Spectral X-ray Material Density Map Calculation

Solution Overview

Problem

Existing methods for generating material density maps, such as those used in stroke diagnosis, require prior knowledge of precise attenuation values, which can be impractical and lead to inferior results.

Innovation Solution

A system that determines material density maps by analyzing spectral data to identify material clusters, determining their geometrical constellation, and projecting auxiliary material clusters onto a subspace defined by the target material cluster, without relying on predefined attenuation values.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If stock values from reference tables are used for attenuation values, then the process is simplified, but the result quality deteriorates

Engineering Contradiction:

Improveprocess simplicityVSAvoidmap accuracy

Core Design Contradiction:

Ease of manufactureVSManufacturing precision

Solution Approach 1:

The system determines attenuation values autonomously by analyzing the spectral data itself, without requiring external reference tables or prior knowledge. The spectral data contains embedded information about the materials present, and the system extracts this information through clustering analysis, allowing the data to serve itself rather than requiring external calibration

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent introduces material clusters as an intermediary representation between raw spectral data and the final density map. These clusters group voxels with similar attenuation characteristics, and their geometrical constellation provides a bridge to determine both attenuation values and material distribution, mediating between the spectral measurements and the quantitative map

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If precise attenuation values are known a priori, then material separation accuracy is improved, but the applicability and ease of operation deteriorate

Engineering Contradiction:

Improvematerial separation accuracyVSAvoidapplicability

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system determines attenuation values autonomously by analyzing the spectral data itself, without requiring external reference tables or prior knowledge. The spectral data contains embedded information about the materials present, and the system extracts this information through clustering analysis, allowing the data to serve itself rather than requiring external calibration

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent transforms the problem from requiring fixed, known attenuation values to dynamically determining attenuation values based on the geometrical constellation of material clusters in spectral space. This parameter transformation allows the system to adapt to different material compositions and imaging conditions without requiring precise prior knowledge

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If conventional material decomposition is used, then the process is straightforward, but artifacts and reduced contrast occur

Engineering Contradiction:

Improveprocess complexityVSAvoidimage quality

Core Design Contradiction:

Device complexityVSManufacturing precision

Solution Approach 1:

The patent introduces material clusters as an intermediary representation between raw spectral data and the final density map. These clusters group voxels with similar attenuation characteristics, and their geometrical constellation provides a bridge to determine both attenuation values and material distribution, mediating between the spectral measurements and the quantitative map

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent moves the analysis from traditional image space to spectral data space, where materials form distinct clusters with specific geometrical constellations. This dimensional transformation allows for more accurate material separation by exploiting the spectral signatures of materials, which appear as structured patterns in the higher-dimensional spectral space rather than simple intensity variations

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS20250131558A1Improved water map calculation in spectral x-ray

Publication Date: 2025.04.24 KONINKLIJKE PHILIPS NV

AI summary

System (S-SYS) and related method for determining a material density map for a target material. The system (S-SYS) may receive spectral data representable in a two-dimensional data space. The spectral data may include measurements acquired by a spectral imaging apparatus of an object in a three-dimensional image domain of the spectral imaging apparatus. The system determines clusters in the data space, one indicative of the target material, the target material cluster, and clusters indicative auxiliary materials, the auxiliary material clusters. The system may determine a mutual geometrical constellation of the clusters. The system determines the material density map based on the geometrical constellation so determined.