TOF MR Angiography Venetian Blind Artifact Correction
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Solution Overview
Problem
Time-of-flight MR angiography techniques suffer from venetian blind artifacts due to signal intensity gradients in pixels with background signal when multiple overlapping target volumes are acquired, leading to edge artifacts in composite MR images.
Innovation Solution
A method to identify and normalize the signal profile of background pixels across the target volume using signal intensity distribution, allowing for the reduction of these artifacts by separating background pixels from vessel and noise pixels, and using morphological information to differentiate between signal types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple overlapping target volumes are acquired to show complete vessel structure, then the coverage of vessel imaging is improved, but venetian blind artifacts appear at the boundaries between target volumes
Solution Approach 1:
The imaging volume is divided into multiple overlapping target volumes, each processed independently. Background pixels are identified and segmented separately from vessel pixels in each target volume, allowing independent normalization that prevents artifact generation at boundaries while maintaining comprehensive coverage.
Solution Approach 2:
Background pixels serve as an intermediary reference standard. By identifying background pixels in each target volume and using their signal intensities as a local reference for normalization, the method creates a mediator that harmonizes the signal intensities across overlapping volumes, eliminating venetian blind artifacts.
2Manufacturing precision
If flip angles are varied across the target volume to prevent signal saturation, then the vessel signal homogeneity is improved, but signal intensity gradients appear in background pixels
Solution Approach 1:
The method applies local quality correction by identifying background pixels in each specific target volume and determining a local signal profile based on the background pixels' signal intensities. This local profile is then used to normalize only the background signal in that specific target volume, allowing different flip angle effects to be compensated locally without affecting vessel signal homogeneity.
Solution Approach 2:
The method changes the parameter of signal intensity by normalizing the background pixel signal intensities in each target volume according to its specific signal profile. This parameter adjustment removes the gradient effects caused by varied flip angles while maintaining the improved vessel signal homogeneity achieved through flip angle variation.
3Measurement precision
If background pixels are separated from vessel and noise pixels, then the signal profile determination accuracy is improved, but the complexity of pixel classification increases
Solution Approach 1:
The background pixels themselves provide the reference standard for their own identification and separation. By using the signal intensity distribution of background pixels to define the separation criteria, the method allows the background pixels to self-identify and serve as their own reference, simplifying the classification process while maintaining high accuracy.
Solution Approach 2:
The method replaces complex multi-parameter classification mechanisms with a simpler signal intensity-based separation approach. By substituting sophisticated classification algorithms with a direct signal intensity thresholding method using background pixel profiles, the system achieves accurate pixel separation with reduced computational complexity.
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 method effectively reduces venetian blind artifacts by normalizing the MR signals, maintaining high contrast between vessels and background signals, and ensuring robust identification of background pixels regardless of the number of vessel or noise pixels, thus improving image quality.
Implementation Method 1
MR signals are acquired in a target volume with the time-of-flight (TOF) MR angiography technique
Implementation Method 2
the pixels with background signal are identified in the angiography images by these background pixels being separated from the noise pixels and vessel pixels
Implementation Method 3
the MR signal of a predetermined set of pixels of the target volume can be normalized with the signal profile of the background pixels, so the effect of the signal intensity gradient in the background pixels can be removed or reduced
Data Source
AI summary
In a method and magnetic resonance (MR) apparatus for correction of artifacts in time-of-flight (TOF) MR angiography, MR signals are acquired in a target volume with the TOF MR angiography technique to generate multiple MR angiography images, pixels with background signal are identified in the angiography images by separation of these pixels from noise and vessel pixels, a signal profile of the pixels with background signal is determined across the target volume, and the MR signal of a predetermined set of pixels of the target volume is normalized with the signal profile of the pixels with background signal.