MRI Motion Artifact Prediction Using Deep Learning
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Solution Overview
Problem
Magnetic resonance imaging (MRI) data acquisition is often hindered by motion artifacts due to patient movement, leading to image degradation and the need for repeated scans, which can be time-consuming and affect diagnostic accuracy.
Innovation Solution
A magnetic resonance imaging system equipped with a deep learning-based model that predicts motion artifact levels during data acquisition, allowing for early detection and mitigation of artifacts, enabling real-time quality monitoring and potential re-start of the acquisition process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If MRI data acquisition is performed with long scanning time to ensure image quality, then image quality is improved, but motion artifacts increase due to patient movement
Solution Approach 1:
The system performs preliminary quality assessment during data acquisition by analyzing previously acquired datasets before completing the full scan. This allows early detection of motion artifacts and enables timely intervention (aborting or restarting the scan) before significant degradation occurs, thus preventing poor image quality while avoiding complete scans when motion is detected
2Measurement precision
If complete MRI data acquisition is performed to ensure diagnostic quality, then diagnostic accuracy is improved, but time consumption increases due to potential repeat scans
Solution Approach 1:
The system implements continuous feedback during data acquisition by repeatedly assessing dataset quality using a machine learning model. The quality assessment results feed back into the acquisition process, enabling real-time decisions about whether to continue, abort, or restart the scan. This feedback mechanism ensures diagnostic quality by detecting motion artifacts early while minimizing time loss by avoiding completion of severely degraded scans
3Measurement precision
If motion artifact detection is performed retrospectively after complete data acquisition, then artifact assessment is thorough, but time is lost due to inability to intervene during acquisition
Solution Approach 1:
The system performs quality assessment preliminarily during the acquisition process rather than retrospectively after completion. By analyzing datasets incrementally as they are acquired and using the machine learning model to predict motion artifact levels, the system enables early intervention while the scan is still in progress, thus maintaining thorough artifact detection capability while significantly reducing time loss
Solution Approach 2:
When motion artifacts are detected early in the acquisition process, the system skips completing the full scan by aborting the acquisition. This rushing through approach prevents wasting time on scans that will ultimately be discarded due to motion artifacts, while the preliminary quality assessment ensures that only scans with acceptable quality are completed
Data Source
AI summary
A magnetic resonance imaging system including a memory configured to store machine executable instructions, pulse sequence commands, and a first machine learning model including a first deep learning network. The pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire a set of magnetic resonance imaging data. The first machine learning model includes a first input and a first output, a processor, wherein execution of the machine executable instructions causes the processor to control the magnetic resonance imaging system to repeatedly perform an acquisition and analysis process including: acquiring a dataset including a subset of the set of magnetic resonance imaging data from an imaging zone of the magnetic resonance imaging system according to the pulse sequence commands, providing the dataset to the first input of the first machine learning model, in response to the providing, receiving a prediction of a motion artifact level of the acquired magnetic resonance imaging data from the first output of the first machine learning model, the motion artifact level characterizing a number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data.