X-ray Imaging System with Neural Network Anatomy Classification
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Solution Overview
Problem
Current x-ray imaging systems face inefficiencies in workflow and image consistency due to the need for manual selection of protocols and registration of multiple views, leading to potential misregistration and increased user workload.
Innovation Solution
An x-ray imaging system utilizing a trained neural network for automatic anatomy/view classification, which adjusts post-processing parameters and selects acquisition protocols, enabling automated image stitching and improved registration accuracy without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual protocol selection and registration is used, then user control over imaging parameters is maintained, but workflow efficiency decreases and user workload increases
Solution Approach 1:
The system performs automatic anatomy identification and protocol selection without requiring manual user input. The neural network classifies the anatomy and view from the image data, automatically selects the appropriate protocol, and performs registration, allowing the system to serve itself rather than requiring continuous user intervention for these tasks.
Solution Approach 2:
The system dynamically adjusts imaging parameters and post-processing settings based on the automatically classified anatomy and view. By changing parameters automatically based on image content analysis, the system improves workflow efficiency while maintaining appropriate imaging quality for different anatomical regions.
2Measurement precision
If manual registration of multiple views is performed, then registration accuracy can be controlled by the user, but misregistration errors increase and time consumption rises
Solution Approach 1:
The system replaces manual mechanical registration operations with an automated computational approach using neural networks. The AI-based system automatically identifies anatomical features and performs registration across multiple views, eliminating the need for manual point-by-point registration while achieving consistent and accurate results.
Solution Approach 2:
The neural network acts as an intermediary between image acquisition and final image display, automatically performing the registration task. This intermediary system analyzes the images, identifies corresponding anatomical structures across different views, and performs the registration without direct user involvement, reducing both time and potential for human error.
3Ease of operation
If automated anatomy classification is implemented, then workflow simplification and consistency improvement are achieved, but system complexity increases
Solution Approach 1:
The neural network system performs multiple functions including anatomy identification, view classification, protocol selection, and registration. By consolidating these previously separate functions into a single automated system, the interface remains simple for users while the backend handles the complexity of multiple processing tasks through one unified AI model.
Data Source
AI summary
Various methods and systems are provided for x-ray imaging. In one embodiment, a method for an image pasting examination comprises acquiring, via an optical camera and/or depth camera, image data of a subject, controlling an x-ray source and an x-ray detector according to the image data to acquire a plurality of x-ray images of the subject, and stitching the plurality of x-ray images into a single x-ray image. In this way, optimal exposure techniques may be used for individual acquisitions in an image pasting examination such that the optimal dose is utilized, stitching quality is improved, and registration failures are avoided.