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Computed Tomography

Computed Tomography

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: May 1, 2026

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

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

Published on: November 23, 2019

Prior-adapted progressive time-resolved CBCT reconstruction using a dynamic reconstruction and motion estimation

Ruizhi Zuo¹, Hua-Chieh Shao¹, You Zhang¹

¹The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Medical Physics

|November 11, 2025

Summary

This summary is machine-generated.

The DREME-adapt-pro framework enables fast and accurate time-resolved cone-beam CT (CBCT) reconstruction for radiotherapy. This method significantly improves motion tracking accuracy and reduces reconstruction time, enhancing clinical adoption.

Keywords:

fine‐tuning image reconstruction motion estimation motion model time‐resolved dynamic CBCT

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Last Updated: May 1, 2026

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

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Published on: June 21, 2024

Area of Science:

Medical Imaging
Radiotherapy Physics
Computational Imaging

Background:

Cone-beam CT (CBCT) is crucial for image guidance in radiotherapy, but respiration-induced motion complicates accurate anatomical capture.
Time-resolved CBCT is desired for tracking spatiotemporal anatomical variations but faces accuracy and efficiency challenges.

Purpose of the Study:

To develop a fast time-resolved CBCT reconstruction framework (DREME-adapt) for improved accuracy and efficiency.
To enable dynamic reconstruction and motion estimation, initialized and conditioned adaptively on prior reconstructions.

Main Methods:

DREME-adapt reconstructs time-resolved CBCT sequences from fractional CBCT scans, creating a machine learning-based motion model.
It utilizes a 'cold-start' virtual fraction for initial reconstruction and 'warm-start' for subsequent fractions, optimizing reconstruction speed.
Three strategies (DREME-cs, DREME-adapt-vfx, DREME-adapt-pro) were evaluated in phantom and patient studies.

Main Results:

DREME-adapt-pro demonstrated superior performance with lower image reconstruction error (0.14 ± 0.01) and tumor center-of-mass tracking error (0.92 ± 0.62 mm) in simulations.
In patient studies, DREME-adapt-pro localized moving lung landmarks with a mean error of 2.21 ± 1.79 mm.
Training time for DREME-adapt-pro was reduced to 11 minutes, 15% of the original DREME algorithm.

Conclusions:

DREME-adapt-pro achieves high efficiency and accuracy in on-board time-resolved CBCT reconstruction.
The framework significantly enhances the clinical applicability of the DREME method for radiotherapy image guidance.