Automatic Correction Factor Determination for MR Imaging
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Solution Overview
Problem
The manual determination of correction factors for magnetic resonance (MR) images is time-consuming and prone to errors, requiring repeated calibration when system hardware specifications change, especially in the development phase of MR imaging sequences like RESOLVE and PETRA.
Innovation Solution
An automated method for determining correction factor values by producing multiple MR images with different parameter combinations, evaluating them for artifacts, and identifying the most artifact-free image to determine optimal correction factors, using techniques such as pixel value derivation, Fourier coefficients, similarity analysis, or machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual determination of correction factors is performed, then accuracy of correction factor values can be achieved through expert evaluation, but the process is time-consuming and prone to human error
Solution Approach 1:
The system performs self-calibration by automatically evaluating MR images against reference images to determine optimal correction factors without human intervention. The magnetic resonance system itself carries out the calibration process that previously required manual expert evaluation, making the system self-sufficient for correction factor determination
Solution Approach 2:
The manual mechanical process of expert evaluation and manual selection of correction factors is replaced by an automated computational system. The system uses algorithmic comparison of MR images with reference images to automatically determine correction factors, substituting human manual operations with automated image processing and analysis
2Reliability
If manual calibration is performed for each hardware configuration change, then accurate correction factors can be determined, but the process must be repeatedly repeated during development
Solution Approach 1:
The system automatically adapts to hardware changes by performing self-calibration whenever gradient coil designs or other hardware specifications change. Instead of requiring repeated manual calibration, the system autonomously determines new correction factors by comparing newly acquired MR images against reference images, making the calibration process adaptive to hardware modifications
Solution Approach 2:
The system handles hardware configuration changes by detecting changes in system parameters (such as gradient coil design) and automatically adjusting correction factors accordingly. The calibration process dynamically adapts to parameter changes in hardware specifications through automated image comparison and correction factor recalculation
3Adaptability or versatility
If comprehensive calibration measurements are performed for all parameter combinations, then complete coverage of parameter space is achieved, but the number of required measurements increases significantly
Solution Approach 1:
A set of reference images is acquired in advance under ideal conditions with known optimal correction factors. These reference images serve as a benchmark for subsequent automated comparisons, allowing the system to determine correction factors for new parameter combinations by comparing against the pre-acquired reference images rather than performing exhaustive calibration measurements
Solution Approach 2:
The system creates virtual references by comparing newly acquired MR images against stored reference images. Instead of physically performing exhaustive calibration measurements for every possible parameter combination, the system uses image comparison algorithms to copy and adapt correction factors from reference cases to new parameter combinations, significantly reducing the number of required measurements
Data Source
AI summary
The disclosure relates to the automatic determination of correction factor values for producing MR images using a magnetic resonance system. A plurality of MR images is produced, wherein each MR image is produced using parameters with parameter values and using correction factors with correction factor values. In order to produce the MR images, MR data of the same examination object is acquired under the same external boundary conditions. The MR images are evaluated automatically in respect of artifacts in the respective MR image, in order to determine the MR image with the least artifacts among the MR images. The correction factor values are determined as those correction factor values which have been used to produce the MR image with the least artifacts. The parameters determine a sequence, with which the MR data is acquired for producing the MR images. The correction factors reduce influences which influence the acquisition of the MR data.