MR Image Reconstruction Using Modulated Reference Data
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Solution Overview
Problem
In magnetic resonance imaging, undersampled raw data and reference data often exhibit different imaging characteristics due to external influences like frequency deviations, leading to reconstruction artifacts in parallel imaging techniques.
Innovation Solution
A method that determines a disturbance variable affecting the imaging characteristics and uses a modulation function to adapt the reference data to match the imaging characteristics of the raw data, ensuring that the modulated reference data are influenced by the same disturbances as the raw data, thereby reducing artifacts through a combination algorithm that reconstructs image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If reference data are acquired with a different sampling scheme than raw data, then reference data acquisition time is reduced, but imaging characteristics differ causing reconstruction artifacts
Solution Approach 1:
The patent applies preliminary action by acquiring reference data before raw data with a different sampling scheme, then using a modulation function to adapt the reference data to match the imaging characteristics of the raw data. This allows flexible reference data acquisition while eliminating artifacts through post-acquisition modulation, resolving the contradiction between reduced acquisition time and maintained image quality.
Solution Approach 2:
The patent changes parameters by applying a modulation function that adjusts the sampling scheme, timing, and imaging characteristics of the reference data to match those of the raw data. This parameter adaptation enables reference data to be acquired efficiently with different characteristics initially, then corrected to ensure artifact-free reconstruction.
2Reliability
If reference data are acquired with the same sampling scheme as raw data, then imaging characteristics match reducing artifacts, but reference data acquisition time increases
Solution Approach 1:
The patent performs preliminary acquisition of reference data with a simplified or different sampling scheme, then applies modulation to match the raw data characteristics. This preliminary action with flexible sampling reduces initial acquisition time while the subsequent modulation step ensures image quality is maintained.
Solution Approach 2:
The patent introduces dynamics by allowing the sampling scheme for reference data to be flexible and adaptive rather than fixed. The modulation function dynamically adjusts the reference data characteristics based on the raw data properties, enabling optimal balance between acquisition speed and image quality.
3Measurement precision
If a larger k-space matrix is used to increase resolution, then image resolution is improved, but measurement time increases
Solution Approach 1:
The patent applies partial action by acquiring reference data with undersampling (not fully populating the k-space matrix) while still achieving the desired resolution through modulation and combination with raw data. This partial acquisition of reference data reduces measurement time while maintaining the ability to reconstruct high-resolution images.
Solution Approach 2:
The patent changes parameters by using modulation functions that adjust the effective sampling density and k-space utilization. This allows the system to achieve high resolution through parameter optimization rather than simply increasing the amount of data acquired, thereby reducing measurement time.
Data Source
AI summary
In a method and magnetic resonance (MR) apparatus for reconstruction of image data of an examination object from undersampled raw MR data and reference data, undersampled raw data are used that were recorded from an examination region of the examination object by an MR control sequence, to which a reconstruction algorithm for the reconstruction of image data is assigned. A disturbance variable in the examination region is determined. A modulation function is established, which describes the influence of the disturbance variable on the MR control sequence. Modulated reference data are created on the basis of the modulation function and the reference data such that the modulated reference data are subjected to the influence of the disturbance variable on the raw data recorded by the MR control sequence. A combination algorithm is executed in order to reconstruct image data from the undersampled raw data with the use of the modulated reference data.