CT Density Filtering with Local Average Preservation
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Solution Overview
Problem
Computed tomography (CT) imaging systems face challenges in accurately measuring projection data due to noise corruption, leading to shading artifacts in images, especially when the patient is dense or large, and existing filtering methods either introduce negative values or blur high signal-to-noise regions.
Innovation Solution
A filtering method that preserves local averages by distributing error to adjacent pixels, ensuring positive output values and controlled smoothing based on measurement density, using a non-linear function to maintain high signal-to-noise ratios and correct shading artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a smoothing filter is applied to average the measurement with neighboring values, then the noise is reduced, but the local mean value is not preserved and shading artifacts remain
Solution Approach 1:
The patent applies a non-linear transformation function to the measurement data before filtering. This parameter change ensures that the filtered values remain positive and the local mean value is preserved, while still achieving noise reduction through the smoothing operation on the transformed data.
2Productivity
If the measurement is used directly when the patient is dense or large, then the processing is fast, but the measurement may be negative or have large percentage error
Solution Approach 1:
The patent applies a preliminary non-linear transformation to the measurement data before it is used in reconstruction. This preliminary action ensures that the data is transformed into a form that avoids negative values and large percentage errors, making the subsequent reconstruction process more reliable without sacrificing processing speed.
3Measurement precision
If multiple measurements are averaged to obtain the expected value, then the noise is reduced, but the patient is exposed to higher radiation and more time is required
Solution Approach 1:
The patent introduces a non-linear transformation function as an intermediary between the noisy measurement and the expected value calculation. This intermediary allows the system to work with transformed data that has better statistical properties, enabling accurate expected value estimation from a single measurement rather than requiring multiple measurements and their associated radiation exposure.
4Measurement precision
If a smoothing filter is applied to reduce noise, then the measurement is improved, but high signal-to-noise regions are blurred
Solution Approach 1:
The patent applies the smoothing filter to transformed measurement data rather than the original data. This local quality approach ensures that regions with high signal-to-noise ratio are not excessively blurred, while still achieving noise reduction in regions where it is needed, thereby preserving overall image resolution while improving measurement precision.
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 effectively corrects shading artifacts along projection lines of strong attenuation, maintains local averages, and ensures positive pixel values, improving image reconstruction quality by addressing noise-related issues without significant blurring.
Implementation Method 1
The beam, after being attenuated by the patient, impinges upon an array of radiation detectors. The intensity of the attenuated beam radiation received at the detector array is typically dependent upon the attenuation of the X-ray beam by the patient.
Implementation Method 2
X-ray detectors typically include a collimator for collimating X-ray beams received at the detectors, a scintillator for converting X-rays to light energy adjacent the collimator, and photodiodes for receiving the light energy from the adjacent scintillator.
Implementation Method 3
The photodiodes convert the light energy into electrical energy that represents projection data.
Data Source
AI summary
A method for filtering a measurement of density of an object is described. The method includes filtering the measurement of the density and preserving a local average of the measurement and additional measurements when performing the filtering.