Liver Fat Content Determination via Spectral Subrange Segmentation
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Solution Overview
Problem
Current magnetic resonance techniques face challenges in accurately determining liver fat content due to interference from other substances like iron, which can broaden the frequency range of spectral data, making it difficult to reliably diagnose conditions such as hepatic steatosis.
Innovation Solution
A method that subdivides the frequency range into subranges based on spectral characteristics of fat and water, approximates the contribution from water and other substances, and determines the fat content by isolating the first contribution from fat signals, accounting for the effects of other substances on the spectral data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral magnetic resonance data is used to determine liver fat content, then measurement capability is provided, but interference from other substances like iron broadens the frequency range making accurate determination difficult
Solution Approach 1:
The frequency range of the spectral magnetic resonance data is divided into multiple subranges, with each subrange corresponding to signals from different substances (fat, water, iron). By segmenting the frequency spectrum, the method isolates fat signals from interfering substances, enabling accurate fat content determination despite the presence of other substances that broaden the overall frequency range.
2Adaptability or versatility
If the frequency range is broadened to account for other substances, then all substances are detected, but fat content determination becomes unreliable
Solution Approach 1:
The method segments the broad frequency range into distinct subranges, each assigned to specific substances based on their characteristic resonance frequencies. This segmentation allows the system to maintain versatility in detecting multiple substances while ensuring reliability in fat content determination by analyzing only the subrange corresponding to fat signals, effectively filtering out interference from other substances.
Solution Approach 2:
The method extracts and isolates the fat-specific signal contribution from the total spectral magnetic resonance data by identifying and separating the frequency subrange attributable to fat. This extraction process removes interfering signals from other substances, enabling reliable fat content determination even when the overall frequency range is broadened by the presence of multiple substances.
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 allows for a simple and accurate determination of liver fat content, providing reliable diagnostics for conditions like hepatic steatosis, even in the presence of other substances, by effectively isolating and quantifying the fat contribution from spectral magnetic resonance data.
Implementation Method 1
Radio-frequency (RF) pulses, for instance excitation pulses, are then emitted by an RF antenna using suitable antenna coils, causing the nuclear spins of certain atoms, which spins have been excited to resonance by these RF pulses
Implementation Method 2
Spectral magnetic resonance data are characterized by the magnetic resonance signals being recorded with their corresponding frequencies. Spectral magnetic resonance data can be associated with a frequency at which the corresponding MR signals were emitted by the nuclear spins. On the basis of spectral magnetic resonance data, information can be extracted about the environment of the nuclear spins
Data Source
AI summary
In a method and apparatus for determining the fat content of the liver of a patient using measured spectral magnetic resonance data, wherein a first contribution to the spectral magnetic resonance data is based on fat, a second contribution to the spectral magnetic resonance data is based on water and on a further substance, a frequency range that forms the basis of the spectral magnetic resonance data is subdivided at least into a first subrange and into a second subrange, the second contribution is approximated using the spectral magnetic resonance data from the second subrange. The first contribution is determined taking into account the spectral magnetic resonance data and the approximation of the second contribution. The fat content is determined on the basis of the first contribution.