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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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...
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...

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Related Experiment Video

Updated: Jun 12, 2026

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
05:32

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Deep Learning-Based Metal Artifact Reduction in Cardiac Computed Tomography: A Preliminary Study Enabling Radiomic

Nicholas Benigni1, Francesca Lo Iacono2, Lisa M Verheul3

  • 1CardioTechLab, Centro Cardiologico Monzino IRCCS, Milan, Italy.

Journal of Imaging Informatics in Medicine
|June 10, 2026
PubMed
Summary

A novel deep learning method effectively reduces metal artifacts in cardiac CT scans for patients with implantable cardioverter-defibrillators (ICDs), preserving diagnostic quality for radiomic analysis and clinical outcome prediction.

Keywords:
Artifact reductionCardiac computed tomographyDeep learningDefibrillatorIdiopathic ventricular fibrillation

Related Experiment Videos

Last Updated: Jun 12, 2026

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
05:32

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiology

Background:

  • Idiopathic ventricular fibrillation (IVF) survivors often require implantable cardioverter-defibrillators (ICDs).
  • Metal artifacts from ICDs in cardiac computed tomography (CCT) compromise image quality and hinder advanced analyses like radiomics.
  • This limits diagnostic capabilities for managing IVF patients with ICDs.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) based solution for reducing metal-induced artifacts in CCT scans of IVF patients with ICDs.
  • To assess the effectiveness of artifact reduction in preserving diagnostic integrity for radiomic analysis.
  • To predict clinical outcomes using radiomic features extracted after artifact removal.

Main Methods:

  • A 20-layer fully convolutional neural network was trained on a simulated dataset using artifact-free control images and real artifact masks.
  • The DL model was evaluated using metrics like LV-SSIM, LV-MAE, and LV-MSE on the left ventricle wall region of interest (ROI).
  • Radiomic features were extracted from the ROI, and machine learning models were used to predict a composite clinical endpoint.

Main Results:

  • The DL model demonstrated strong performance in artifact reduction (LV-SSIM: 0.936 ± 0.045).
  • 98% of stable radiomic features were preserved post-artifact removal, indicating maintained diagnostic integrity.
  • The predictive model achieved a high F1 score of 0.85 for the composite clinical endpoint.

Conclusions:

  • A DL-based approach effectively reduces metal artifacts in CCT scans of IVF patients with ICDs.
  • The method preserves diagnostic integrity, enabling advanced radiomic analyses.
  • This technique holds promise for expanding the utility of CCT in patients with cardiac devices.