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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: Jul 4, 2025

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Energy-guided diffusion model for CBCT-to-CT synthesis.

Linjie Fu¹, Xia Li², Xiuding Cai¹

¹Chengdu Computer Application Institute Chinese Academy of Sciences, China; University of the Chinese Academy of Sciences, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|February 6, 2024

Summary

This summary is machine-generated.

EGDiff, a novel diffusion model framework, enhances Cone Beam Computed Tomography (CBCT) image quality by reducing noise and artifacts. This improves accuracy in image-guided radiation therapy (IGRT) for better cancer treatment.

Keywords:

CBCT-to-CT synthesis Diffusion model Energy-guided function

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Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

Area of Science:

Medical Imaging
Radiation Oncology
Artificial Intelligence

Background:

Cone Beam Computed Tomography (CBCT) is vital for Image-Guided Radiation Therapy (IGRT), but suffers from noise and artifacts.
These image quality issues hinder precise dose calculation and tissue localization in radiation therapy.
Improving CBCT image quality is crucial for practical and effective IGRT applications.

Purpose of the Study:

To introduce EGDiff, a novel diffusion model-based framework for enhancing CBCT image quality.
To address scatter noise and artifacts in CBCT images for improved accuracy in radiation therapy.
To synthesize high-quality CBCT-to-CT images for more reliable IGRT.

Main Methods:

Utilized a forward diffusion process by adding Gaussian noise to CT images.
Employed a reverse denoising process with ResUNet and an attention mechanism to predict noise.
Incorporated an energy-guided function to retain domain-independent features and discard domain-specific ones.

Main Results:

EGDiff demonstrated superior performance on the thoracic tumor dataset, achieving an SSIM of 0.850, MAE of 26.87 HU, PSNR of 19.83 dB, and NCC of 0.874.
On the pancreas dataset, EGDiff outperformed state-of-the-art methods with an SSIM of 0.754, MAE of 32.19 HU, PSNR of 19.35 dB, and NCC of 0.846.
Experimental results validate the effectiveness of EGDiff in CBCT-to-CT synthesis.

Conclusions:

EGDiff significantly improves the accuracy and reliability of CBCT images by mitigating noise and artifacts.
Enhanced CBCT image quality through EGDiff can lead to more precise radiation therapy.
This advancement contributes to minimizing radiation exposure to healthy tissues and enables more personalized cancer treatment strategies.