CT Iterative Reconstruction for Truncated Data Artifacts

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Solution Overview

Problem

Iterative reconstruction techniques in computed tomography (CT) face challenges with truncation artifacts, particularly in scenarios where the scan field of view is incomplete, leading to poor reconstruction quality and artifacts in the reconstructed images.

Innovation Solution

The Extra Truncation Regularization-Border Feathering-Iterative Reconstruction (ETR-BF-IR) method employs a combination of aggressive volume smoothing in truncated regions and border feathering to mitigate truncation artifacts, using a content penalty to encourage zero-valued voxels at the volume edges and smoothing the dexel weights near the detector borders.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If iterative reconstruction is performed with truncated scan data, then the reconstruction process can handle incomplete field of view, but truncation artifacts are introduced and image quality deteriorates

Engineering Contradiction:

Improveability to handle truncated dataVSAvoidimage quality

Core Design Contradiction:

Adaptability or versatilityVSManufacturing precision

Solution Approach 1:

The volume is divided into truncated and non-truncated parts, with different regularizers applied to each region. The truncated part uses aggressive regularizer to suppress artifacts, while the non-truncated part uses standard regularizer to preserve image quality, resolving the contradiction by localized processing

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different regularization strength is applied locally to different regions of the volume. Aggressive regularizer is applied specifically to truncated regions to suppress artifacts, while standard regularizer is applied to non-truncated regions to maintain image fidelity, allowing simultaneous optimization for both adaptability and quality

Inventive Principle:

Principle #3Local quality

2Object-affected harmful factors

If aggressive regularizer is applied to truncated regions, then truncation artifacts are suppressed, but computational complexity increases

Engineering Contradiction:

Improvetruncation artifactsVSAvoidcomputational complexity

Core Design Contradiction:

Object-affected harmful factorsVSDevice complexity

Solution Approach 1:

The computational domain is segmented into truncated and non-truncated regions, with aggressive regularizer applied only to the truncated part. This localized approach suppresses artifacts while limiting computational complexity to only the affected regions rather than the entire volume

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Computational resources are allocated non-uniformly, with higher computational intensity (aggressive regularizer) applied locally to truncated regions where artifacts occur, while standard regularizer is used in non-truncated regions, optimizing the balance between artifact suppression and computational complexity

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS12579718B1Handling truncated data in iterative reconstruction

Publication Date: 2026.03.17 VAREX IMAGING CORP

AI summary

Technology is described for handling truncated data in iterative reconstruction. A method comprises iterating on a volume of an object including a non-truncated part based on image data and at least one truncated part representing deficiently imaged data. The volume is represented by voxels. The iterating includes regularizing the non-truncated part of the volume using a first regularizer, and regularizing the truncated part of the volume using a second regularizer different from the first regularizer.