Microvessel Ultrasound Imaging with Microbubble Subset Separation
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Solution Overview
Problem
The challenge in ultrasound super-resolution microvessel imaging is the inadequate separation of microbubbles, leading to inaccurate localization due to overlapping echo signals, which is exacerbated by the tradeoff between microbubble concentration and data acquisition time, particularly in clinical settings.
Innovation Solution
A method for generating microvessel images by separating microbubble signal data into subsets based on microbubble properties using spatiotemporal hemodynamics, acoustic characteristics, and machine learning algorithms, allowing for higher microbubble concentrations and reduced data acquisition time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microbubble concentration is lowered to improve microbubble separation and localization accuracy, then measurement precision is improved, but productivity deteriorates due to sparser microbubble events and significantly elongated data acquisition time
Solution Approach 1:
The patent segments the dense microbubble signal data into multiple subsets based on signal intensity thresholds. By dividing the microbubble population into different intensity-based groups, the system can process each subset separately, improving localization accuracy for individual microbubbles while maintaining higher overall concentration for sufficient event rates.
Solution Approach 2:
The patent applies partial action by focusing processing efforts on selecting and localizing only a subset of microbubbles with sufficient signal intensity, rather than attempting to process all microbubbles equally. This selective approach improves localization precision while reducing the effective processing load, thereby shortening acquisition time.
2Productivity
If microbubble concentration is increased to reduce data acquisition time, then productivity is improved, but measurement precision deteriorates due to overlapping echo signals and inadequate microbubble separation
Solution Approach 1:
By segmenting microbubble signals into intensity-based subsets, the system can handle higher concentrations by processing only the most distinguishable signals (those above threshold) in each frame, preventing overwhelming overlap while maintaining high overall concentration for sufficient event rates.
Solution Approach 2:
The patent changes the parameter of signal intensity thresholding to differentiate between resolvable and unresolved microbubble signals. By adjusting and applying intensity thresholds, the system can process dense microbubble populations by selectively focusing on well-separated, high-intensity signals, thereby maintaining precision at higher concentrations.
3Measurement precision
If data acquisition time is elongated to obtain sufficient isolated microbubble signals, then measurement precision is improved, but loss of time increases, which is particularly detrimental in vivo due to tissue and operator-induced motion
Solution Approach 1:
The patent applies partial action by processing only a selected subset of microbubble signals that meet intensity criteria, rather than waiting for and processing all possible microbubble events. This selective processing achieves sufficient statistical sampling for super-resolution imaging more quickly, reducing time loss while maintaining image quality.
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 improves microbubble localization and reduces data acquisition time, enabling more robust super-resolution imaging with increased microbubble detection and enhanced microvasculature reconstruction.
Implementation Method 1
backscattered microbubble signals are used to form images
Implementation Method 2
conventional ultrasound imaging
Data Source
AI summary
Super-resolution ultrasound imaging of microvessels in a subject is described. Ultrasound data are acquired from a region-of-interest in a subject who has been administered a microbubble contrast agent. The ultrasound data are acquired while the microbubbles are moving through, or otherwise present in, the region-of-interest. Microbubble signals are isolated from the ultrasound data and are separated into subsets of data based on properties of the microbubbles, such as spatial-temporal hemodynamics. By localizing, tracking, and accumulating the microbubbles in each subset of data, super-resolution images of the microvessels can be generated for each subset, such that each of these images represents a sparse subset of microbubble signals. These images are combined to generate a super-resolution microvessel image.