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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

13
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...
13
Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

11
Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
11
Imaging Studies V: Intravenous Urography and Retrograde Pyelography01:22

Imaging Studies V: Intravenous Urography and Retrograde Pyelography

41
IntroductionIntravenous Urography (IVU) and Retrograde Pyelography (RP) are important diagnostic imaging techniques used to evaluate the urinary system. These methods help identify structural abnormalities, obstructions, and functional issues in the kidneys, ureters, and bladder. Both procedures use iodine-based contrast media to enhance the visibility of urinary tract structures on X-ray images, though they differ in their methods and indications.1. Intravenous Urography (IVU)Intravenous...
41
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

12
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
12

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Related Experiment Video

Updated: Jul 15, 2025

A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods
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Dose Optimization Using a Deep Learning Tool in Various CT Protocols for Urolithiasis: A Physical Human Phantom

Jae Hun Shim1, Se Young Choi1, In Ho Chang1

  • 1Department of Urology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea.

Medicina (Kaunas, Lithuania)
|September 28, 2023
PubMed
Summary

Deep learning reduces radiation dose for CT scans by one-third while maintaining image quality. This AI tool improves diagnostic accuracy, especially at lower radiation settings, offering a promising approach for medical imaging.

Keywords:
deep learningphantoms, imagingradiation dosagetomography, X-ray computedurolithiasis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Radiation Dose Optimization

Background:

  • Determining optimal radiation doses for CT scans is crucial for maintaining image quality.
  • Deep learning (DL) applications show potential in enhancing medical images.
  • Evaluating DL's impact on image quality and radiation dose is essential.

Purpose of the Study:

  • To assess the effectiveness of a deep learning application in optimizing radiation dose for CT imaging.
  • To maintain diagnostic image quality using reduced radiation exposure.
  • To compare deep learning-enhanced images with traditional reconstruction methods.

Main Methods:

  • Uric acid stones were placed in a physical human phantom.
  • CT scans were performed using varying tube voltages (120, 100, 80 kV) and current-time products (100, 70, 30, 15 mAs).
  • Images were reconstructed using filtered back projection (FBP), iterative reconstruction (IR, iDose), and knowledge-based iterative model reconstruction (IMR), with and without deep learning application.
  • Objective (Hounsfield unit standard deviation) and subjective assessments by radiologists and urologists were used to evaluate image quality and diagnostic accuracy.

Main Results:

  • Deep learning application reduced objective image noise across all reconstruction methods.
  • Deep learning-applied FBP achieved similar noise levels to IR at approximately one-third the radiation dose (100 kV-30 mAs).
  • Subjective image quality scores deteriorated at radiation doses below 100 kV-30 mAs.
  • Diagnostic accuracy improved with deep learning at settings below 100 kV-30 mAs, except for 80 kV-15 mAs.

Conclusions:

  • Deep learning-applied FBP demonstrates comparable image quality to IR at 100 kV-30 mAs or higher settings.
  • A radiation dose reduction of approximately one-third is achievable at 100 kV-30 mAs using deep learning while preserving objective noise levels.
  • Deep learning holds promise for reducing radiation exposure in CT imaging without compromising diagnostic performance.