MRI Image Reconstruction Using Corkscrew Trajectory Sampling

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Solution Overview

Problem

Magnetic resonance imaging (MRI) systems face limitations in reconstructing high-quality images with high acceleration factors due to noise amplification in parallel imaging, which restricts the efficiency of data acquisition and image quality.

Innovation Solution

A method for MRI image reconstruction involving the use of a computing device to obtain k-space data along a corkscrew trajectory, determine coil sensitivities, and generate target images based on an objective function that accounts for the mapping relationship between k-space data and coil sensitivities, while employing a point spread function to improve image quality and reduce noise.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If parallel imaging with high acceleration factor is used to accelerate data acquisition, then productivity is improved, but noise amplification increases non-linearly degrading image quality

Engineering Contradiction:

Improvedata acquisition speedVSAvoidimage quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent changes the sampling trajectory parameter from traditional Cartesian or spiral paths to a corkscrew trajectory in k-space. This parameter change enables more efficient sampling patterns that reduce noise amplification while maintaining high acceleration factors, directly resolving the contradiction between productivity and reliability

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces traditional image reconstruction methods with a learned reconstruction network that uses deep learning techniques. This substitution transforms the reconstruction process from deterministic mathematical operations to a data-driven approach that can handle high acceleration factors without proportional noise amplification, improving both productivity and image quality

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If traditional parallel imaging reconstruction is used, then image quality is maintained at low acceleration factors, but the method becomes insufficient for high acceleration factors due to noise amplification

Engineering Contradiction:

Improveimage qualityVSAvoidacceleration capability

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent introduces a learned reconstruction network as an intermediary between the accelerated k-space sampling and the final image reconstruction. This intermediary component processes the undersampled data in a way that preserves image quality while enabling higher acceleration factors than traditional methods, bridging the gap between productivity and reliability requirements

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If acceleration factor is increased to improve scanning efficiency, then productivity is improved, but noise amplification increases non-linearly

Engineering Contradiction:

Improvescanning efficiencyVSAvoidnoise amplification

Core Design Contradiction:

ProductivityVSObject-generated harmful factors

Solution Approach 1:

The patent converts the harmful effect of noise amplification into a manageable problem by using the learned reconstruction network to identify and suppress noise patterns. The network learns to distinguish between actual signal and noise artifacts, effectively converting the harmful noise amplification into a benefit where high acceleration can be achieved without proportional noise increase

Inventive Principle:

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

Data Source

PatentUS12193803B2Systems and methods for magnetic resonance imaging

Publication Date: 2025.01.14 SHANGHAI UNITED IMAGING HEALTHCARE

AI summary

The present disclosure is related to systems and methods for magnetic resonance imaging (MRI). The method includes obtaining a plurality of target sets of k-space data by filling target MR signals acquired by a plurality of coils of an MRI device into k-space along a corkscrew trajectory. The method includes obtaining a coil sensitivity of each of the plurality of coils. The method includes obtaining a point spread function corresponding to the corkscrew trajectory. The method includes generating a target image based on an objective function.