2018
DOI: 10.1088/1361-6560/aacdd4
|View full text |Cite
|
Sign up to set email alerts
|

Computed tomography super-resolution using deep convolutional neural network

Abstract: The objective of this study is to develop a convolutional neural network (CNN) for computed tomography (CT) image super-resolution. The network learns an end-to-end mapping between low (thick-slice thickness) and high (thin-slice thickness) resolution images using the modified U-Net. To verify the proposed method, we train and test the CNN using axially averaged data of existing thin-slice CT images as input and their middle slice as the label. Fifty-two CT studies are used as the CNN training set, and 13 CT s… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

3
98
0
1

Year Published

2019
2019
2024
2024

Publication Types

Select...
8
1

Relationship

2
7

Authors

Journals

citations
Cited by 185 publications
(102 citation statements)
references
References 28 publications
3
98
0
1
Order By: Relevance
“…The peak signal-to-noise ratio and normalized root-mean-square error values were calculated as additional image quality metrics to quantify the similarity of attenuation and activity maps (30).…”
Section: Image Analysismentioning
confidence: 99%
“…The peak signal-to-noise ratio and normalized root-mean-square error values were calculated as additional image quality metrics to quantify the similarity of attenuation and activity maps (30).…”
Section: Image Analysismentioning
confidence: 99%
“…Table 1 exhibits the included studies regarding the use of AI for 3D imaging in DMFR. These studies focused on three main applications, including automated diagnosis of dental and maxillofacial diseases [16][17][18][19][20][28][29][30][31][32], localization of anatomical landmarks for orthodontic and orthognathic treatment planning [21,22,[33][34][35], and improvement of image quality [23,36].…”
Section: Current Use Of Ai For 3d Imaging In Dmfrmentioning
confidence: 99%
“…The balance between the radiation dose and CT image resolution is the biggest concern for radiologists. To address this issue, Park et al proposed a deep learning algorithm to enhance the thick-slice CT image resolution similar to that of a thin slice [36]. It is reported that the noise level of the enhanced CT images is even lower than the original images.…”
Section: Automated Improvement Of Image Qualitymentioning
confidence: 99%
“…[5] and Park et.al. [6] use convolutional neural networks to achieve SR. While the previous works attack the SR problem from the 2D perspective, others such as Chaudhari et.al.…”
Section: Related Workmentioning
confidence: 99%