MRI Confidence Maps for PDFF and R2* Measurement Accuracy

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Solution Overview

Problem

Current methods for estimating proton density fat fraction (PDFF) and R2* in MRI face challenges due to low signal-to-noise ratio (SNR), high iron content, inhomogeneous magnetic fields, and motion, leading to unreliable and biased measurements, especially in regions with water-fat swapping and phase errors.

Innovation Solution

A magnetic resonance imaging (MRI) system and method that generates confidence maps to identify regions with accurate estimates of PDFF and R2*, using a multi-echo gradient echo pulse sequence and computer processing to correct for poor signal quality and water-fat swaps, thereby providing reliable quantitative maps for clinical analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If quantitative mapping methods are used to estimate PDFF and R2*, then quantitative information can be obtained, but measurement reliability deteriorates due to low SNR, high iron content, inhomogeneous magnetic fields, and motion

Engineering Contradiction:
Improvequantitative mapping accuracyVSAvoidmeasurement reliability
Core Design Contradiction:
Measurement precisionVSReliability

Solution Approach 1:

The patent introduces confidence maps as an intermediary product that mediates between the quantitative mapping process and clinical analysis. These confidence maps indicate the reliability of PDFF and R2* estimates in different regions, allowing clinicians to distinguish between accurate measurements and unreliable data without requiring repeated scanning or additional quantitative maps

Inventive Principle:
Principle #24Intermediary (Mediator)

Solution Approach 2:

The confidence maps provide feedback information about the quality of quantitative measurements throughout the imaging process. This feedback mechanism allows for real-time identification of regions with poor signal quality, water-fat swapping, or phase errors, enabling targeted review or correction without requiring complete re-acquisition of the entire dataset

Inventive Principle:
Principle #23Feedback

2Measurement precision

If multi-echo gradient echo pulse sequence is used to acquire chemical-shift encoded MR data, then quantitative information can be extracted, but signal quality deteriorates in regions with water-fat swapping and phase errors

Engineering Contradiction:
Improvequantitative information accuracyVSAvoidwater-fat swapping and phase errors
Core Design Contradiction:
Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent converts the harmful effects of water-fat swapping and phase errors into useful diagnostic information by generating confidence maps that highlight these problematic regions. Rather than attempting to eliminate these artifacts through complex correction algorithms, the system embraces them as informative signals about tissue properties and acquisition quality, allowing clinicians to interpret them appropriately in the clinical context

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

3Reliability

If confidence maps are generated to identify valid and invalid regions, then measurement reliability improves, but device complexity increases

Engineering Contradiction:
Improvemeasurement reliabilityVSAvoidsystem complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The patent merges the confidence map generation functionality into the existing quantitative mapping workflow rather than creating a separate, independent system. The confidence maps are generated simultaneously with the PDFF and R2* quantitative maps using the same multi-echo gradient echo pulse sequence and data processing pipeline, thereby avoiding additional hardware complexity while improving measurement reliability

Inventive Principle:
Principle #5Merging (Combining)

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

The system produces accurate and reliable confidence maps that help clinicians and automated algorithms distinguish valid from invalid regions, improving the reliability of PDFF and R2* measurements and aiding in clinical diagnosis and treatment monitoring.

Implementation Method 1

When a substance, such as human tissue, is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the nuclei in the tissue attempt to align with this polarizing field

Methodology Applied
Scientific EffectMagnetic field polarization: Magnetic Field

Implementation Method 2

the individual magnetic moments of the nuclei in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency

Methodology Applied
Scientific EffectLarmor precession: Precession

Implementation Method 3

If the substance, or tissue, is subjected to a magnetic field (excitation field B1) that is in the x-y plane and that is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or 'tipped', into the x-y plane to produce a net transverse magnetic moment, Mxy

Methodology Applied
Scientific EffectMagnetic resonance: Resonance

Data Source

PatentUS20250102607A1System and method for confidence maps for quantitative mapping with magnetic resonance imaging
Publication Date: 2025.03.27 WISCONSIN ALUMNI RES FOUND
  • US20250102607A1 patent drawing
  • US20250102607A1 patent drawing
  • US20250102607A1 patent drawing

AI summary

A system and method are provided for generating at least one confidence map indicating the accuracy of a quantitative map generated from magnetic resonance (MR) data acquired from a subject. The method includes accessing at least one of proton density fat fraction (PDFF) or R2* maps of a region of interest (ROI) of a subject produced using chemical-shift encoded magnetic resonance (MR) data acquired from the ROI in the subject, generating at least one confidence map that indicates an accuracy of the at least one of the PDFF or R2* maps, and outputting at least one of (i) the at least one confidence map or (ii) a corrected PDFF or R2* map that is corrected using the at least one confidence map.