Multispectral CT Imaging for Tissue Characterization
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Solution Overview
Problem
Current computed tomography imaging technologies face challenges in accurately representing all regions of the human body within appropriate time frames for medical indications, particularly in ischemic stroke cases, where timely diagnosis and treatment are critical due to limitations in imaging capabilities beyond 4.5 hours post-stroke, leading to potential hemorrhage risks and suboptimal therapeutic decisions.
Innovation Solution
A method and device for computed tomography imaging using multispectral data recording combined with iterative data reconstruction and machine learning algorithms to suppress tissue type contrasts, enabling improved tissue characterization and identification of changes in tissue types, particularly in brain tissue, to predict hemorrhage risks and optimize therapeutic approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CT imaging is used to represent all regions of the human body, then comprehensive anatomical coverage is achieved, but tissue characterization precision deteriorates due to insufficient differentiation of tissue types with low density differences
Solution Approach 1:
The patent segments the imaging process into multiple energy spectrum acquisitions, separating the detection of different tissue types based on their energy-dependent attenuation characteristics. By acquiring CT data at multiple energy levels and processing them independently, the system enhances tissue differentiation without requiring a complete overhaul of the CT system architecture.
Solution Approach 2:
The patent changes the energy parameter of the X-ray beam by acquiring data at multiple energy spectra. This parameter change enables differentiation of tissue types based on their varying attenuation properties at different energies, improving tissue characterization precision while using standard CT hardware.
2Loss of information
If imaging time is extended to capture all necessary data for comprehensive tissue analysis, then measurement completeness is improved, but time consumption increases beyond acceptable clinical windows for stroke treatment
Solution Approach 1:
The patent performs preliminary actions by acquiring multiple energy spectrum data sets during a single rapid scan. All necessary information for comprehensive tissue characterization is collected upfront in one imaging pass, eliminating the need for multiple sequential scans and preventing information loss.
Solution Approach 2:
The patent maintains continuity of useful action by acquiring multiple energy spectra continuously during a single scan without interrupting the imaging process. This continuous multi-energy acquisition ensures complete tissue information is obtained within the clinical time window.
3Duration of action of moving object
If traditional CT imaging is used beyond 4.5 hours post-stroke, then extended treatment window is achieved, but reliability deteriorates due to increased hemorrhage risk and reduced diagnostic accuracy
Solution Approach 1:
The patent replaces conventional single-energy CT mechanics with multi-energy spectral CT. This substitution enables reliable differentiation of acute hemorrhage from ischemic penumbra even in extended time windows, maintaining diagnosis reliability beyond 4.5 hours by providing superior tissue characterization.
Solution Approach 2:
The patent introduces spectral decomposition algorithms as intermediaries between raw CT data and diagnostic interpretation. These algorithms process multi-energy data to generate tissue-specific maps that reliably distinguish between salvageable penumbra and hemorrhagic tissue, enabling safe treatment decisions in extended time windows.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances imaging capabilities to distinguish between different tissue types with low density differences, allowing for more accurate prediction of hemorrhage risks and improved therapeutic decision-making, thereby expanding the time window for effective treatment beyond traditional limits.
Implementation Method 1
a source for generation of a polychromatic beam of X-rays
Implementation Method 2
a detector for measurement of a beam intensity of the beam after passage through the object
Data Source
AI summary
A method is for computed tomography imaging. In an embodiment, the method includes provisioning a CT data set of an object, the CT data set being previously recorded via a multispectral recording method; suppressing a contrast, caused by a tissue type, and generating a contrast-suppressed data set from the CT data set provisioned; and analyzing at least the contrast-suppressed data set generated or a data set generated via a machine learning algorithm based on the contrast-suppressed data set, the analyzing being configured to identify at least one change in the tissue type. A corresponding device, a control device for a computed tomography system or a diagnosis system, and a diagnosis system and a computed tomography system are also disclosed.