assessments and visual inspection show that the dual-domain modulated model we propose provides better reconstruction of low-dose CT images under different imaging geometries compared to other modulated models.

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Non-local texture learning approach for CT imaging problems using convolutional neural network

Cited by 2 publications

References 0 publications

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Dual-domain modulation for high-performance multi-geometry low-dose CT image reconstruction

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