Acute Cerebral Infarction Onset Time Estimation via MRI Feature Analysis
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Solution Overview
Problem
In acute cerebral infarction cases, the onset time is often unknown, leading to delayed thrombolysis due to the lack of specialists to accurately assess medical images, and there is a need for a system to quickly determine the feasibility of treatment.
Innovation Solution
An acute cerebral infarction onset time estimation system and method that uses MRI images, specifically diffusion-weighted and fluid-attenuated inversion recovery images, to automatically analyze the infarction region, extract feature information, and calculate the elapsed time since the onset, providing clinicians with timely information for treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialists manually assess medical images to determine acute cerebral infarction onset time, then measurement precision is improved, but loss of time increases due to shortage of specialists and manual analysis duration
Solution Approach 1:
The patent replaces the manual mechanical assessment process with an automated computer-based image processing system. The system uses algorithms to automatically analyze MRI images, extract features from infarction regions, and calculate onset time estimates, substituting human specialists' manual work with automated computational processes that operate faster and without fatigue.
Solution Approach 2:
The system enables self-service by allowing the computer to autonomously perform the complete assessment workflow without human intervention. The automated system independently processes images, extracts relevant features, compares them against reference data, and generates onset time estimates, making the assessment process self-sufficient and eliminating dependency on specialist availability.
2Productivity
If automated image analysis system is implemented, then productivity is improved by reducing analysis time, but measurement precision may deteriorate due to lack of specialist judgment
Solution Approach 1:
The system performs preliminary actions by pre-processing MRI images, automatically segmenting infarction regions, and extracting key features before the actual onset time calculation. This preliminary automated processing prepares the data in advance, enabling rapid analysis while maintaining consistency and reducing variability that can occur in manual assessments.
Solution Approach 2:
The patent replaces manual specialist judgment with automated image processing algorithms that systematically analyze MRI data. The system uses computer vision and pattern recognition techniques to objectively measure infarction characteristics, providing consistent and reproducible results that are not subject to human fatigue or variability.
3Measurement precision
If manual assessment by specialists is used, then measurement precision is maintained, but device complexity increases due to need for specialist expertise and manual processes
Solution Approach 1:
The patent creates a universal system that handles multiple functions within a single integrated platform. The automated system can process different types of MRI sequences (DWI, FLAIR), perform various image processing operations, extract multiple features, and generate comprehensive assessments, replacing the need for multiple specialists with different expertise areas.
Solution Approach 2:
The system substitutes complex manual specialist processes with standardized automated algorithms. By encoding the assessment methodology into computer-executable instructions, the system reduces operational complexity while maintaining measurement precision, making the process more systematic and less dependent on individual specialist knowledge.
Data Source
AI summary
The present disclosure relates to a system and a method for estimating an acute cerebral infarction onset time. The method according to the present disclosure includes at least: receiving a first image and a second image of a first patient whose acute cerebral infarction onset time is not identified; extracting an infarction area image from the second image; aligning the second image with the first image; defining an infarction area in the first image, based on a result of the alignment of the second image with the first image; extracting feature information of the first patient, from the infarction area in the first image; comparing the extracted feature information with reference data; and calculating an amount of time that has elapsed since the acute cerebral infarction onset time, based on a result of the comparison of the extracted feature information with reference data.