Related Experiment Video
Updated: Nov 16, 2025
Cryo-Structured Illumination Microscopic Data Collection from Cryogenically Preserved Cells
Published on: May 28, 2021
[CT image quality assessment based on prior information of pre-restored images]
1School of Biomedical Engineering, Southern Medical University; Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Guangzhou 510515, China.
Objective:
We propose a CT IQA strategy based on the prior information of pre-restored images (PR-IQA) to improve the performance of IQA models.
Objective:
We propose a CNN-based no-reference CT IQA strategy using the prior information of image quality features in the image restoration algorithm, which is combined with the original distorted image information into the two CNNs through the pre-restored image and the residual image. Multi-information fusion was used to improve the feature extraction ability and prediction performance of CNN. We built a CT IQA dataset based on spiral CT data published by Mayo Clinic. The performance of PR- IQA was evaluated by calculating the quantitative metrics and statistical tests. The influence of different hyperparameter settings for PR-IQA was analyzed. We then compared PR-IQA with the BASELINE model based on the single CNN to evaluate the original distorted image without reference image and other eight IQA algorithms.
Objective:
The comparative experiment results showed that the PR-IQA model based on the prior information of 3 different image restoration algorithms (BF, NLM and BM3D) was better than all the tested IQA algorithms. Compared with the BASELINE method, the proposed method showed significantly improved performance, and the mean PLCC was increased by 12.56% and SROCC by 19.95%, and RMSE was decreased by 22.77%.
Objective:
The proposed PR-IQA method can make full use of the prior information of the image restoration algorithm to effectively predict the quality of CT images.
