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Related Concept Videos

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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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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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CBCT-Based synthetic CT image generation using conditional denoising diffusion probabilistic model.

Junbo Peng1,2, Richard L J Qiu1, Jacob F Wynne1

  • 1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.

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|August 30, 2023
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Summary
This summary is machine-generated.

This study introduces a new AI model that converts low-quality cone-beam CT (CBCT) scans into high-quality synthetic CT (sCT) images. This advancement improves image quality for adaptive radiotherapy (ART) planning and dose calculations.

Keywords:
CBCTdiffusion modelsynthetic CT

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiotherapy

Background:

  • Cone-beam computed tomography (CBCT) is vital for image-guided radiotherapy (IGRT) and adaptive radiotherapy (ART) due to its frequent use for patient positioning.
  • CBCT scans suffer from severe artifacts and inaccurate Hounsfield unit (HU) values, limiting their use in quantitative applications like organ segmentation and dose calculation.
  • Improving CBCT quality to CT scan standards is essential for enabling online ART clinical practices.

Purpose of the Study:

  • To develop a conditional diffusion model for image translation, enhancing CBCT image quality by converting it to the CT distribution.
  • To enable quantitative applications of CBCT data through improved image fidelity.

Main Methods:

  • A conditional denoising diffusion probabilistic model (DDPM) with a time-embedded U-net architecture, incorporating residual and attention blocks, was employed.
  • The model was trained on paired deformed planning CT (dpCT) and CBCT images from brain and head-and-neck (H&N) patient studies.
  • Performance was assessed using Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Normalized Cross-Correlation (NCC), comparing the generated synthetic CT (sCT) against CBCT and other generative models.

Main Results:

  • The proposed method significantly reduced artifacts and improved HU accuracy in generated sCT images compared to original CBCT scans across both brain and H&N patient studies.
  • Quantitative metrics showed superior performance: in brain studies, MAE improved from 40.63 HU to 25.99 HU, PSNR increased from 27.87 dB to 30.49 dB.
  • The method outperformed four other diffusion models and a Cycle Generative Adversarial Network (Cycle GAN) in both visual and quantitative evaluations.

Conclusions:

  • The conditional DDPM effectively generates synthetic CT (sCT) from CBCT, yielding accurate Hounsfield units and reduced artifacts.
  • This image quality improvement facilitates precise CBCT-based organ segmentation and dose calculations.
  • The developed method is crucial for advancing online adaptive radiotherapy (ART) by enabling reliable quantitative analysis directly from CBCT data.