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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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Related Experiment Video

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 Zuo, Hua-Chieh Shao, You Zhang

|September 26, 2025

Summary

This summary is machine-generated.

This study introduces DREME-adapt, a fast framework for time-resolved cone-beam CT (CBCT) reconstruction, improving accuracy and efficiency in radiotherapy image guidance by adapting to patient motion.

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Last Updated: May 1, 2026

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Published on: June 21, 2024

Area of Science:

Medical Physics
Radiotherapy Technology
Image Reconstruction

Background:

Cone-beam CT (CBCT) is crucial for radiotherapy image guidance but struggles with motion artifacts.
Respiration-induced motion necessitates time-resolved CBCT for accurate spatiotemporal anatomical capture.
Current methods face challenges in reconstruction accuracy and efficiency.

Purpose of the Study:

To develop a fast and accurate time-resolved CBCT reconstruction framework.
To integrate machine learning-based motion modeling for improved image guidance.
To enhance the clinical applicability of dynamic reconstruction and motion estimation methods.

Main Methods:

Proposed DREME-adapt: a dynamic reconstruction and motion estimation framework with adaptive initialization.
Reconstructed time-resolved CBCT sequences from fractional CBCT scans.
Generated a machine learning-based motion model for intra-treatment CBCT estimation and motion tracking.
Utilized virtual fractions for cold-start and warm-start strategies for subsequent fractions.

Main Results:

DREME-adapt demonstrated fast and accurate time-resolved CBCT reconstruction.
Evaluated three strategies (DREME-cs, DREME-adapt-vfx, DREME-adapt-pro) on phantom and patient data.
The framework successfully integrated motion modeling for improved guidance.

Conclusions:

DREME-adapt offers a significant advancement in time-resolved CBCT reconstruction.
The framework enhances accuracy and efficiency, addressing key challenges in radiotherapy.
This technology holds potential for broader clinical adoption in image-guided radiotherapy.