Related Experiment Video
Updated: Jun 22, 2025
06:56
Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
Published on: January 7, 2021
2.4K
Iterative Motion Correction Technique with Deep Learning Reconstruction for Brain MRI: A Volunteer and Patient Study
Koichiro Yasaka1,2, Hiroyuki Akai2,3, Shimpei Kato3
1Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Journal of Imaging Informatics in Medicine
|June 28, 2024
Summary
Iterative motion correction (IMC) significantly reduces motion artifacts in brain MRI scans reconstructed with deep learning reconstruction (DLR). This technique improves overall image quality without compromising similarity to motionless scans.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Motion artifacts are a common problem in brain MRI, degrading image quality and potentially affecting diagnosis.
- Deep learning reconstruction (DLR) offers advanced image reconstruction but can still be susceptible to motion-induced artifacts.
- Iterative motion correction (IMC) is a technique aimed at mitigating these artifacts.
Purpose of the Study:
- To evaluate the effectiveness of iterative motion correction (IMC) in reducing motion artifacts in brain MRI.
- To assess the impact of IMC on image quality when combined with deep learning reconstruction (DLR).
- To compare quantitative and qualitative measures of image quality with and without IMC.
Main Methods:
- Brain MRI scans (FLAIR sequence) were acquired from 10 volunteers (motionless and self-induced motion) and 30 patients.
- Images were reconstructed using deep learning reconstruction (DLR) with and without iterative motion correction (IMC).
- Quantitative analysis used the Structural Similarity Index Measure (SSIM); qualitative analysis involved blinded reader evaluation of motion artifacts, noise, and overall quality.
Main Results:
- Quantitative analysis showed a significantly higher SSIM for IMC-on images (0.952) compared to IMC-off images (0.949) (p < 0.001).
- Qualitative analysis revealed that all three blinded readers found motion artifacts and overall image quality to be significantly better with IMC-on (p < 0.001).
- While two readers noted increased noise with IMC-on (p < 0.001), the reduction in motion artifacts and improvement in overall quality were deemed more significant.
Conclusions:
- Iterative motion correction (IMC) is effective in reducing motion artifacts in brain FLAIR DLR images.
- IMC preserves the similarity of reconstructed images to motionless scans.
- The combination of IMC and DLR enhances overall image quality in brain MRI, making it a valuable technique for clinical practice.
