Method of reconstructing a dynamic series of motion-compensated magnetic resonance images
Inventors
Kunze, Karl-Philipp • Neji, Radhouene
Assignees
Publication Number
US-12352838-B2
Publication Date
2025-07-08
Expiration Date
2042-06-29
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Abstract
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.
Core Innovation
The invention relates to a computer-implemented method for reconstructing a dynamic series of motion-compensated magnetic resonance images of a patient. The method acquires k-space data over time at a first image resolution on a frame-by-frame basis during successive respiratory and/or cardiac cycles. Each frame's k-space data comprises a first subset of data points with a first sample density and a second subset with a second sample density.
The method selects, for each frame, sub-groups of these first and second subsets and reconstructs images at a second image resolution. It estimates motion between these images as motion fields and incorporates this motion information into a final reconstruction at a third image resolution, yielding a dynamic series of motion-compensated magnetic resonance images.
The problem addressed relates to challenges in cardiovascular MRI imaging caused by motion corruption due to cardiac and respiratory movement. Existing methods struggle to provide accurate motion compensation during dynamic imaging, especially when acquiring multiple fast single-shot images. Breath holding is an imperfect solution, often unfeasible for many patients and limiting kinetic data acquisition time. Moreover, current reconstruction methods either lack temporal regularization or rely on assumptions of motion periodicity, unsuitable for irregular motion patterns. Obtaining accurate motion information from highly undersampled data remains difficult, adversely affecting image fidelity and motion correction.
The invention provides a dedicated spatio-temporal sampling strategy enabling preliminary reconstruction at a potentially lower resolution without temporal regularization. This approach allows high-fidelity depiction of inter-frame motion. It combines coherently undersampled (high sample density) regions centered in k-space with incoherently undersampled (lower sample density) regions, enabling robust explicit motion estimation in the form of motion fields while preserving favorable spatio-temporal properties for final temporally regularized reconstruction at higher resolution.
Claims Coverage
The patent includes two independent claims covering a computer-implemented method and a data processing apparatus for dynamic motion-compensated magnetic resonance imaging reconstruction. The following are the main inventive features disclosed in the claims.
Coherent and incoherent undersampling of k-space data
Each frame of k-space data comprises a first subset of data points undersampled coherently across the dynamic series in a k-space dimension and a second subset undersampled incoherently across the dynamic series in the same dimension.
Dynamic image reconstruction at reduced resolution
For each frame, a sub-group of the first and second subsets of data points is selected to reconstruct images at a second resolution, lower than the first, facilitating motion estimation.
Motion estimation using motion fields
Motion between the second resolution images is estimated in the form of motion fields representing inter-frame motion.
Incorporating motion fields into final reconstruction
The motion fields are incorporated into the final dynamic series reconstruction at a third image resolution, enabling motion-compensated images.
Sampling and undersampling schemes
The coherently undersampled region is linearly undersampled, covering the center of k-space, often obtained via time-interleaved undersampling, while the incoherent region uses random or pseudo-random undersampling.
Use of spatial and temporal regularization
Preliminary reconstruction at the second resolution employs spatial regularization without temporal regularization; the final reconstruction applies temporal regularization across dynamic images, and may also use spatial regularization.
Registration and interpolation of motion fields
The reconstructed images are registered frame-by-frame to derive motion fields, which are interpolated to a desired resolution used in final reconstruction.
Applicability to free-breathing acquisition
The method supports acquisition while the patient is free-breathing for at least a portion of the k-space data acquisition.
The claims collectively cover a method and apparatus for motion-compensated dynamic MRI image reconstruction that leverages combined coherent and incoherent undersampling, reduced-resolution preliminary reconstruction for motion estimation, and incorporation of estimated motion fields into a temporally regularized final reconstruction at higher resolution, facilitating robust motion correction including during free-breathing.
Stated Advantages
The method enables robust estimation of explicit motion fields between dynamic MRI frames while maintaining favorable spatio-temporal sampling properties.
Preliminary reconstruction at reduced resolution without temporal regularization preserves inter-frame motion fidelity, improving motion depiction accuracy.
The combined coherent and incoherent undersampling reduces spatial undersampling artifacts and temporal signal variations in preliminary reconstructions.
The approach supports imaging during free-breathing, removing the need for breath hold, easing clinical workflow and expanding applicability to unwell patients unable to hold their breath.
The final reconstruction benefits from temporal regularization exploiting data redundancy, enabling higher acceleration factors to gain image resolution or improved morphological coverage.
Documented Applications
Cardiovascular magnetic resonance imaging, particularly myocardial perfusion imaging requiring detection of small perfusion defects.
Dynamic MRI imaging capturing multiple images of tissue perfusion over time after contrast agent injection.
Imaging during free-breathing to avoid limitations of breath-hold techniques, enabling kinetic data acquisition over longer time windows.
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