Magnetic Resonance Fingerprinting Trajectory Optimization

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Solution Overview

Problem

Current magnetic resonance fingerprinting (MRF) techniques face challenges in reducing scan time due to significant artifacts from undersampling and field inhomogeneity issues, particularly in high-field applications, necessitating a more efficient acquisition method.

Innovation Solution

A deterministic schedule optimization method is employed to select pulse sequence parameters that minimize schedule lengths for fully sampling k-space, allowing for the acquisition of MRF data with improved discrimination between tissue types, thereby reducing the number of necessary measurements and minimizing artifacts.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If k-space is undersampled by sampling along a single spiral at each acquisition, then scan time is reduced, but significant artifacts are introduced requiring a large number of acquisitions

Engineering Contradiction:

Improvescan timeVSAvoidimage quality

Core Design Contradiction:

Loss of timeVSManufacturing precision

Solution Approach 1:

The patent segments k-space sampling into multiple trajectories per TR period, where each trajectory samples a portion of k-space. This allows more complete sampling without increasing TR length, reducing artifacts while maintaining scan time efficiency. The segmentation of the sampling process enables acquisition of multiple k-space lines per repetition cycle.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent employs dynamic adjustment of trajectory parameters including flip angle, TR, and TE across multiple acquisitions. By varying these parameters dynamically, the system optimizes signal discrimination and reduces artifacts. The deterministic optimization method adjusts sampling trajectories adaptively to maximize tissue discrimination while minimizing scan time.

Inventive Principle:

Principle #15Dynamics

2Manufacturing precision

If EPI-based sampling is used to sample the entire k-space, then sampling completeness is improved, but field inhomogeneity artifacts are introduced making it unsuitable for high fields

Engineering Contradiction:

Improvesampling completenessVSAvoidfield inhomogeneity artifacts

Core Design Contradiction:

Manufacturing precisionVSObject-affected harmful factors

Solution Approach 1:

The patent applies local quality by using multiple localized spiral trajectories that sample different regions of k-space. Each trajectory is optimized for its specific sampling region, allowing complete k-space coverage without the global field inhomogeneity issues of EPI. This localized approach reduces susceptibility artifacts while maintaining sampling efficiency.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent changes sampling parameters including trajectory count, flip angle, TR, and TE to optimize both sampling completeness and artifact reduction. The deterministic optimization method systematically adjusts these parameters to achieve the best trade-off between scan time, image quality, and artifact minimization for high-field applications.

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If the number of acquisitions is increased to reduce artifacts from undersampling, then image quality is improved, but scan time increases

Engineering Contradiction:

Improveimage qualityVSAvoidscan time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent maintains continuous useful action by acquiring multiple k-space trajectories within each TR period without idle periods. The deterministic optimization ensures that each acquisition contributes maximally to image quality, reducing the total number of TR periods needed. This continuous sampling approach improves image quality while minimizing scan time compared to traditional methods.

Inventive Principle:

Principle #20Continuity of useful action

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 results in reduced scan times, enhanced discrimination between tissue types, and improved accuracy of quantitative parameter mapping, overcoming the limitations of existing MRF techniques.

Implementation Method 1

a magnet system configured to generate a polarizing magnetic field about at least a portion of a subject

Methodology Applied

Scientific EffectMagnetic field: Magnetic Field

Implementation Method 2

a magnetic gradient system including a plurality of magnetic gradient coils configured to apply at least one magnetic gradient field to the polarizing magnetic field

Methodology Applied

Scientific EffectMagnetic gradient field: Magnetic Field

Implementation Method 3

a radio frequency (RF) system configured to apply an RF field to the subject and to receive magnetic resonance signals from the subject

Methodology Applied

Scientific EffectRadio frequency field: Electromagnetic Induction

Data Source

PatentUS10466321B2Systems and methods for efficient trajectory optimization in magnetic resonance fingerprinting

Publication Date: 2019.11.05 THE GENERAL HOSPITAL CORP

AI summary

Systems and methods for acquiring magnetic resonance fingerprinting (MRF) data includes performing a schedule optimization that sequentially selects discrimination at each trajectory to yield an optimal trajectory and controlling a magnetic resonance imaging (MRI) system to perform a pulse sequence using the optimal trajectory to acquire MRF data. The process also includes estimating quantitative parameters of the subject using the MRF data by comparing the MRF data to a dictionary database and generating a map of quantitative parameters of the subject using the estimated quantitative parameters of the subject and the MRF data.