ROI Imaging Workflow for Precise Atherosclerotic Plaque Detection
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Solution Overview
Problem
Current methods for detecting atherosclerotic plaques in blood vessels are inefficient and prone to human error, leading to inaccurate results.
Innovation Solution
A system and method that utilizes a first determination model to determine positioning information of a region of interest (ROI) through a first scan, followed by a second scan with a smaller field of view (FOV) to acquire higher resolution medical imaging data, which is then processed using a second determination model to enhance detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection methods are used for atherosclerotic plaques, then user intervention can identify plaques, but the process is inefficient and inaccurate
Solution Approach 1:
The system enables automatic detection of atherosclerotic plaques through AI models that process medical images independently without requiring manual user intervention. The first determination model automatically identifies the region of interest, and the second determination model automatically detects plaque information, replacing manual detection with autonomous automated processing.
2Measurement precision
If a single scan with large FOV is performed, then the entire imaging area is covered, but the detection precision is insufficient
Solution Approach 1:
The detection process is segmented into two stages: first, a determination model identifies the region of interest (ROI) containing potential plaques from the entire imaging area; second, the system focuses detection resources on this specific ROI to achieve high-resolution plaque characterization. This segmentation allows the system to maintain both broad coverage and high precision.
3Measurement precision
If multiple scans are performed to improve detection accuracy, then detection precision improves, but the time consumption and complexity increase
Solution Approach 1:
The system performs preliminary identification of the region of interest using the first determination model before conducting detailed plaque detection. This preliminary action guides the second determination model to focus computational resources only on the relevant ROI, avoiding unnecessary processing of entire images and significantly reducing detection time while maintaining high accuracy.
4Productivity
If automated determination models are implemented, then labor consumption is reduced and efficiency improves, but the system complexity increases
Solution Approach 1:
The patent introduces determination models as intermediary components that bridge the gap between raw medical images and clinical diagnoses. These models act as intelligent mediators that automatically extract meaningful information from images, reducing the need for manual analysis while providing interpretable results that assist clinicians in decision-making.
Data Source
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AI summary
The present disclosure provides methods and systems for determining information of an ROI of a target subject. The methods may include determining positioning information of the ROI of the target subject by processing first medical imaging data of the target subject using a first determination model. The first medical imaging data may be acquired by performing a first scan with a first field of view (FOV) on the target subject. The methods may include acquiring second medical imaging data of the target subject by performing one or more second scans with a second FOV on the target subject based on the positioning information of the ROI. The methods may further include determining, based on the second medical imaging data, detection information of the ROI.