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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: Mar 2, 2026

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

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

Published on: November 23, 2019

Semiautomated four-dimensional computed tomography segmentation using deformable models.

Dustin Ragan¹, George Starkschall¹, Todd McNutt²

¹Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas 77030.

Medical Physics

|May 12, 2017

Summary

This summary is machine-generated.

This study shows a new deformable model algorithm can automatically outline structures like lungs and the heart in four-dimensional (4D) CT scans. However, it struggled with soft tissues like the esophagus.

Keywords:

4D imaging Anatomy CT segmentation Cardiac dynamics Computed radiography Computed tomography Computer software Data sets Heart Hemodynamics Lungs Medical imaging Medical treatment planning Pneumodyamics, respiration Radiation treatment Tissues biological organs biological tissues cardiology computerised tomography deformable models image segmentation lung medical image processing pneumodynamics

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Area of Science:

Medical Imaging
Computational Anatomy
Radiology

Background:

Four-dimensional (4D) computed tomography (CT) provides dynamic anatomical information crucial for various medical applications.
Accurate delineation of anatomical structures in 4D CT datasets is essential but often time-consuming.
Deformable model algorithms offer a potential solution for automated contour generation.

Purpose of the Study:

To assess the feasibility of a commercial deformable model algorithm for delineating thoracic anatomical structures in 4D CT data.
To evaluate the accuracy and automation potential of the algorithm in reproducing manually defined contours across respiratory phases.

Main Methods:

A 4D CT dataset of a patient's thorax, comprising eight respiratory phases, was utilized.
Manual contours for lungs, heart, and esophagus were generated on the end-inspiration phase.
An interactive deformable model algorithm was applied to automatically propagate contours across the eight phases.

Main Results:

The algorithm accurately reproduced contours for high-density gradient structures like lungs and heart.
Accuracy was compromised in areas with complex gradient boundaries, such as near bronchi.
The algorithm failed to accurately contour the esophagus without manual intervention due to similar surrounding tissue densities.

Conclusions:

The deformable model algorithm shows potential for facilitating contour delineation in 4D CT image datasets.
Further software development is needed to improve accuracy, particularly for soft-tissue structures.
The technique could streamline the process of anatomical structure segmentation in dynamic imaging.