Attenuation Correction Map Generation Using MR Segmentation
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Solution Overview
Problem
Current nuclear imaging systems face challenges in generating high-quality attenuation correction maps, particularly when using MR images, as they lack radiation attenuation information compared to CT images, which affects the accuracy of attenuation correction in PET imaging.
Innovation Solution
An apparatus and method that employs watershed segmentation, feature determination, and attenuation value assignment based on image values to generate an improved attenuation correction map, even without radiation attenuation information, by utilizing MR images and potentially combining them with other imaging modalities like PET images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MR images are used for generating attenuation correction maps, then the spatial resolution and tissue type differentiation are improved, but the radiation attenuation information is lost compared to CT images
Solution Approach 1:
The patent introduces MR images as an intermediary to provide spatial and tissue type information, while using segmentation and feature analysis to infer attenuation properties. The MR image serves as a mediator that bridges the gap between available imaging data and required attenuation correction information.
Solution Approach 2:
The patent transforms MR image parameters (intensity values, tissue contrast) into attenuation correction map parameters by establishing relationships between MR signal characteristics and radiation attenuation properties. This involves changing the parameter space from MR-specific metrics to attenuation-relevant metrics.
2Loss of information
If CT images are used for attenuation correction, then the radiation attenuation information is obtained, but the differentiation of different tissue types is reduced compared to MR images
Solution Approach 1:
The patent applies segmentation to divide the image into distinct tissue regions, enabling separate analysis and assignment of attenuation values for different tissue types. This allows the system to leverage tissue type information even when starting from attenuation-based imaging.
Solution Approach 2:
The patent assigns different attenuation values to different segmented regions based on their identified tissue types. Each region receives locally optimized attenuation parameters appropriate to its specific tissue characteristics, rather than applying a uniform attenuation model.
3Measurement precision
If MR images without radiation attenuation information are used, then the tissue type differentiation is improved, but the quality of attenuation correction map is reduced
Solution Approach 1:
The patent performs preliminary segmentation and tissue type identification from the MR image before generating the attenuation correction map. This preliminary classification enables subsequent accurate assignment of attenuation values, improving the final map quality despite the lack of direct attenuation information in the source image.
Solution Approach 2:
The patent incorporates feedback mechanisms where the generated attenuation correction map can be evaluated and refined. The system uses the relationship between MR tissue characteristics and expected attenuation properties to validate and adjust the correction map, ensuring improved quality.
Data Source
AI summary
The invention relates to an apparatus for generating an attenuation correction map. An image providing unit (5, 6) provides an image of an object comprising different element classes and a segmentation unit (11) applies a segmentation to the image for generating a segmented image comprising image regions corresponding to the element classes. The segmentation is based on at least one of a watershed segmentation and a body contour segmentation based on a contiguous skin and fat layers in the image. A feature determination unit (12) determines features of at least one of a) the image regions and b) boundaries between the image regions depending on image values of the image and an assigning unit (13) assigns attenuation values to the image regions based on the determined features for generating the attenuation correction map. The performed image processing steps can allow for producing a high quality attenuation correction map, even if the initial image does not comprise image values related to the attenuation of radiation like a CT image.