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Related Concept Videos

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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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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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Diffusion-based image translation model from low-dose chest CT to calcium scoring CT with random point sampling.

Ji-Hoon Jung1, Jong Eun Lee2, Hyun Seo Lee3

  • 1Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea; Department of Biomedical Engineering, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.

Computers in Biology and Medicine
|June 8, 2025
PubMed
Summary

This study introduces an AI method to convert low-dose CT (LDCT) scans into high-quality calcium scoring CT (CSCT) images. This advancement improves cardiovascular risk assessment by enhancing coronary artery calcium scoring accuracy from LDCT scans.

Keywords:
Calcium scoring computed tomographyCoronary artery calcium scoreDeep learningDenoising diffusion implicit modelDomain adaptationLow-dose chest computed tomography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Health

Background:

  • Coronary artery calcium (CAC) scoring is crucial for cardiovascular risk assessment.
  • Automating CAC scoring with AI in calcium scoring CT (CSCT) is established.
  • Applying AI to low-dose CT (LDCT) for CAC scoring is challenging due to LDCT's lower image quality and higher noise.

Purpose of the Study:

  • To develop a diffusion model for converting LDCT to CSCT images.
  • To enhance the accuracy of CAC scoring from LDCT scans.

Main Methods:

  • A conditional diffusion model based on denoising diffusion implicit model (DDIM) was developed.
  • Novel sampling techniques, 'random pointing' and 'intermediate sampling,' were introduced to improve domain adaptation and structural preservation.
  • The model was trained on paired LDCT and CSCT images and validated on 37 test cases.

Main Results:

  • The diffusion model outperformed existing image-to-image models (CycleGAN, CUT, DCLGAN, NEGCUT) in key metrics like PSNR, LNCC, SSIM, and Dice coefficient.
  • Image quality was maintained with a significant reduction in sampling iterations from 1000 to 10.
  • Enhanced CSCT images showed a stronger correlation with expert annotations for calcium volumes compared to original LDCT images.

Conclusions:

  • The proposed method effectively transforms LDCT to CSCT images, preserving anatomical structures and calcium deposits.
  • Reduced sampling time and improved calcium structure preservation indicate potential clinical applicability for cardiovascular risk assessment.