AI X-ray Image Orientation Correction via DICOM Tag Automation
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Solution Overview
Problem
Current X-ray imaging systems require significant time and effort for technicians to manually review and label X-ray images with DICOM tags, which can lead to errors and inefficiencies in image processing and orientation.
Innovation Solution
An AI-powered X-ray image information detection and correction system automatically scans post-exposure X-ray images to detect anatomy, view, orientation, and laterality, and can correct anatomical orientation errors, thereby reducing manual intervention and improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and labeling of X-ray images with DICOM tags is performed by technicians, then accurate identification of anatomy, view, orientation and laterality is achieved, but significant time is required and errors may occur
Solution Approach 1:
The system enables self-service by allowing the X-ray imaging system to automatically perform the identification and labeling functions that previously required technician intervention. The system extracts anatomy, view, orientation and laterality information automatically and generates DICOM tags without human input, making the system serve itself rather than requiring external manual processing.
Solution Approach 2:
The patent replaces the mechanical manual process of review and data entry with an automated computational system. Instead of technicians manually examining images and typing DICOM tags, the system uses automated image analysis algorithms to extract relevant information and populate DICOM metadata, substituting human mechanical actions with computational processes.
2Reliability
If technicians manually enter DICOM tags for X-ray images, then identifying information is stored, but incorrect tags may be entered requiring time-consuming discovery and correction
Solution Approach 1:
The system performs self-verification by automatically checking the extracted information against the X-ray image characteristics and validating the generated DICOM tags. The system identifies and corrects its own errors without requiring external review, ensuring high reliability while eliminating the time loss associated with manual error detection and correction.
Solution Approach 2:
The system implements feedback mechanisms where the automatically extracted information is validated against multiple criteria including image features, anatomical consistency, and DICOM standards. This feedback loop ensures accuracy by continuously verifying the extracted data and correcting any inconsistencies before finalizing the DICOM tags.
3Ease of operation
If orientation/rotation of anatomy in X-ray images is adjusted during initial review, then desired view of anatomy is achieved, but significant time is required for re-positioning
Solution Approach 1:
The system performs preliminary action by automatically determining the correct orientation and rotation of anatomy before the radiologist reviews the image. The system analyzes the X-ray image to identify anatomical structures and their proper orientation, pre-adjusting the image presentation so that the desired view is immediately available without requiring time-consuming manual re-positioning during review.
Solution Approach 2:
The system enables self-service by automatically performing the orientation and rotation adjustments that previously required technician or radiologist intervention. The system analyzes the image characteristics and autonomously repositions anatomical structures to their correct orientation, eliminating the need for manual manipulation during the review process.
Data Source
AI summary
An artificial intelligence (AI) X-ray image information detection and correction system is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to automatically scan post-exposure X-ray images to detect various types of information or characteristics of the X-ray image, including, but not limited to, anatomy, view, orientation and laterality of the X-ray image, along with an anatomical landmark segmentation. The information detected about the X-ray image can then be stored by the AI system in association with the X-ray image for use in various downstream X-ray system workflow automations and/or reviews of the X-ray image.