Motion-Insensitive Magnetic Resonance Fingerprinting Algorithm
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Solution Overview
Problem
Conventional magnetic resonance fingerprinting (MRF) techniques are susceptible to patient motion, particularly in early stages of data acquisition, which can lead to motion artifacts and reduce the diagnostic value of MRI scans, especially in vulnerable patient populations.
Innovation Solution
The implementation of a motion-insensitive magnetic resonance fingerprinting (MORF) method that uses a series of variable sequence blocks to produce distinct signal evolutions in resonant species, with a computer system identifying and compensating for motion-corrupted image frames to generate motion-compensated data, thereby reducing the sensitivity to patient motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRF techniques are used, then the diagnostic information can be obtained, but motion artifacts are introduced and measurement precision deteriorates
Solution Approach 1:
The system performs preliminary actions by acquiring multiple images at different time points before final analysis. Motion correction is performed iteratively using these preliminary images to estimate motion fields, which are then applied to correct the MRI data. This preliminary acquisition and iterative correction process enables the system to compensate for patient motion before final diagnostic images are produced.
Solution Approach 2:
The system implements feedback by continuously monitoring image quality metrics and motion parameters throughout the acquisition process. The estimated motion fields from preliminary images are fed back into the correction process, where they are used to adjust and refine the final image reconstruction. This feedback loop enables dynamic adaptation to patient motion patterns.
2Loss of information
If the scan duration is extended to improve diagnostic value, then more diagnostic information is obtained, but patient motion increases and harmful factors worsen
Solution Approach 1:
The system acquires preliminary images at multiple time points during the scan to establish baseline data for motion correction. These preliminary actions enable the system to track and compensate for motion throughout the extended acquisition period, allowing longer scan durations without proportionally increasing motion artifacts.
Solution Approach 2:
The system uses feedback from continuously monitored motion parameters to adjust the correction process in real-time during extended scans. This enables the system to maintain image quality throughout prolonged acquisition periods by dynamically adapting to changing patient motion patterns.
3Object-affected harmful factors
If anesthesia is used to eliminate patient motion, then motion artifacts are reduced, but scan duration increases and productivity decreases
Solution Approach 1:
The system uses feedback from motion monitoring to identify and correct motion artifacts without requiring anesthesia. By continuously tracking motion parameters and applying iterative correction algorithms, the system can maintain scan efficiency while eliminating the need for anesthesia-induced patient immobilization.
Solution Approach 2:
The system performs self-service by automatically detecting and correcting motion artifacts through iterative algorithms that process the acquired images. This self-correcting capability eliminates the need for external intervention such as anesthesia, thereby maintaining scan efficiency and productivity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The MORF algorithm effectively suppresses motion artifacts, improving the accuracy of MRI scans and reducing the need for anesthesia, thereby enhancing the diagnostic and therapeutic value of the imaging process.
Implementation Method 1
magnetic resonance fingerprinting (MRF), which is described, as one example, by D. Ma, et al., in Magnetic Resonance Fingerprinting, Nature, 2013; 495 (7440): 187-192
Implementation Method 2
A series of initial image frames is generated with the computer system by comparing the magnetic resonance data to a dictionary of signal evolutions
Implementation Method 3
Motion-compensated magnetic resonance data are generated with the computer system by applying the estimated subject motion to the provided magnetic resonance data
Data Source
AI summary
Methods for magnetic resonance fingerprinting (“MRF”) that are more robust to patient motion than conventional MRF techniques are described. The methods described in the present disclosure provide an image reconstruction algorithm for MRF that decreases the motion sensitivity of MRF.