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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...
Computed Tomography01:10

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 15, 2026

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
09:36

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Published on: March 14, 2018

Deep Learning Super-Resolution from Normal to Ultra-High Resolution CT: Conditional Diffusion Model Development and

Tianyi Ye1, Gengxin Shi1, Aswath Sivakumar1

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205.

Proceedings of Spie--The International Society for Optical Engineering
|July 14, 2026
PubMed
Summary

A new Conditional Denoising Diffusion Probabilistic Model (Conditional DDPM) enhances normal resolution (NR) CT images to ultra-high-resolution (UHR) quality. This super-resolution (SR) technique improves trabecular bone texture analysis for better microarchitecture assessment.

Keywords:
Bone microstructureDeep LearningDiffusion modelRadiomicsSuper resolution CTTexture

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Published on: June 21, 2011

Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Current multi-detector CT (MDCT) offers normal resolution (NR) imaging, limiting detailed analysis of bone microarchitecture.
  • Ultra-high-resolution (UHR) CT provides superior detail but is less accessible.
  • Super-resolution (SR) techniques aim to enhance image quality, but their impact on quantitative texture analysis needs evaluation.

Purpose of the Study:

  • To develop a Conditional Denoising Diffusion Probabilistic Model (Conditional DDPM) for SR of NR CT images to UHR CT levels.
  • To assess the impact of this SR method on trabecular bone texture metrics.

Main Methods:

  • A Conditional DDPM was trained using paired NR and UHR CT image patches from human cadaver femurs.
  • The model's performance was validated and tested on separate datasets.
  • Trabecular bone texture was quantified using 24 Grey Level Co-occurrence Matrix (GLCM) features and analyzed via concordance correlation coefficients (CCC) and principal component analysis (PCA).

Main Results:

  • Visually, SR images achieved resolution comparable to UHR CT.
  • SR significantly improved CCCs for 22 out of 24 GLCM texture features compared to NR vs. UHR.
  • PCA confirmed enhanced trabecular bone texture feature overlap between SR and UHR images compared to NR and UHR.

Conclusions:

  • The Conditional DDPM effectively enhances NR CT images to UHR quality, improving visualization and quantification of bone microarchitecture.
  • SR-derived trabecular bone texture features show strong agreement with UHR CT, suggesting potential for harmonizing NR data for predictive modeling.