MRI Chemical Species Separation via Dual-Echo Voting
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Solution Overview
Problem
Current MRI systems face challenges in accurately separating signals from multiple chemical species, such as water and fat, which can lead to inaccurate image representation and hinder disease diagnosis due to signal overlap and water-fat swapping issues.
Innovation Solution
The method involves acquiring multiple MR images at different echo times, selecting dual-echo pairs, and separately processing them to estimate the B0 field map and separate chemical species, using frequency shifts and voting methods to generate accurate water and fat images, thereby improving chemical species separation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI signal acquisition is used, then multiple chemical species signals are acquired simultaneously, but signal separation accuracy deteriorates due to signal overlap and water-fat swapping
Solution Approach 1:
The patent divides the signal acquisition process into multiple separate acquisitions at different echo times. By segmenting the acquisition into multiple time points, the system can separately process and assign signals to different chemical species (water and fat), preventing signal overlap and improving separation accuracy.
Solution Approach 2:
The patent performs preliminary signal acquisition at multiple predetermined echo times before processing. By collecting signals at multiple known time points with known phase evolution characteristics, the system establishes a foundation for accurate chemical species separation through subsequent processing using the acquired phase information.
2Measurement precision
If multiple echo times are acquired and processed separately, then chemical species separation improves, but processing complexity increases
Solution Approach 1:
The patent changes the temporal parameter (echo time) across multiple acquisitions to create distinguishable signal characteristics for different chemical species. By acquiring signals at multiple echo times with different phase evolutions, the system creates parameter variations that enable separation while using standard MRI acquisition capabilities.
Solution Approach 2:
The patent uses the phase information from acquired signals as feedback to determine chemical species assignment. The processing algorithm uses phase differences between echo times to feedback-determine whether signals belong to water or fat, creating an automated separation process that reduces manual intervention complexity.
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 more accurate separation of chemical species, reducing signal overlap and improving image quality by generating precise water and fat images, enhancing diagnostic capabilities.
Implementation Method 1
Magnetic resonance imaging (MRI) systems and methods are widely used, particularly for medical imaging and diagnosis
Implementation Method 2
using frequency shifts and voting methods to generate accurate water and fat images
Implementation Method 3
The images can include signals from multiple chemical species in the subject's body
Data Source
AI summary
A method and apparatuses are provided to perform chemical species separation in magnetic resonance (MR) imaging (MRI). At least three MR images corresponding respectively to different echo times are obtained and represent signals from multiple chemical species including a first species and a second species in a tissue. A plurality of dual-echo pairs is selected from the at least three MR images. For each pair, a set of dual-echo separated images including a B0 field map, a first image for the first species, and a second image for the second species is estimated. An initial set of combined images including at least one of: an initial combined B0 field map, first, and second image is generated by combining at least one of: two or more of the B0 field maps, two or more of the first images, and two or more of the second images.