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Related Concept Videos

Computed Tomography01:10

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.
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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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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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Improving CBCT quality to CT level using deep learning with generative adversarial network.

Yang Zhang1,2, Ning Yue1, Min-Ying Su2

  • 1Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA.

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Summary
This summary is machine-generated.

This study developed a deep learning generative adversarial network (GAN) to enhance cone beam CT (CBCT) image quality. The improved CBCT images show better accuracy for radiotherapy planning.

Keywords:
CBCTGANadaptive radiotherapydeep learning

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Cone beam CT (CBCT) is crucial for daily image guidance in radiotherapy.
  • Improving CBCT image quality and accuracy is essential for precise treatment delivery.
  • Deep learning offers potential solutions for enhancing medical imaging.

Purpose of the Study:

  • To enhance image quality and computed tomography (CT) number accuracy of daily CBCT using a deep learning generative adversarial network (GAN).

Main Methods:

  • A 2.5D pixel-to-pixel GAN model with feature mapping was developed and trained on 12,000 CT-CBCT slice pairs.
  • The model's robustness was verified using ten-fold cross-validation.
  • Independent testing was performed on pelvic and head-and-neck patient data from different machines, comparing various network architectures and loss functions.

Main Results:

  • The deep learning generated synthetic CT (sCT) images exhibited improved image quality, reduced artifacts, and enhanced soft tissue contrast.
  • The proposed 2.5 Pix2pix GAN with feature matching achieved the best performance, indicated by the highest PSNR and lowest MAE compared to reference CT (rCT).
  • The model demonstrated high dosimetric accuracy for photon-based planning and efficient image generation (less than a second per 3D volume).

Conclusions:

  • The developed deep learning algorithm effectively improves CBCT image quality and accuracy.
  • This approach shows significant potential for supporting online CBCT-based adaptive radiotherapy.