Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests
Benedikt Lorch1,2, Ghislain Vaillant2, Christian Baumgartner2
1Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nürnberg, 91058 Erlangen, Germany.
Abstract:
The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.
Insights
Patient motion during Magnetic Resonance (MR) scans causes image artifacts. This study shows a machine learning approach can effectively detect these motion artifacts in MR images, improving diagnostic quality.
Area of Science:
- Medical Imaging
- Machine Learning
- Image Reconstruction
Background:
- Magnetic Resonance (MR) scan acquisition times often exceed patient stillness, leading to motion artifacts.
- Patient motion, including bulk and respiratory motion, significantly degrades MR image quality and diagnostic value through artifacts like ghosting, blurring, and smearing.
Purpose of the Study:
- To investigate the impact of motion on reconstructed MR slices.
- To develop and evaluate a supervised learning method for detecting motion artifacts in MR image reconstruction.
Main Methods:
- A supervised learning approach using random decision forests was employed.
- The study analyzed bulk patient motion in head scans and respiratory motion in cardiac scans.
- Synthetic MR images with introduced motion artifacts were evaluated across Cartesian, radial, and spiral k-space sampling patterns.
Main Results:
- The machine learning model demonstrated capability in learning motion artifact characteristics.
- Detection confidence of motion artifacts varied depending on the k-space sampling pattern used.
Conclusions:
- Supervised machine learning is effective for detecting motion artifacts in MR imaging.
- Understanding the influence of k-space sampling patterns is crucial for accurate artifact detection.
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