Multi-material correction for CT image artifacts
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Solution Overview
Problem
Current non-invasive imaging technologies, such as computed tomography (CT), face challenges in producing accurate images due to artifacts like beam hardening, heel-effect, and bone-induced spectral variations, which affect the quality and accuracy of tissue characterization.
Innovation Solution
A multi-material correction (MMC) approach is implemented, utilizing a generalized modeling function to estimate the fraction of basis materials within each voxel, reducing the number of materials analyzed to two basis materials (e.g., water and iodine), and generating re-mapped image volumes for accurate projection correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-material correction methods are used, then the correction process is simple, but the accuracy of tissue characterization is insufficient
Solution Approach 1:
The patent segments the complex material composition into two basis materials (water and iodine) for characterization purposes. By dividing the multi-material problem into a two-material framework, the system achieves more accurate tissue characterization while maintaining manageable computational complexity through systematic material decomposition.
Solution Approach 2:
The patent transforms the correction approach by changing from single-material to multi-material parameter estimation. The system estimates fractions of basis materials (water and iodine) throughout the image volume, enabling accurate characterization of complex tissues like bone, soft tissue, and contrast agents through parameter transformation rather than complex computational geometry.
2Object-affected harmful factors
If beam hardening correction is applied, then image quality improves, but artifacts such as heel-effect and bone-induced spectral variations remain
Solution Approach 1:
The patent introduces basis material decomposition as an intermediary correction layer between the raw projection data and the final image. By representing materials as combinations of water and iodine basis materials, the system creates a mediator model that systematically accounts for spectral variations and reduces residual artifacts like heel-effect and bone-induced spectral variations.
Solution Approach 2:
The patent replaces traditional single-material correction algorithms with a multi-material correction system based on material decomposition. This substitution of the correction mechanism enables simultaneous handling of multiple artifact types (beam hardening, heel-effect, BIS) through a unified material fraction estimation approach rather than sequential corrections.
3Adaptability or versatility
If multiple materials are analyzed in the image volume, then comprehensive tissue characterization is achieved, but computational complexity increases
Solution Approach 1:
The patent segments the comprehensive material analysis into a systematic two-basis-material framework. Instead of analyzing all possible materials simultaneously, the system divides the problem by representing all tissues as combinations of water and iodine, achieving comprehensive characterization through a simplified, structured approach that reduces computational complexity.
Solution Approach 2:
The patent creates a universal correction framework where water and iodine serve as basis materials that can represent any tissue type. This multi-functional basis material approach allows the same computational framework to characterize bone, soft tissue, contrast agents, and other materials through linear combinations, eliminating the need for separate correction algorithms for each material type.
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
This method minimizes beam-hardening artifacts, provides more accurate CT values for bone, soft tissue, and contrast agents, improving image quality, differentiation between cysts and metastases, and accurate contrast measurement, while being independent of patient size or region-of-interest location.
Implementation Method 1
In computed tomography (CT) and other X-ray based imaging technologies, X-ray radiation spans a subject of interest, such as a human patient, and a portion of the radiation impacts a detector where the image data is collected
Implementation Method 2
performing material characterization of an image volume of the first reconstructed image to reduce a number of materials analyzed in the image volume to two basis materials. Performing material characterization of the image volume includes utilizing a generalized modeling function to estimate a fraction of at least one basis material within each voxel of the image volume
Implementation Method 3
performing forward projection on at least the re-mapped image volume for the at least one basis material to produce a material-based projection
Data Source
AI summary
A method includes acquiring projection data of an object from a plurality of detector elements, reconstructing the acquired projection data into a first reconstructed image, and performing material characterization of an image volume of the first reconstructed image to reduce a number of materials analyzed in the image volume to two basis materials. Performing material characterization includes utilizing a generalized modeling function to estimate a fraction of at least one basis material within each voxel of the image volume. The method also includes generating a re-mapped image volume for the at least one basis material of the two basis materials, performing forward projection on at least the re-mapped image volume for the at least one basis material to produce a material-based projection, and generating multi-material corrected projections based on the material-based projection and a total projection attenuated by the object, which represents both of the two basis materials.