MRI Navigator Signal Estimation for Motion and Field Offset Correction
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Solution Overview
Problem
Current methods for motion correction in magnetic resonance imaging (MRI) scans are limited and require additional hardware, while navigator-based approaches are promising but lack accuracy and efficiency in estimating object motion and magnetic field offsets.
Innovation Solution
A method utilizing a train of sequence modules with RF excitation and image encoding segments, incorporating navigator gradient segments for acquiring object signals, and applying linear least-squares estimation to calculate transformation matrices for motion and field offsets, enabling precise estimation of rotations, translations, and magnetic field perturbations using complex-valued navigator signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If navigator-based motion estimation is used, then additional hardware requirements are reduced, but measurement precision and accuracy of motion estimation deteriorate
Solution Approach 1:
The motion estimation process is segmented into two distinct phases: (1) calibration phase where a transformation matrix M is calculated from initial navigator signals, and (2) measurement phase where subsequent navigator signals are processed using the pre-calculated matrix. This segmentation allows the system to use simple navigator signals without additional hardware while achieving accurate motion estimation through the pre-computed transformation relationship.
Solution Approach 2:
The transformation matrix M is calculated in advance during a calibration phase before actual motion measurement begins. This preliminary action stores the relationship between navigator signal changes and object motion in a lookup table (transformation matrix), enabling rapid and accurate motion estimation during subsequent scans without requiring complex real-time calculations or additional hardware sensors.
2Reliability
If real-time motion correction is implemented, then image quality improves, but computation time and processing complexity increase
Solution Approach 1:
The transformation matrix M is pre-calculated during a calibration phase before actual imaging begins. This preliminary computation stores the relationship between navigator signal variations and object motion parameters, enabling real-time motion correction during imaging without requiring complex on-the-fly calculations, thus maintaining both image quality and computational efficiency.
Solution Approach 2:
The system implements a feedback loop where subsequent navigator signals are continuously acquired during imaging, compared against the pre-calculated transformation matrix M, and used to generate real-time motion estimates. These motion estimates are then fed back to correct the imaging process, enabling dynamic motion compensation while maintaining computational efficiency through the use of the pre-computed matrix.
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
Achieves high-accuracy and low-complexity estimation of object motion and magnetic field offsets, allowing real-time compensation during scans, improving image quality by reducing artifacts.
Implementation Method 1
a main magnetic field is generated in the object of interest by a main magnet
Implementation Method 2
object signal is acquired with an RF receive coil or coil array during a measurement segment of each sequence module
Data Source
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AI summary
In a method of estimating motion of an object and/or magnetic field offsets in a region surrounding the object during a magnetic resonance (MR) imaging scan of the object, superimposed magnetic fields and radiofrequency fields are generated according to an MR sequence for forming images, the MR sequence comprising a train of sequence modules, each sequence module comprising a radiofrequency (RF) excitation segment and an image encoding gradient segment, the MR sequence further comprising a plural- ity of navigator gradient segments. The navigator signal is acquired along a trajectory in k-space and expressed as a discrete time series comprising a predefined number of complex-valued signal datapoints. The navigator signal acquired in a first sequence module is used to calculate a transformation matrix which relates rotation angles and translational shifts and/or changes of the magnetic field to corresponding changes in navigator signal in a first order approximation. The navigator signal acquired in a subse- quent sequence module is used to estimate object motion in terms of a translational dis- placement and a rotational displacement of the object and/or magnetic field offsets in terms of a scalar field variation and a vectorial field variation between the first sequence module and the subsequent sequence module by solving a corresponding linear least- squares estimation problem.