Spiral MR Imaging B0 Inhomogeneity Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Non-Cartesian MR imaging techniques, such as spiral imaging, are vulnerable to B0 inhomogeneities, leading to blurring and mal-sampling artefacts that affect image quality, especially in regions with strong susceptibility-induced magnetic field gradients, where conventional de-blurring methods fail to correct for remaining artefacts.
Innovation Solution
A method using a deep learning network to detect and correct mal-sampling artefacts by deriving an artefact map from MR images, combining B0 map-based de-blurring with deep learning to identify and correct artefacts, particularly in regions with strong magnetic field inhomogeneities, and utilizing anatomical atlases to restrict corrections to relevant areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If non-Cartesian k-space trajectories (spiral imaging) are used to achieve fast imaging and efficient k-space coverage, then imaging speed and productivity are improved, but image quality deteriorates due to blurring and mal-sampling artefacts caused by B0 inhomogeneities
Solution Approach 1:
A B0 map is acquired before the main imaging sequence to characterize magnetic field inhomogeneities. This preliminary measurement allows the system to prepare correction parameters and trajectory adjustments in advance, enabling the subsequent spiral imaging to compensate for off-resonance effects and reduce mal-sampling artefacts while maintaining fast imaging speed
Solution Approach 2:
The system uses the acquired B0 map as feedback to adjust the spiral k-space trajectory and imaging parameters. The measured field inhomogeneities are fed back into the reconstruction algorithm to correct the non-Cartesian data, creating a closed-loop system that maintains image quality despite the fast non-Cartesian sampling approach
2Manufacturing precision
If conventional de-blurring methods are applied to correct B0 inhomogeneity effects, then image clarity is improved in general areas, but mal-sampling artefacts in regions with strong susceptibility-induced magnetic field gradients remain uncorrected
Solution Approach 1:
The system applies different correction strategies to different regions of the image based on local B0 inhomogeneity characteristics. In regions with strong susceptibility-induced field gradients, the spiral trajectory is adjusted or additional correction steps are applied, while in uniform regions standard de-blurring suffices. This localized approach ensures reliable artefact correction across all image regions
Solution Approach 2:
The system dynamically changes imaging parameters such as spiral trajectory timing, gradient amplitudes, and sampling rates based on the measured B0 map. In regions with strong field gradients, parameters are adjusted to maintain adequate k-space sampling, preventing mal-sampling artefacts while preserving overall image clarity
3Manufacturing precision
If long acquisition intervals are used in spiral imaging to achieve high spatial resolution when combined with parallel imaging, then manufacturing precision is improved, but vulnerability to B0 inhomogeneities increases causing severe blurring
Solution Approach 1:
The B0 map is acquired beforehand to identify regions with significant field inhomogeneities. This preliminary information allows the system to adjust the spiral acquisition parameters in advance, such as reducing the acquisition interval in problematic regions or applying targeted correction, thereby maintaining high spatial resolution while mitigating sensitivity to B0 variations
Solution Approach 2:
The system integrates multiple functions into a unified imaging protocol: the B0 mapping sequence serves both as a diagnostic tool and as the basis for correcting subsequent high-resolution spiral images. The same system hardware and processing pipeline handle both the field mapping and the corrected imaging, enabling the system to achieve high spatial resolution while compensating for B0 inhomogeneities through the unified approach
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 enables high-quality non-Cartesian MR imaging by automatically identifying and correcting mal-sampling artefacts, improving image clarity and diagnostic validity even in areas with significant B0 inhomogeneities and steep gradients.
Implementation Method 1
a main magnet coil for generating a uniform static magnetic field within an examination volume
Implementation Method 2
a number of gradient coils for generating time-dependent magnetic field gradients in different spatial directions within the examination volume
Implementation Method 3
at least one RF coil for generating RF pulses within the examination volume and/or for receiving MR signals from an object positioned in the examination volume
Data Source
Figure 1
Figure 2~3
Figure 4a~4b
AI summary
The invention relates to a method of MR imaging of an object (10) positioned in an examination volume of a MR device (1). It is an object of the invention to enable efficient and high-quality non-Cartesian MR imaging, even in situations of strong B0 inhomogeneity. In accordance with the invention, the method comprises: - subjecting the object to an imaging sequence comprising at least one RF excitation pulse and modulated magnetic field gradients, - acquiring MR signals along at least one non-Cartesian k-space trajectory, - reconstructing an MR image from the acquired MR signals, and - detecting one or more mal-sampling artefacts caused by B0 inhomogeneity induced insufficient k-space sampling in the MR image using a deep learning network. Moreover, the invention relates to a MR device (1) and to a computer program.