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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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Imaging Studies III: Computed Tomography01:27

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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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Related Experiment Video

Updated: Jul 26, 2025

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
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Deep neural network-based synthetic image digital fluoroscopy using digitally reconstructed tomography.

Shinichiro Mori1, Ryusuke Hirai2, Yukinobu Sakata2

  • 1National Institutes for Quantum Science and Technology, Quantum Life and Medical Science Directorate, Institute for Quantum Medical Science, Inage-ku, Chiba, 263-8555, Japan. mori.shinichiro@qst.go.jp.

Physical and Engineering Sciences in Medicine
|June 22, 2023
PubMed
Summary

A deep neural network (DNN) successfully generates realistic X-ray flat panel detector (FPD) images from digitally reconstructed radiographs (DRR). This advancement improves image quality and could streamline comparisons between different imaging modalities.

Keywords:
2D/3D registrationImage qualityParticle beam therapyPatient setupRadiotherapy

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Accurate image acquisition is crucial for radiation therapy planning and quality assurance.
  • Comparing images from different modalities, such as digitally reconstructed radiographs (DRRs) and flat panel detector (FPD) images, can be challenging.
  • Current methods may require significant time for image processing and comparison.

Purpose of the Study:

  • To develop and evaluate a deep neural network (DNN) for synthesizing FPD images from DRR images.
  • To assess the image quality of DNN-generated FPD images compared to ground-truth FPD and input DRR images.
  • To determine the potential of this technique for improving workflow efficiency in medical imaging.

Main Methods:

  • A DNN was developed to generate synthetic FPD images from DRR images.
  • Patient data from prostate and head and neck (H&N) malignancies were used for training and validation.
  • Image quality was quantitatively assessed using Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM).

Main Results:

  • The DNN successfully generated synthetic FPD images that closely resembled ground-truth FPD images.
  • For prostate cancer cases, synthetic FPD images showed significantly improved MAE (0.12 ± 0.02) and PSNR (16.81 ± 1.54 dB) compared to DRR images.
  • For H&N cancer cases, synthetic FPD images also demonstrated superior metrics: MAE (0.08 ± 0.03), PSNR (19.40 ± 2.83 dB), and SSIM (0.80 ± 0.04) compared to DRR images.

Conclusions:

  • The developed DNN effectively synthesizes high-quality FPD images from DRR images.
  • This technique offers a potential solution for enhancing image quality and facilitating visual comparisons between different imaging modalities.
  • The method shows promise for increasing throughput in clinical workflows where image comparison is essential.