MRI Gradient Mismatch Correction via Retrospective Factor Selection

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Solution Overview

Problem

Current methods for balancing gradient mismatches in MRI sequences are time-consuming and inflexible, often requiring initial calibration that deteriorates over the scanner's lifetime, leading to artifacts and phase errors, especially in readout and phase encoding directions.

Innovation Solution

A computer-implemented method for determining a correction factor retrospectively by acquiring and processing magnetic resonance raw datasets with varying correction factors to identify the factor minimizing artifacts, allowing for flexible and efficient balancing of gradient mismatches without extending patient scan time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If correction factors are determined by prospectively varying parameters and acquiring multiple images, then gradient mismatch is balanced, but measurement time is extended significantly

Engineering Contradiction:
Improvegradient mismatch balancingVSAvoidmeasurement time
Core Design Contradiction:
ReliabilityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by determining correction factors during a preliminary calibration phase using phantom measurements before actual patient scans. This allows the correction factors to be pre-calculated and stored for later use, avoiding time-consuming corrections during patient examinations. The calibration is performed once during installation or periodically, separating the correction determination from routine patient scans.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The patent uses a phantom copy or simulation model to determine correction factors instead of requiring multiple patient scans. By using a phantom that mimics the imaging conditions and processing multiple images with varying parameters to find optimal correction factors, the system obtains accurate correction data without extending patient measurement time. The phantom serves as a surrogate for actual patient measurements.

Inventive Principle:
Principle #26Copying

2Reliability

If correction factors are determined initially during scanner installation, then gradient mismatch is balanced, but the correction becomes outdated due to material deterioration over time

Engineering Contradiction:
Improvegradient balancing accuracyVSAvoidscanner lifetime
Core Design Contradiction:
ReliabilityVSDuration of action of stationary object

Solution Approach 1:

The patent implements periodic action by scheduling regular recalibration of correction factors at predetermined intervals during the scanner's operational lifetime. The system automatically or manually triggers recalibration procedures periodically, updating correction factors to account for material aging and drift. This ensures the correction factors remain accurate throughout the scanner's lifetime without requiring continuous adjustments during patient scans.

Inventive Principle:
Principle #19Periodic action

Solution Approach 2:

The patent applies feedback by monitoring scanner performance and automatically triggering recalibration when correction factors become outdated. The system can detect drift in gradient performance through quality control measurements or artifact detection, then initiates recalibration to update correction factors. This closed-loop approach ensures correction factors remain accurate without requiring manual intervention or extending patient scan time.

Inventive Principle:
Principle #23Feedback

3Measurement precision

If multiple images are acquired with varying parameters to determine correction factors, then accurate correction is achieved, but patient-specific or measurement-specific corrections cannot be provided

Engineering Contradiction:
Improvecorrection factor accuracyVSAvoidpatient-specific correction capability
Core Design Contradiction:
Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent applies local quality by determining different correction factors for different regions, sequences, or imaging conditions rather than using a single universal correction factor. The system can provide patient-specific or measurement-specific correction factors by tailoring the calibration process to specific scan parameters, coil configurations, or anatomical regions. This allows accurate corrections for each specific imaging scenario without requiring separate calibration studies for each patient.

Inventive Principle:
Principle #3Local quality

Data Source

PatentEP3702798B1Method for obtaining a correction factor, storage medium and magnetic resonance apparatus
Publication Date: 2023.05.31 SIEMENS HEALTHINEERS AG
  • EP3702798B1 patent drawingFigure 1
  • EP3702798B1 patent drawingFigure 2
  • EP3702798B1 patent drawingFigure 3

AI summary

Storage medium, magnetic resonance apparatus and method for obtaining a correction factor (bcf) to balance a mismatch between gradient moments providing the steps: - providing a magnetic resonance raw dataset (70) the generation of which includes: ∘ acquiring the k-space of the magnetic resonance raw dataset (70) in several partial measurements (71), in every partial measurement (71) several k-space lines (55, 56, 57, 58, 59, 60, 61, 62, 92, 93) are at least partially sampled having the steps: ∘ setting a given set of acquisition parameters, ∘ applying at least one radio frequency excitation pulse (12), ∘ applying a first gradient (26) in a predetermined direction (k(x)), ∘ applying a second gradient (27) in the predetermined direction (k(x)), ∘ reading out the magnetic resonance signals (40), - the first gradient (26) being changed between at least two partial measurements (71), - processing the magnetic resonance raw dataset (70) several times to shifted raw datasets (72, 73, 74, 75, 76), each time using a different correction factor (cf1, cf2, cf3, cf4, cf5) to shift the magnetic resonance signals (40, 87) in k-space in the predetermined direction (k(x), k(y)), and - creating several magnetic resonance image datasets (82, 83, 84, 85, 86) out of the shifted raw datasets (72, 73, 74, 75, 76), - determining the correction factor with respect to the image datasets (82, 83, 84, 85, 86).