Hybrid k-t MR Imaging with Dynamic Regularization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional dynamic imaging techniques, such as ktSENSE and RIGR, face limitations in achieving high spatiotemporal resolution due to the use of static regularization images, which can lead to decreased temporal resolution and misregistration errors.
Innovation Solution
The integration of variable-density, sequentially-interleaved k-space sampling with the acquisition of reference frames and dynamic imaging data, combined with the use of a non-static regularization image generated by RIGR, to improve spatiotemporal resolution and reduce artifacts in MR imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fully sampled frames are acquired to achieve high spatial resolution, then spatial resolution is improved, but acquisition time increases significantly thereby degrading temporal resolution
Solution Approach 1:
The patent applies partial sampling in k-space by acquiring only a subset of phase encoding lines (e.g., every other line or using radial sampling patterns) rather than full sampling. This partial action reduces acquisition time while using correlation techniques to recover the missing information, thereby improving temporal resolution without completely sacrificing spatial resolution
Solution Approach 2:
The patent introduces dynamic regularization that adapts to temporal changes in the imaged object. The regularization parameters and reconstruction algorithms are updated dynamically across time frames to reflect actual motion and contrast changes, allowing the system to maintain high spatial resolution in dynamic regions while using reduced sampling in static regions, thus optimizing the spatial-temporal resolution tradeoff
2Loss of time
If ktSENSE uses a static regularization image to improve temporal resolution, then temporal resolution is improved, but spatial resolution and image quality deteriorate due to misregistration errors and inability to capture dynamic features
Solution Approach 1:
The patent replaces the static regularization image with a dynamic regularization approach where the reference image is updated across time frames to reflect actual temporal changes. This dynamic regularization adapts to motion and contrast enhancement, maintaining spatial resolution by accurately representing the current state of the imaged object rather than relying on a fixed reference from a previous time point
Solution Approach 2:
The patent performs preliminary registration and alignment of dynamic frames before generating the regularization image. By pre-aligning the dynamic data with the reference frame and correcting for motion artifacts in advance, the system eliminates misregistration errors that would otherwise degrade spatial resolution, thereby enabling the use of dynamic regularization without sacrificing image quality
3Loss of time
If RIGR assumes low spatial resolution for dynamic features to improve temporal resolution, then temporal resolution is improved, but diagnostically valuable information is lost
Solution Approach 1:
The patent applies local quality by allowing different spatial resolutions in different regions of the image based on the degree of motion and diagnostic importance. Highly dynamic regions with diagnostically valuable information (e.g., contrast-enhancing lesions, moving cardiac structures) are reconstructed with higher spatial resolution, while static or less important regions use lower resolution, thereby preserving critical diagnostic information while maintaining overall temporal resolution
Solution Approach 2:
The patent dynamically adjusts reconstruction parameters such as regularization strength, filtering coefficients, and sampling density based on the local temporal frequency content and diagnostic importance of different image regions. By changing these parameters adaptively, the system maintains high spatial resolution for diagnostically critical features while using aggressive temporal compression for less important regions, thus preventing loss of valuable diagnostic information
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
This approach enhances spatiotemporal resolution and signal-to-noise ratio (SNR) of dynamic images, reducing artifacts and misregistration errors, while maintaining high temporal resolution.
Implementation Method 1
When utilizing these signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed.
Implementation Method 2
A signal is emitted by the excited spins after the excitation signal B1 is terminated and this signal may be received and processed to form an image.
Data Source
AI summary
Variable-density (VD), sequentially-interleaved sampling of k-space coupled with the acquisition of reference frames of data is carried out to improve spatiotemporal resolution, image quality, and signal-to-noise ratio (SNR) of dynamic images. In one example, ktSENSE is implemented with a non-static regularization image, such as that provided by RIGR or similar technique, to acquire and reconstruct dynamic images. The integration of ktSENSE and RIGR, for example, provides dynamic images with higher spatiotemporal resolution and lower image artifacts compared to dynamic images acquired and reconstructed using ktSENSE alone.