MRI RF Shading Correction via Geometry Model Decomposition
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Solution Overview
Problem
Higher field MRI systems, such as 3.0 T, face significant challenges in correcting image intensity non-uniformities caused by non-uniform RF fields, which existing methods fail to adequately address, leading to errors exceeding 25% in image brightness due to factors like penetration and wavelength interference.
Innovation Solution
The proposed solution involves using a geometry model to fit map prescan data, decomposing it into TX and RX components, and applying correction maps to remove central shading effects without additional hardware or scans, adjusting for patient-specific factors like size and tissue composition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher field MRI systems (3.0 T) are used to improve image resolution and signal strength, then image quality and signal-to-noise ratio are improved, but image intensity non-uniformities and shading artifacts increase due to non-uniform RF fields
Solution Approach 1:
The patent applies preliminary action by acquiring map prescan data before the actual MRI scan to characterize the RF field non-uniformities. The system fits a geometry model to the prescan data, decomposes it into TX and RX components, and generates correction maps in advance. These correction maps are then applied during image reconstruction to compensate for the non-uniformities, allowing the system to maintain high field strength benefits while correcting intensity errors.
2Manufacturing precision
If existing correction methods are applied to reduce intensity non-uniformities, then some shading artifacts are reduced, but intensity errors exceed 25% due to inadequate correction of TX and RX non-uniformities
Solution Approach 1:
The patent applies segmentation by dividing the RF field non-uniformity correction into two distinct components: transmit (TX) non-uniformity and receive (RX) non-uniformity. The geometry model fitting and decomposition process separates these effects, allowing independent correction of each component. This segmented approach enables more accurate correction than treating them as a single unified problem, reducing intensity errors from over 25% to under 10%.
3Manufacturing precision
If additional hardware or scan sequences are added to correct RF non-uniformities, then correction accuracy may improve, but scan time and system complexity increase
Solution Approach 1:
The patent applies universality by designing a correction methodology that uses the existing MRI system hardware and standard prescan sequences for multiple purposes. The geometry model fitting approach can correct both TX and RX non-uniformities using a single unified framework, eliminating the need for additional dedicated correction hardware or separate correction scans. This multi-functional approach maintains correction accuracy while avoiding increased scan time.
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 reduces intensity errors to less than 10% and maintains anatomical feature accuracy, improving image uniformity across MRI images without introducing new artifacts or requiring extra hardware or time.
Implementation Method 1
an RF transmitter 34 for generating an RF magnetic field for transmission into a patient volume
Implementation Method 2
an RF receiver 40 for receiving an RF magnetic field emitted by a patient
Data Source
AI summary
An MRI MAP prescan data from a predetermined imaged patient volume is decomposed to produce a transmit RF field inhomogeneity map and a receive RF field inhomogeneity map for the imaged patient volume based on a three-dimensional geometrical model of the inhomogeneity maps. At least one of the transmit RF field inhomogeneity map and the receive RF field inhomogeneity map is used to generate intensity-corrected target MRI diagnostic scan image data representing the imaged patient volume.