Medical Image Evaluation System for Tissue Sufficiency Assessment
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Solution Overview
Problem
Current methods for evaluating medical image quality are subjective and lack comprehensive assessment of tissue sufficiency, obstruction, and technical image quality, which hinders accurate interpretation and prognosis.
Innovation Solution
A medical image evaluation method and system that perform image segmentation to delineate tissues of interest, resolve missing tissue, and assess the suitability of images for interpretation, using quantitative and objective criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If subjective visual evaluation is used to assess image quality, then the evaluation process is simple and quick, but the accuracy and objectivity of tissue sufficiency assessment deteriorates
Solution Approach 1:
The patent replaces the mechanical/visual evaluation system with an automated computational system that uses image processing algorithms, machine learning models, and quantitative metrics to objectively assess tissue sufficiency, obstruction, and technical image quality, thereby improving measurement precision while maintaining evaluation efficiency
Solution Approach 2:
The patent introduces an intermediary automated evaluation system that acts as a bridge between the acquired medical image and the final interpretation, providing objective quantitative assessments of tissue sufficiency, obstruction levels, and technical quality metrics that enhance the reliability of clinical decision-making
2Reliability
If comprehensive assessment of tissue sufficiency, obstruction, and technical image quality is performed, then the accuracy of image interpretation improves, but the complexity of the evaluation system increases
Solution Approach 1:
The patent segments the comprehensive image quality assessment into three distinct modular components: tissue sufficiency evaluation, obstruction detection and quantification, and technical image quality assessment. Each component can be independently developed, validated, and optimized, reducing overall system complexity while maintaining comprehensive evaluation capability
Solution Approach 2:
The patent designs a universal automated evaluation system that performs multiple functions including tissue sufficiency assessment, obstruction detection, technical quality metrics calculation, and interpretation suitability determination, thereby achieving comprehensive assessment through a single integrated platform rather than multiple separate systems
3Illumination intensity
If image processing is applied to enhance visual assessment, then image quality perception improves, but image artefacts and noise may be introduced that alter feature perception
Solution Approach 1:
The patent performs preliminary assessment of tissue sufficiency, obstruction, and technical quality metrics before any interpretive analysis is conducted. This preliminary evaluation identifies potential issues with processing-induced artefacts and noise, allowing the system to flag images that may require alternative processing approaches or repeat acquisition
Solution Approach 2:
The patent implements feedback mechanisms where the automated evaluation system continuously monitors image quality metrics throughout the processing pipeline, adjusting processing parameters in real-time to minimize the introduction of artefacts and noise while maintaining enhanced visual assessment quality
Data Source
AI summary
A system and method are disclosed for quantitative evaluation of image acquisition quality within a field of view and derivation of associated qualitative metrics to inform interpretation. The medical image evaluation system comprises: a data input device to acquire data of medical images: and a data processor to perform an image segmentation on one or more of the medical image(s) of a body portion to delineate tissue(s) of interest from a surrounding region within a field of view. The data processor is configured to resolve any missing tissue which is any portion of the tissue(s) of interest missing from the image segmentation. The data processor is configured to quantify suitability of the medical images for interpretation by making an estimate of amount or location of the missing tissue. A data device is configured to advise a user of the quantified suitability of the medical images for interpretation.