MRI Image Normalization via Coil Sensitivity Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Magnetic resonance imaging (MRI) systems face challenges in maintaining image quality during table movement during data acquisition, as intensity variations due to varying coil sensitivity can lead to false positives and hinder anatomical assessment, especially when using local coils with different sensitivities.
Innovation Solution
A method and system that normalize image data based on measured coil sensitivity data, acquired using both local and volume coils, to correct intensity variations caused by differing coil sensitivities, allowing for accurate image reconstruction even during table movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If local coils are used during table feed to acquire raw imaging data, then signal-to-noise ratio is improved, but intensity variations occur due to varying coil sensitivity
Solution Approach 1:
The system performs preliminary measurement of coil sensitivity data before or during the acquisition of raw imaging data. This preliminary action enables subsequent normalization of image data to correct intensity variations, allowing local coils to be used for their high signal-to-noise ratio while maintaining image intensity uniformity through pre-acquired sensitivity information.
2Reliability
If multiple local coils with different sensitivities are used, then image quality is improved, but false positives increase due to intensity variations
Solution Approach 1:
The system uses measured coil sensitivity data as feedback to normalize image data. The sensitivity measurements provide information about each coil's characteristics, which is then applied to correct intensity variations in the reconstructed images. This feedback mechanism ensures that multiple local coils can be used to improve image quality while maintaining diagnostic accuracy by compensating for sensitivity differences.
3Productivity
If table movement is used during data acquisition, then productivity is improved, but image intensity uniformity deteriorates due to coil sensitivity variations
Solution Approach 1:
The system performs preliminary measurement of coil sensitivity data at different table positions during the acquisition process. This preliminary action at multiple positions enables subsequent normalization that accounts for table movement effects, allowing continuous table feed to maintain productivity while preserving image intensity uniformity through position-dependent sensitivity corrections.
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 normalization of image data using coil sensitivity data effectively corrects intensity variations, improving image quality and reducing false positives, enabling more accurate anatomical assessments and diagnostic evaluations.
Implementation Method 1
Radio-frequency excitation signals (RF pulses)—known as the 'B1 field'—are then emitted via a radio-frequency transmission system by means of suitable antenna devices, which cause the nuclear spins of specific atoms excited to resonance by this radio-frequency field
Implementation Method 2
Upon relaxation of the nuclear spins, radio-frequency signals (known as magnetic resonance signals) are radiated that are received by suitable reception antennas
Implementation Method 3
a normalization unit (15) which is designed to implement a normalization of the image data on the basis of measured coil sensitivity data of the local coils that are used
Data Source
AI summary
A method and magnetic resonance tomography system to generate magnetic resonance image data of an examination subject, raw imaging data are acquired from multiple slices of a predetermined volume region of the examination subject using local coils during a table feed in the magnetic resonance scanner. Image data of the slices are reconstructed on the basis of the raw imaging data. A normalization of the image data is subsequently implemented on the basis of measured coil sensitivity data of the local coils that are used.