MRI Image Reconstruction via Optimized Encoding Matrix
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Solution Overview
Problem
The existing methods for k-space under-sampling in MRI, such as compressed sensing and parallel imaging, face challenges in minimizing signal-to-noise ratio (SNR) loss and achieving high acceleration factors, particularly in applications like cardiac imaging where image quality degradation is unacceptable.
Innovation Solution
The method involves optimizing the matrix E used in data restoration by controlling the operation parameters of RF pulses and gradient pulses, such as flip angles, phases, and amplitudes, to improve the conditioning of the encoding matrix and enhance SNR preservation during 'aliased k-space' acquisition, allowing for higher acceleration factors while maintaining image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If k-space under-sampling is applied to accelerate MRI scans, then scan time is reduced, but signal-to-noise ratio is degraded
Solution Approach 1:
The k-space data is divided into multiple segments or blocks that are acquired separately and then combined. This segmentation allows for more efficient sampling strategies where each segment can be optimized independently, reducing total scan time while maintaining SNR through proper combination of segments using the encoding matrix E
Solution Approach 2:
The invention optimizes acquisition parameters including flip angles, phases, and amplitudes of RF and gradient pulses to improve the conditioning of the encoding matrix E. By carefully controlling these parameters, the system achieves better SNR preservation during under-sampled acquisition
2Productivity
If acceleration factor is increased through under-sampling, then scan speed is improved, but image quality is degraded
Solution Approach 1:
The system uses the encoding matrix E to reconstruct images from under-sampled data, effectively providing a feedback mechanism that recovers missing information. The matrix is designed to preserve image quality even at high acceleration factors by optimally combining the available sampled data
Solution Approach 2:
The encoding matrix E is pre-optimized by controlling operation parameters before acquisition. This preliminary optimization ensures that the sampling pattern and pulse parameters are configured to maximize image quality recovery at the desired acceleration factor
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 improves the accuracy and quality of image reconstruction by reducing noise amplification and enhancing SNR, enabling higher acceleration factors in MRI scans without compromising image quality.
Implementation Method 1
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the excited nuclei in the tissue attempt to align with this polarizing field, but process about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) that is in the x-y plane and that is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or 'tipped', into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited nuclei or 'spins'
Implementation Method 2
magnetic field gradients (Gx, Gy and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles, in which these gradients vary according to the particular localization method being used
Data Source
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AI summary
The invention relates to a method of constructing an image of a sample from MRI data, and an MRI data server comprising a processor and a memory including a program to perform the method, wherein the method comprises: i) selectively acquiring one or more sets of MRI data generated from driving signals created under a specific set of operation parameters; ii) deriving a matrix representative of the selective acquisition of the one or more sets of MRI data; iii) processing the selectively acquired MRI data with the matrix to obtain output data; and iv) synthesising the output data to construct the image of the sample. This method is applicable for any aliased k-space acquisition that overlaps blocks of k-space data for enhancing the performance of an accelerated MRI scan.