Iterative CT Image Reconstruction Using Modified Statistical Weights

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Solution Overview

Problem

Conventional statistical weighting techniques in computed tomography (CT) imaging lead to slow convergence and image artifacts due to large dynamic range in weights, especially when objects move or have varying attenuation values, affecting image texture and quality.

Innovation Solution

A method to calculate modified statistical weights that deviate from inverse variance, using transformation functions to adjust the dynamic range and reduce artifacts, improving image reconstruction by tuning scalar parameters and applying nonlinear transformations to projection data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional statistical weighting techniques are used in iterative reconstruction, then image noise is reduced, but convergence speed decreases and image artifacts increase

Engineering Contradiction:

Improveimage noise reductionVSAvoidconvergence speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent modifies the statistical weighting parameters by applying nonlinear transformations (power law with exponent α, logarithmic transformation) to the conventional inverse-variance weights. This changes the weight distribution to reduce dynamic range while preserving noise reduction benefits, thereby improving convergence speed without sacrificing image quality

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces adaptive weighting schemes where the weight exponent α can be adjusted dynamically based on projection data characteristics. This allows the system to adapt the degree of weight modification to different imaging scenarios, optimizing both convergence speed and artifact reduction for each specific case

Inventive Principle:

Principle #15Dynamics

2Measurement precision

If conventional statistical weights with large dynamic range are used, then projection measurements are accurately weighted, but image artifacts increase and convergence slows

Engineering Contradiction:

Improveprojection measurement weighting accuracyVSAvoidimage artifacts

Core Design Contradiction:

Measurement precisionVSObject-generated harmful factors

Solution Approach 1:

The patent applies parameter transformation to the statistical weights using power law (w' = w^α) and logarithmic transformations. This compresses the dynamic range of weights, reducing the disparity between high and low weight values that cause artifacts, while maintaining the relative weighting information needed for accurate reconstruction

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

Instead of directly using the conventional inverse-variance weights that produce large dynamic range, the patent inverts the approach by applying transformations that compress the weight distribution. This reverse engineering of the weighting scheme eliminates the harmful effect of large dynamic range while preserving the beneficial statistical weighting

Inventive Principle:

Principle #13The other way round (Inversion)

3Reliability

If conventional statistical weighting is applied, then reconstruction follows standard Poisson statistics, but texture representation deteriorates

Engineering Contradiction:

Improvestatistical consistencyVSAvoidimage texture quality

Core Design Contradiction:

ReliabilityVSManufacturing precision

Solution Approach 1:

The patent modifies the weight parameters to achieve a balance between statistical consistency and texture quality. By using transformed weights with adjusted exponents, the system preserves the statistical foundation while improving the visual texture representation in the reconstructed image

Inventive Principle:

Principle #35Parameter changes

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 enhances image reconstruction by reducing artifacts, improving convergence speed, and maintaining image texture, particularly in the presence of dense objects or motion, leading to better representation of tissue and reduced metal artifacts.

Implementation Method 1

an x-ray source emits a fan-shaped beam toward a subject or object... The beam, after being attenuated by the subject, impinges upon an array of radiation detectors

Methodology Applied

Scientific EffectX-ray attenuation: Absorption (EM radiation)

Implementation Method 2

a scintillator for converting x-rays to light energy adjacent the collimator

Methodology Applied

Scientific EffectScintillation: Scintillation

Implementation Method 3

photodiodes for receiving the light energy from the adjacent scintillator and producing electrical signals therefrom

Methodology Applied

Scientific EffectPhotoelectric effect: Photoelectric Effect

Data Source

PatentUS9261467B2System and method of iterative image reconstruction for computed tomography

Publication Date: 2016.02.16 GENERAL ELECTRIC CO

AI summary

A system and method include acquisition of projection data from a scanned object, the set of projection data comprising a plurality of projection measurements. The system and method also include calculation of a set of modified statistical weights from the projection data, wherein a respective modified statistical weight of the set of modified statistical weights comprises a deviation from an inverse variance of a corresponding projection measurement of the projection data. The system and method further include reconstruction of an image of the scanned object using the set of modified statistical weights as coefficients in an iterative reconstruction algorithm.