X-ray Dissectography Module for 3D Feature Extraction
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Solution Overview
Problem
Current x-ray imaging technologies face challenges in improving radiogram quality due to superimposed organs and tissues, which complicates diagnosis, and CT scans expose patients to high radiation doses and are costly.
Innovation Solution
The development of an x-ray dissectography module that uses artificial neural networks to extract a region of interest from 2D radiographs by generating 3D feature sets and suppressing other structures, enhancing image quality and facilitating 3D visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CT scan is used to produce three-dimensional images for separating overlapping organs and tissues, then imaging quality is improved, but radiation dose increases significantly and cost increases
Solution Approach 1:
The patent segments the imaging process into two stages: first capturing multiple 2D radiographs at low radiation dose, then using AI-based dissectography to separate and reconstruct 3D features from these segmented 2D images. This avoids the need for a single high-radiation CT scan while achieving comparable 3D imaging quality.
Solution Approach 2:
The patent creates virtual 3D copies of anatomical structures from 2D radiographs using artificial intelligence. The dissectography module generates 3D feature sets that replicate the information content of CT scans without requiring the high radiation exposure associated with actual CT imaging.
2Measurement precision
If CT scan is used to produce three-dimensional images for separating overlapping organs and tissues, then imaging quality is improved, but cost increases
Solution Approach 1:
The patent uses inexpensive 2D radiographs as disposable input data that can be captured quickly and processed digitally. Instead of requiring expensive CT imaging hardware and operation, the system uses multiple low-cost radiographs processed through AI-based dissectography to achieve high-quality 3D visualization.
Solution Approach 2:
The patent replaces the mechanical and hardware-intensive CT scanning system with a software-based AI dissectography module that processes standard radiographs. This substitution of mechanical imaging with intelligent software processing significantly reduces equipment cost and operational expense while maintaining imaging quality.
3Object-affected harmful factors
If x-ray radiography is used to capture two-dimensional images, then radiation dose is reduced and cost is reduced, but diagnostic performance deteriorates due to superimposed organs and tissues
Solution Approach 1:
The patent transforms 2D radiograph data into 3D feature representations through AI-based dissectography. By adding the third dimension virtually, the system separates overlapping anatomical structures that appear superimposed in 2D, thereby improving diagnostic performance without increasing radiation dose.
Solution Approach 2:
The patent introduces an intermediary AI-based dissectography module that processes the 2D radiographs and extracts 3D features. This intermediary layer bridges the gap between low-dose 2D imaging and high-quality 3D visualization, enabling diagnostic performance comparable to CT while maintaining low radiation exposure.
Data Source
AI summary
In one embodiment, there is provided a dissectography module for dissecting a two-dimensional (2D) radiograph. The dissectography module includes an input module, an intermediate module, and an output module. The input module is configured to receive a number K of 2D input radiographs, and to generate at least one three-dimensional (3D) input feature set, and K 2D input feature sets based, at least in part, on the K 2D input radiographs. The intermediate module is configured to generate a 3D intermediate feature set based, at least in part, on the at least one 3D input feature set. The output module is configured to generate output image data based, at least in part, on the K 2D input feature sets, and the 3D intermediate feature set. Dissecting corresponds to extracting a region of interest from the 2D input radiographs while suppressing one or more other structure(s).