Motion-Compensated Dynamic MRI Reconstruction for Free-Breathing Imaging
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Solution Overview
Problem
Existing MRI reconstruction techniques struggle with motion compensation, particularly in cardiovascular imaging, due to cardiac and respiratory motion, leading to image corruption and limited spatial resolution and morphological coverage, especially when patients cannot hold their breath.
Innovation Solution
A method involving spatio-temporal sampling strategies for MRI data acquisition, including coherent and incoherent undersampling of k-space data, followed by motion field estimation and incorporation into a final reconstruction, allowing for robust motion compensation without temporal regularization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If temporal regularization is used in image reconstruction, then image resolution and morphological coverage are improved, but motion compensation performance deteriorates
Solution Approach 1:
The patent segments the k-space data into multiple subsets with different sampling densities (first subset with higher density, second subset with lower density). This segmentation allows the reconstruction process to handle motion and contrast changes separately, enabling motion compensation without relying on temporal regularization that would confuse motion artifacts with contrast dynamics.
Solution Approach 2:
Different regions of k-space are sampled at different densities. The first subset of k-space data points is sampled at a higher density than the second subset. This local quality differentiation allows the reconstruction algorithm to achieve high image resolution in regions where motion is minimal while maintaining the ability to compensate for motion in regions where it occurs, resolving the contradiction between resolution and motion compensation.
2Manufacturing precision
If breath-holding is used to reduce motion, then image quality is improved, but patient comfort and workflow complexity worsen
Solution Approach 1:
The patent extracts and removes the breath-holding requirement from the imaging process by using retrospective motion correction. Instead of requiring patients to hold their breath during acquisition, the system acquires data during natural breathing and then mathematically corrects for the motion in the reconstruction phase, thereby maintaining image quality while improving patient comfort and workflow.
Solution Approach 2:
The patent introduces motion fields as an intermediary between the raw k-space data and the final image reconstruction. These motion fields capture the respiratory and cardiac motion effects and are used to correct the images retrospectively, eliminating the need for breath-holding while maintaining image quality through mathematical modeling rather than physical constraint.
3Duration of action of moving object
If fast single-shot image acquisitions are used, then temporal coverage is improved, but motion compensation and image resolution deteriorate
Solution Approach 1:
The patent applies partial undersampling to the k-space data, acquiring only a subset of the required data points for each single-shot image. By using intelligent sampling strategies (first and second subsets with different densities), the system achieves sufficient image quality and motion compensation at lower acceleration factors, thereby improving temporal coverage without sacrificing resolution.
Solution Approach 2:
The patent performs preliminary motion estimation using the acquired k-space data before final image reconstruction. Motion fields are estimated from the undersampled data and then used to guide the full reconstruction process, allowing fast single-shot acquisitions to maintain both high resolution and accurate motion compensation through preparatory motion characterization.
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
Enables high-fidelity motion compensation in dynamic MRI series, improving spatial resolution and morphological coverage, even in free-breathing patients, without the need for breath-holding and with reduced computational complexity.
Implementation Method 1
Magnetic resonance imaging (MRI) is used frequently in medical applications as a diagnostic and staging tool. A patient is exposed to a static magnetic field Bo and an incident pulsed RF (radio-frequency) signal, which excite the nuclear spin energy transition in hydrogen atoms present in water and fat in the body. Magnetic field gradients are used to localize the resulting magnetization in space, leading to the generation of an image.
Data Source
AI summary
A Computer-implemented method of reconstructing a dynamic series of motion-compensated magnetic resonance images of a patient is provided. Images of a patient are acquired over time, at least partially in free-breathing, at a first image resolution and on a frame-by-frame basis. Each frame of the k-space data includes a first subset of data points having a first sample density and a second subset of data points having a second sample density. For each frame, a sub-group of the first subset and the second subset of the k-space data is selected, and an image is reconstructed at a second image resolution. The motion between the second image resolution images is estimated in the form of motion fields. The motion information is incorporated into a final reconstruction of a dynamic series of motion-compensated magnetic resonance images of the patient at a third image resolution.