How Machine Learning is Used to Reduce Motion in MRI

Introduction

Artificial Intelligence (AI) has shown great potential in improving many areas of medical imaging, and its application to Magnetic Resonance Imaging (MRI) is no exception. One of the main challenges in MRI is dealing with motion artefacts, which can distort images and impact their diagnostic value. My literature review examines the role of AI in addressing this issue, particularly focusing on the effectiveness of AI tools in motion artefact correction. In this post, I will summarise the key findings from my review and explore the potential and limitations of AI in this field.

Understanding MRI and Motion Artefacts

MRI is a widely used medical imaging technique, valued for its ability to provide detailed images of soft tissues without using ionising radiation. It works by aligning the protons in our body with a magnetic field and then using radiofrequency pulses to create images. However, due to the length of time needed to acquire high-resolution MRI images, they are prone to artefacts, especially motion artefacts. These distortions occur when patients move during the scan, whether it’s voluntary or involuntary motion (such as breathing or heartbeats). The resulting images can be blurry or ghosted, compromising their usefulness for diagnosis.

Traditional Approaches to Motion Correction

Traditional motion correction methods in MRI have focused on both preventing and correcting motion artefacts. Techniques like “PROPELLER” (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction) have effectively improved image quality by estimating and compensating for motion. However, traditional methods have their limitations. They can be computationally intensive, require additional hardware or postprocessing software, and sometimes increase the scanning time, which might introduce more artefacts.

AI-Based Techniques for Motion Artefact Correction

AI, particularly deep learning (DL) techniques like Convolutional Neural Networks (CNNs), has gained significant attention for its ability to automate and enhance image analysis in medical imaging. CNNs are highly effective at recognising and correcting artefacts in MR images. For example, they have been used to detect and quantify motion artefacts, and even propose visualisation methods that highlight the features within an image that are responsible for motion artefacts.

Studies show that CNNs can handle various types of motion, such as rigid head motion and non-rigid respiratory motion, making them versatile tools for MRI motion correction. More advanced methods like the Network Accelerated Motion Estimation and Reduction (NAMER) technique combine CNNs with model-based reconstructions, allowing for rapid motion correction in brain imaging.

Challenges in AI-Driven Motion Correction

One of the major challenges with AI techniques is the time required for training models. Deep learning models, such as CNNs, require extensive training with large datasets to perform effectively. Additionally, the time needed to train these models can be long, which presents a barrier, especially in clinical settings where real-time corrections are needed. However, once trained, these models can be used to reconstruct images in real time, which could save time in the long run.

Another issue is the lack of sufficient training data. MRI scans vary widely between patients, and there isn’t always enough high-quality data to train AI models effectively. Some researchers have turned to motion simulation tools to generate synthetic training data, which can help to overcome this limitation.

Hybrid Approaches: Combining AI and Traditional Techniques

Hybrid approaches are becoming more popular as they combine the strengths of traditional motion correction methods with the flexibility and automation of AI techniques. For instance, traditional methods can still provide a reliable framework for estimating motion, while AI can be used to fine-tune and optimise the corrections. This hybrid model has been shown to improve the overall efficacy of motion correction in MRI, with studies suggesting that AI tools can work alongside existing clinical methods to provide more consistent and accurate results.

Conclusion

The use of AI in MRI motion artefact correction is an exciting and rapidly evolving field. While AI tools like CNNs and U-Nets have shown considerable promise, they are not without their limitations, particularly in terms of training time and data availability. However, when combined with traditional techniques, AI can significantly improve the quality and efficiency of MRI scans, leading to better patient outcomes. As this field continues to develop, future research should focus on creating diverse training data sets and refining hybrid approaches to maximise the benefits of both AI and conventional methods.

The next frontier in this field lies in clinical implementation, where we can begin to see AI’s true potential in improving diagnostic accuracy and patient care.

Read the full review here.