CT Image Reconstruction Using Morphological Guidance
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Solution Overview
Problem
Current CT imaging methods suffer from poor statistical quality in individual data records, leading to noise artifacts and loss of morphological information due to strong smoothing and high computational requirements, especially when reconstructing spectral or material images.
Innovation Solution
A method that combines multiple independent projection data records to create a noise-reduced combined data record, incorporating morphological information to enhance structural integrity and statistical quality, using a locally adaptive filter for improved image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple independent data records are used together during reconstruction to improve statistical quality, then noise is reduced, but spatial resolution is lost due to strong smoothing
Solution Approach 1:
The patent applies preliminary action by determining morphological information from a combined data record before reconstructing individual spectral or material images. This pre-acquired structural information is then used to guide the reconstruction process, allowing noise reduction through combining data records while preserving spatial resolution through edge-aware filtering that prevents excessive smoothing at structural boundaries.
2Measurement precision
If iterative reconstruction methods with system matrix are used to reconstruct spectral images from combined data records, then statistical quality improves, but computing outlay increases very high
Solution Approach 1:
The patent performs preliminary action by determining morphological information from a combined data record before the actual spectral image reconstruction. This pre-processing step enables subsequent reconstruction methods to work with pre-extracted structural information rather than requiring full iterative reconstruction with system matrices, significantly reducing computing outlay while maintaining statistical quality improvements.
Solution Approach 2:
The patent extracts morphological information from the combined data record as a separate intermediate result. This extraction separates the structural information determination from the spectral image reconstruction process, allowing the latter to proceed more efficiently using the pre-determined morphological data rather than requiring computationally intensive iterative methods.
3Manufacturing precision
If individual data records are used for reconstruction, then spatial resolution is maintained, but noise artifacts increase significantly
Solution Approach 1:
The patent merges multiple independent data records to create a combined data record with improved statistical quality before extracting morphological information. This combining reduces noise artifacts in the source data, and when combined with edge-aware filtering during individual image reconstruction, preserves spatial resolution while significantly reducing noise artifacts compared to using individual data records alone.
4Measurement precision
If strong smoothing is applied during reconstruction to reduce noise, then statistical quality improves, but morphological information is lost
Solution Approach 1:
The patent determines morphological information as a preliminary step before individual spectral or material image reconstruction. This pre-extracted morphological information is then used to guide the reconstruction process with edge-aware filtering, allowing statistical quality improvement through noise reduction while preserving morphological information that would otherwise be lost to smoothing.
Solution Approach 2:
The patent implements feedback by using the determined morphological information to control the filtering process during reconstruction. The morphological data provides feedback about where structural boundaries exist, allowing the reconstruction algorithm to adjust filtering strength locally - applying stronger smoothing in homogeneous regions while preserving edges and fine structures, thus maintaining both statistical quality and morphological information.
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
The method achieves improved structural integrity and noise reduction in reconstructed images by leveraging morphological information, preserving detailed structures and reducing artifacts, while being computationally efficient.
Implementation Method 1
The imaging methods are frequently based on the capture of x-ray radiation, wherein what is known as projection measured data is generated
Implementation Method 2
With quanta-counting or photon-counting x-ray detectors, the detection signal for x-ray radiation is analyzed in respect of intensity and spectral distribution of the x-ray radiation in the form of count rates
Data Source
AI summary
A method for reconstructing image data during CT imaging is described. In the method, a plurality of independent data records of projection measured data are captured. A combined data record is then determined based on the captured data records. In addition, morphological information is determined based on the combined data record. A target data record is also determined based on the captured independent data records. A target image data record is reconstructed based on the target data record and the determined morphological information. An image data determination facility and a computed tomography system are also described.