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Blind CT image quality assessment via deep learning strategy: initial study
Author(s): Sui Li; Ji He; Yongbo Wang; Yuting Liao; Dong Zeng; Zhaoying Bian; Jianhua Ma
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Paper Abstract

Computed Tomography (CT) is one of the most important medical imaging modality. CT images can be used to assist in the detection and diagnosis of lesions and to facilitate follow-up treatment. However, CT images are vulnerable to noise. Actually, there are two major source intrinsically causing the CT data noise, i.e., the X-ray photo statistics and the electronic noise background. Therefore, it is necessary to doing image quality assessment (IQA) in CT imaging before diagnosis and treatment. Most of existing CT images IQA methods are based on human observer study. However, these methods are impractical in clinical for their complex and time-consuming. In this paper, we presented a blind CT image quality assessment via deep learning strategy. A database of 1500 CT images is constructed, containing 300 high-quality images and 1200 corresponding noisy images. Specifically, the high-quality images were used to simulate the corresponding noisy images at four different doses. Then, the images are scored by the experienced radiologists by the following attributes: image noise, artifacts, edge and structure, overall image quality, and tumor size and boundary estimation with five-point scale. We trained a network for learning the non-liner map from CT images to subjective evaluation scores. Then, we load the pre-trained model to yield predicted score from the test image. To demonstrate the performance of the deep learning network in IQA, correlation coefficients: Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are utilized. And the experimental result demonstrate that the presented deep learning based IQA strategy can be used in the CT image quality assessment.

Paper Details

Date Published: 7 March 2018
PDF: 5 pages
Proc. SPIE 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, 105771A (7 March 2018); doi: 10.1117/12.2293240
Show Author Affiliations
Sui Li, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Ji He, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Yongbo Wang, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Yuting Liao, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Dong Zeng, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Zhaoying Bian, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Jianhua Ma, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)


Published in SPIE Proceedings Vol. 10577:
Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)

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