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A Deep Learning-Based De-Artifact Diffusion Model for Removing Motion Artifacts in Knee MRI
Yingchun Li1, Tong Gong1, Qing Zhou2
1Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Background:
Motion artifacts are common for knee MRI, which usually lead to rescanning. Effective removal of motion artifacts would be clinically useful.
Purpose:
To construct an effective deep learning-based model to remove motion artifacts for knee MRI using real-world data.
Study Type:
Retrospective.
Subjects:
Model construction: 90 consecutive patients (1997 2D slices) who had knee MRI images with motion artifacts paired with immediately rescanned images without artifacts served as ground truth. Internal test dataset: 25 patients (795 slices) from another period; external test dataset: 39 patients (813 slices) from another hospital.
Field Strength/Sequence:
3-T/1.5-T knee MRI with T1-weighted imaging, T2-weighted imaging, and proton-weighted imaging.
Assessment:
A deep learning-based supervised conditional diffusion model was constructed. Objective metrics (root mean square error [RMSE], peak signal-to-noise ratio [PSNR], structural similarity [SSIM]) and subjective ratings were used for image quality assessment, which were compared with three other algorithms (enhanced super-resolution [ESR], enhanced deep super-resolution, and ESR using a generative adversarial network). Diagnostic performance of the output images was compared with the rescanned images.
Statistical Tests:
The Kappa Test, Pearson chi-square test, Fredman's rank-sum test, and the marginal homogeneity test. A p value < 0.05 was considered statistically significant.
Results:
Subjective ratings showed significant improvements in the output images compared to the input, with no significant difference from the ground truth. The constructed method demonstrated the smallest RMSE (11.44 5.47 in the validation cohort; 13.95 4.32 in the external test cohort), the largest PSNR (27.61 3.20 in the validation cohort; 25.64 2.67 in the external test cohort) and SSIM (0.97 0.04 in the validation cohort; 0.94 0.04 in the external test cohort) compared to the other three algorithms. The output images achieved comparable diagnostic capability as the ground truth for multiple anatomical structures.
Data Conclusion:
The constructed model exhibited feasibility and effectiveness, and outperformed multiple other algorithms for removing motion artifacts in knee MRI.
Evidence Level:
Level 3.
Technical Efficacy:
Stage 2.
Insights
A new deep learning model effectively removes motion artifacts from knee MRI scans, significantly improving image quality. This advanced technique matches the quality of rescanned images, reducing the need for repeat scans.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology
Background:
- Motion artifacts are a frequent issue in knee MRI, often necessitating rescanning.
- Effective artifact removal can significantly improve clinical utility and patient experience.
Purpose of the Study:
- To develop and validate a deep learning model for removing motion artifacts in knee MRI using real-world data.
- To assess the model's performance against existing algorithms and ground truth data.
Main Methods:
- A retrospective study utilizing 1997 knee MRI slices from 90 patients with paired artifact-affected and artifact-free images.
- Construction of a supervised conditional diffusion model trained on real-world knee MRI data.
- Evaluation using objective metrics (RMSE, PSNR, SSIM) and subjective image quality assessments, compared against three other algorithms.
Main Results:
- The deep learning model significantly improved image quality compared to input images, with no significant difference from ground truth.
- The model achieved superior performance with the lowest RMSE and highest PSNR and SSIM values compared to other algorithms.
- Output images demonstrated diagnostic performance comparable to artifact-free ground truth images.
Conclusions:
- The developed deep learning model is feasible and effective for removing motion artifacts in knee MRI.
- The model outperforms existing algorithms, offering a clinically valuable solution for improving MRI efficiency and quality.
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