A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images

doi:10.12122/j.issn.1673-4254.2024.06.22

Abstract

Abstract:

Objective We propose a motion artifact correction algorithm (DMBL) for reducing motion artifacts in reconstructed dental cone-beam computed tomography (CBCT) images based on deep blur learning. Methods A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion, and the obtained motion degradation features were imported in the artifact correction module for artifact removal. The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns. Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets. Results The experimental results with the simulated dataset showed that compared with the existing methods, the PSNR of the proposed method increased by 2.88%, the SSIM increased by 0.89%, and the RMSE decreased by 10.58%. The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417 (in a 5-point scale), significantly higher than those of the comparison methods. Conclusion The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

Key words: motion artifact reduction, dental cone-beam computed tomography, blur learning

Zongyue LIN, Yongbo WANG, Zhaoying BIAN, Jianhua MA. A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images[J]. Journal of Southern Medical University, 2024, 44(6): 1198-1208.

Figures/Tables 13

Fig.1 Flowchart of motion artifact simulation in dental cone-beam computed tomography (CBCT).

Fig.2 Coordinate system in CBCT.

Fig.3 Deep motion blur learning for motion atifact reduction (DMBL) in dental CBCT images. E: Blur-Encoder; G: Deblur-Generator.

Fig.5 Comparison between real and simulated motion artifacts. A, B: Real moving images. C, D: Simulated moving images.

Fig.6 Transverse plane results of motion artifact reduction for the simulated data. Display windows: C=400 HU, W=3000 HU.

Fig.7 Profile comparison among different methods along the Red line in Fig.6.

Fig.8 Coronal and sagittal plane results of motion artifact reduction for the simulated data. Display windows: C=400 HU, W=3000 HU.

Tab.1 Quantitative comparison of restoration of simulated motion data using different methods

Methods	Uncorrected	U-net	WGAN	HINet	Restormer	Ours
RMSE	7.7350	9.5548	7.7357	6.2869	5.5503	4.9629
PSNR	30.4728	28.8756	30.4767	32.2988	33.3387	34.2981
SSIM	0.9130	0.9176	0.9017	0.9391	0.9476	0.9560

Fig.9 Comparison of the results of motion artifact reduction for clinical data Case 1. Display windows: C=400 HU, W=3000 HU.

Fig.10 Comparison of the results of motion artifact reduction for clinical data Case 2. Display windows: C=400 HU, W=3000 HU.

Tab.2 Overall image quality score statistics

Methods

Scores (Mean±SD)

U-net

WGAN

HINet

Restormer

Ours

2.250±0.778

2.208±0.762

3.083±0.759

3.667±0.687

4.417±0.571

<0.001

Fig.11 Results of verification of the joint learning framework.

Tab.3 Quantitative comparison of verification experiment results of the joint learning framework

Methods	Uncorrected	Restormer	Ours
RMSE	8.7842	6.5889	5.8347
PSNR	0.9052	0.9406	0.9498
SSIM	29.6934	32.2733	33.2851

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