X-Ray Image Quality Feedback Using Machine Learning for Rephotography
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Solution Overview
Problem
Existing X-ray chest imaging techniques face challenges in ensuring consistent image quality for accurate diagnosis, particularly in patients who have difficulty inhaling sufficiently, leading to potential missed lesions and cumbersome re-photographing procedures.
Innovation Solution
A system and method that utilizes machine learning to evaluate the quality of X-ray images, specifically focusing on lung area extraction and structural evaluations, providing immediate feedback on whether re-photographing is needed and reasons for suboptimal quality, with expert feedback integration for enhanced learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image quality evaluation by radiologists is performed, then diagnostic accuracy can be ensured, but the process is time-consuming and cannot provide real-time feedback
Solution Approach 1:
The patent replaces the manual mechanical evaluation process by radiologists with an automated machine learning system that uses deep neural networks to evaluate image quality metrics such as inhalation degree, scapular positioning, and lung field coverage, thereby eliminating the time-consuming manual review process while maintaining evaluation accuracy
Solution Approach 2:
The system enables self-service by allowing the X-ray imaging system to automatically evaluate its own output images and provide feedback to operators about quality issues, enabling real-time self-correction without requiring external radiologist intervention
2Reliability
If additional photographing is performed when image quality is insufficient, then diagnostic quality can be ensured, but the procedure becomes cumbersome and difficult for patients
Solution Approach 1:
The system performs preliminary evaluation of image quality immediately after capture using machine learning algorithms, identifying issues such as insufficient inhalation or poor positioning before the patient leaves the imaging area, allowing for immediate correction rather than requiring a separate re-photographing session
Solution Approach 2:
The patent implements a real-time feedback loop where the machine learning system analyzes the captured image, identifies quality deficiencies, and provides immediate guidance to operators for correction, ensuring that only high-quality images proceed to diagnosis while minimizing the need for repeat procedures
3Productivity
If automated machine learning evaluation is implemented, then real-time feedback can be provided, but the system complexity increases
Solution Approach 1:
The patent segments the image quality evaluation into distinct modular components handled by different machine learning models: one model evaluates inhalation degree, another evaluates scapular positioning, and a third evaluates lung field coverage, allowing each component to be independently optimized and managed
Solution Approach 2:
The machine learning system is designed with multi-functionality, using the same infrastructure to perform multiple evaluation tasks including quality assessment, automated positioning guidance, and inhalation feedback, thereby managing complexity through unified architecture rather than separate systems
Data Source
AI summary
A method for feeding back medical image quality is disclosed. The medical image quality feedback method according to the present disclosure includes receiving X-ray image data; extracting a region of interest to be diagnosed from the image data; evaluating a quality of the region of interest depending on a predetermined criterion; and generating feedback information including whether to request rephotographing based on a result of the quality evaluation.