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Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Updated: Jun 10, 2026

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Lung motion estimation from 4D CT using structure-tensor-guided finite-element digital volume correlation.

Haizhou Liu^1,2, Zhou Liu², Yuxi Jin¹

¹Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.

Physics in Medicine and Biology

|June 8, 2026

Summary

This summary is machine-generated.

This study introduces an anatomy-informed finite-element digital volume correlation (FE-DVC) method for precise lung motion estimation. The novel framework improves accuracy in vessel-rich areas, crucial for image-guided radiotherapy and lung motion analysis.

Keywords:

anisotropic regularization biomechanical modeling digital volume correlation lung motion estimation structure tensor

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Area of Science:

Medical Imaging
Computational Mechanics
Radiotherapy Technology

Background:

Accurate lung motion estimation from 4D CT is vital for image-guided radiotherapy.
Challenges exist in vessel-rich regions due to weak contrast and complex mechanics.
Conventional registration methods struggle with fine structures and heterogeneous lung motion.

Purpose of the Study:

To develop an anatomy-informed finite-element digital volume correlation (FE-DVC) framework.
To achieve accurate and mechanically plausible lung motion estimation.
To improve registration in challenging vessel-rich lung regions.

Main Methods:

Proposed a structure-tensor-guided heterogeneous anisotropic FE-DVC method.
Utilized anatomical priors from structure tensors to guide regularization.
Employed a lung-specific multi-mesh strategy and L-curve analysis for parameter tuning.

Main Results:

Consistently reduced landmark target registration error across all datasets.
Achieved significantly lower normalized correlation residuals compared to Demons and pTV.
Recovered coherent vessel-oriented strain fields, validating the anisotropic regularization.

Conclusions:

The FE-DVC framework integrates anatomical information with mechanical principles.
Improved vessel-scale lung motion recovery is demonstrated.
Supports development of strain-based biomarkers and pulmonary structure-function models.