Dynamic MRI Reconstruction via Pseudo-Random K-Space Undersampling

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Solution Overview

Problem

Magnetic Resonance Imaging (MRI) requires long scan times, limiting its application in dynamic imaging, such as the heart, due to the need for extensive data sampling in k-space, which violates the Nyquist criterion and results in aliasing artifacts.

Innovation Solution

The method employs pseudo-random ordering in at least one spatial frequency dimension and the time dimension during data acquisition, combined with enforced sparsity constraints, to reconstruct dynamic MRI images efficiently by exploiting transform sparsity, using compressed sensing techniques to undersample k-space while ensuring incoherent artifacts can be recovered through non-linear reconstruction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If extensive data sampling in k-space is performed to maintain image quality, then manufacturing precision is improved, but loss of time increases

Engineering Contradiction:

Improveimage qualityVSAvoidscan time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent applies partial sampling of k-space by acquiring fewer data points than the full Nyquist requirement would dictate. By sampling only a subset of k-space lines in a pseudo-random order and using compressed sensing reconstruction, the system recovers complete dynamic MRI images without acquiring all the data that traditional methods would require, thus reducing scan time while maintaining diagnostic image quality.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent changes the sampling parameters by using pseudo-random sampling patterns instead of systematic Cartesian sampling. The sampling density and distribution are modified to create incoherent undersampling patterns that, when combined with sparsity constraints in the image domain, enable accurate reconstruction from fewer samples. This parameter change transforms the sampling strategy from exhaustive to selective.

Inventive Principle:

Principle #35Parameter changes

2Loss of time

If data sampling rate is reduced to decrease scan time, then loss of time is improved, but measurement precision deteriorates

Engineering Contradiction:

Improvescan timeVSAvoidimage reconstruction accuracy

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The patent converts the harmful effect of undersampling artifacts into a beneficial outcome by using pseudo-random sampling patterns that create incoherent aliasing. These incoherent artifacts, when combined with sparsity constraints during reconstruction, actually help identify and eliminate false signals, allowing accurate image recovery from undersampled data. The harm of insufficient sampling becomes the basis for a novel reconstruction approach.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

Solution Approach 2:

The reconstruction process incorporates iterative feedback where the sparsity constraint information from the image domain is fed back into the sampling strategy. The system uses the known sparsity property of MRI images (where most pixel values are zero or near-zero) to guide the reconstruction of undersampled data, continuously refining the image estimate until convergence. This feedback mechanism ensures measurement precision is maintained despite reduced sampling.

Inventive Principle:

Principle #23Feedback

3Productivity

If pseudo-random sampling is used to reduce scan time, then productivity is improved, but device complexity increases

Engineering Contradiction:

Improvescan efficiencyVSAvoidreconstruction complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces the mechanical/data acquisition complexity with computational complexity. Instead of using complex hardware to rapidly acquire all necessary k-space data, the system uses simpler pseudo-random sampling followed by sophisticated computational reconstruction algorithms. The mechanical sampling process is simplified while the computational reconstruction handles the complexity of recovering images from undersampled data, substituting one form of complexity for another more manageable form.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS7602183B2K-T sparse: high frame-rate dynamic magnetic resonance imaging exploiting spatio-temporal sparsity

Publication Date: 2009.10.13 THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIV

AI summary

A method of dynamic resonance imaging is provided. A magnetic resonance imaging excitation is applied. Data in 2 or 3 spatial frequency dimensions, and time is acquired, where an acquisition order in at least one spatial frequency dimension and the time dimension are in a pseudo-random order. The pseudo-random order and enforced sparsity constraints are used to reconstruct a time series of dynamic magnetic resonance images.