Photon Counting Spectral Imaging from Conventional X-Ray Data
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Solution Overview
Problem
The high cost and complexity of photon-counting CT systems, along with the need for specialized hardware and imaging protocols, have hindered their widespread adoption in clinical settings, despite their potential for improved material quantification and reduced noise in CT imaging.
Innovation Solution
A deep learning regression algorithm is employed to convert non-photon counting X-ray spectral data into photon counting X-ray spectral data, utilizing dual-energy CT data and existing hardware, without the need for specialized photon-counting acquisition systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized photon-counting CT hardware and acquisition systems are used, then measurement precision and material quantification are improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of photon-counting CT data by using deep learning algorithms to transform conventional spectral CT data into photon-counting spectral images. This copying approach allows the system to generate photon-counting quality images without requiring actual photon-counting detector hardware, thereby reducing device complexity while maintaining measurement precision
Solution Approach 2:
The patent replaces the mechanical/physical photon-counting detection system with a computational approach using deep learning regression algorithms. Instead of relying on specialized hardware to count photons directly, the system uses software-based transformation of conventional spectral data to achieve photon-counting equivalent results, substituting physical detection mechanisms with computational processing
2Device complexity
If conventional spectral CT scanners are used, then device complexity is reduced, but spectral information and material differentiation capability are lost
Solution Approach 1:
The patent introduces deep learning regression algorithms as an intermediary processing layer between conventional spectral CT data acquisition and final image generation. This intermediary transforms the conventional spectral data into photon-counting spectral images, preserving and enhancing spectral information without requiring complex specialized hardware
Solution Approach 2:
The patent changes the parameter representation of spectral data by transforming conventional spectral measurements into photon-counting spectral image formats through deep learning. This parameter transformation allows the system to maintain spectral information and material differentiation capability while using simpler conventional scanner hardware
3Ease of manufacture
If dual-energy CT approaches are used, then spectral imaging capability is achieved, but K-edge information and quantitative accuracy are limited
Solution Approach 1:
The patent applies dynamic processing by using deep learning regression algorithms that can adaptively transform dual-energy CT data into photon-counting spectral images. This dynamic computational approach enhances the static dual-energy data to provide K-edge information and improved quantitative accuracy, going beyond the limitations of conventional dual-energy methods while maintaining ease of implementation with existing hardware
Data Source
AI summary
The present invention relates to an apparatus (10) for generating photon counting spectral image data, comprising: an input unit (20); a processing unit (30); and an output unit (40). The input unit is configured to receive non-photon counting X-ray spectral energy data. The processing unit is configured to implement a deep learning regression algorithm to generate photon counting X-ray spectral data, and the generation comprises utilization of the non-photon counting X-ray spectral energy data. The output unit is configured to output the photon counting X-ray spectral data.