GRAPPA Kernel Calibration for MRI K-space Artifact Removal

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Solution Overview

Problem

Magnetic resonance imaging is hindered by high-intensity contaminations in k-space measurement values due to artifacts like radio-frequency interferences, leading to reduced signal-to-noise ratio and image artifacts, which existing hardware adaptations fail to completely address effectively.

Innovation Solution

A method involving the calibration of GRAPPA kernels for magnetic resonance measurement datasets, where k-space values are verified against intensity criteria and reconstructed using linear combinations to replace false values, thereby generating a cleaned dataset with improved image quality.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Object-affected harmful factors

If hardware adaptations such as additional filter devices or oscillation-damped components are implemented, then artifact reduction is improved, but device complexity and cost increase significantly

Engineering Contradiction:

Improveartifact reductionVSAvoiddevice complexity

Core Design Contradiction:

Object-affected harmful factorsVSDevice complexity

Solution Approach 1:

The patent replaces hardware-based artifact reduction mechanisms with a software-based signal processing approach. Instead of using physical filter devices or mechanically damped components, the invention applies digital signal processing methods to identify and remove artifacts from k-space data, thereby eliminating the need for complex hardware adaptations while achieving comparable or superior artifact reduction

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The invention changes the approach from modifying physical system parameters (hardware design) to modifying data processing parameters (signal processing algorithms). By applying mathematical transformations and criteria-based filtering to the measurement data, the system achieves artifact reduction without altering the physical hardware configuration

Inventive Principle:

Principle #35Parameter changes

2Object-affected harmful factors

If hardware adaptations such as additional filter devices are implemented, then artifact reduction is improved, but manufacturing cost increases

Engineering Contradiction:

Improveartifact reductionVSAvoidmanufacturing cost

Core Design Contradiction:

Object-affected harmful factorsVSEase of manufacture

Solution Approach 1:

The patent substitutes expensive hardware components with software-based processing, thereby significantly reducing manufacturing costs. The solution uses standard magnetic resonance imaging hardware combined with algorithmic artifact removal, eliminating the need to manufacture and install additional filter devices or specialized components

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The invention employs computationally intensive but inexpensive software processing instead of expensive hardware. The artifact removal is achieved through data processing algorithms that can be implemented on standard computing platforms, making the solution much more cost-effective than hardware-based approaches

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

3Measurement precision

If k-space values are reconstructed using GRAPPA kernels, then image quality is improved, but processing time increases

Engineering Contradiction:

Improveimage qualityVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary calibration of GRAPPA kernels using only a subset of k-space data (calibration lines) before the actual image reconstruction. This pre-calibration step allows the main reconstruction process to proceed more efficiently, as the kernels are already optimized and ready for application, reducing the overall processing time while maintaining high image quality

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The invention applies GRAPPA reconstruction selectively and efficiently by using a reduced set of calibration lines rather than processing the entire k-space dataset for calibration. This partial action approach achieves sufficient kernel calibration with less computational effort, thereby reducing processing time while still obtaining accurate reconstruction kernels

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11137465B2Method and system for cleaning a magnetic resonance measurement dataset, computer program and computer-readable storage medium

Publication Date: 2021.10.05 SIEMENS HEALTHINEERS AG

AI summary

Method and system for cleaning a magnetic resonance measurement dataset. In the method, a GRAPPA kernel is calibrated on the measurement dataset, k-space values of the measurement dataset are verified against a predefined intensity criterion in order to identify false values, the k-space values of the measurement dataset are reconstructed point-by-point using the calibrated GRAPPA kernel from respective others of the k-space values, and the false values are replaced with the corresponding reconstructed k-space values in order to generate a cleaned measurement dataset.