Rigid-Body MRI Motion Estimation Using Pilot Tone Data
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Solution Overview
Problem
Existing MR imaging techniques suffer from low temporal resolution and inaccurate rigid body motion vectors due to noise in scout and guidance data, especially when minimal subject motion occurs, leading to motion artifacts in MR images.
Innovation Solution
A method and apparatus for MR imaging that calculates rigid body motion vectors for each of the most recent N imaging excitations, using pilot tone data and a linear transformation model to improve accuracy and robustness, and adjusts scanning parameters based on predicted subject motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rigid body motion vector is calculated once every 2 seconds using linear calculations on scout data and guidance data, then the calculation complexity is reduced, but the temporal resolution becomes low
Solution Approach 1:
The patent divides the motion vector calculation into multiple segments: calculating motion vectors for each of the most recent N imaging excitations (where N > 1), and further segmenting the PT data acquisition into multiple channels. This segmentation allows more frequent calculations (improving temporal resolution) while managing complexity through structured processing of multiple data sources.
Solution Approach 2:
The patent performs preliminary calculations by first calculating rigid body motion vectors for the most recent N imaging excitations using scout data and guidance data before performing the main motion estimation. This preliminary action establishes a foundation that reduces the complexity of subsequent real-time motion vector calculations, allowing higher temporal resolution without proportional increase in overall computational burden.
2Productivity
If rigid body motion vector is calculated using scout data and guidance data, then the motion estimation can be performed, but the accuracy becomes insufficient when subject moves very little or does not move due to noise
Solution Approach 1:
The patent merges multiple data sources (scout data, guidance data, and PT data from multiple channels) and combines them through a linear transformation model to calculate rigid body motion vectors. This merging of multiple independent measurement sources allows the system to maintain accuracy even when individual sources are noisy or when subject motion is minimal, as the combined signal provides more robust motion information.
Solution Approach 2:
The patent introduces PT data as an intermediary measurement that directly reflects subject motion during imaging excitations. This intermediary signal serves as a bridge between the imaging process and motion estimation, providing accurate motion information even when traditional scout and guidance data are insufficient or noisy, thereby improving measurement precision without sacrificing productivity.
3Measurement precision
If PT data is acquired from multiple channels and a linear transformation model is calculated, then the accuracy and robustness of motion estimation are improved, but the device complexity increases
Solution Approach 1:
The patent changes the parameters of the linear transformation model by calculating it from PT data of multiple channels and rigid body motion vectors. This parameter transformation allows the system to encode motion information more efficiently, improving accuracy and robustness while the mathematical formulation maintains a structured, manageable complexity that can be implemented through standard computational algorithms.
Data Source
AI summary
A motion estimation method and apparatus for magnetic resonance imaging and a magnetic resonance imaging system. The method includes: calculating a rigid body motion vector of a subject for each of a most recent N imaging excitations; according to pilot tone (PT) data of each channel acquired for the most recent N imaging excitations and N rigid body motion vectors calculated for the most recent N imaging excitations, calculating a linear transformation model for transforming from the PT data of each channel acquired for the most recent N imaging excitations to the N rigid body motion vectors; and for each subsequent imaging excitation, according to PT data of each channel acquired during an echo duration of each echo and the linear transformation model, calculating a rigid body motion vector of the subject for each echo of each imaging excitation.