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Related Concept Videos

Imaging Studies for Cardiovascular System IV: CMRI01:21

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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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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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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
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Related Experiment Video

Updated: Dec 15, 2025

In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography
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Artificial Intelligence and Myocardial Contrast Enhancement Pattern.

Fang Tang1, Chen Bai1, Xin-Xiang Zhao2

  • 1Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, The 278th Baoguang Avenue, Xindu District, Chengdu, Sichuan, 610500, People's Republic of China.

Current Cardiology Reports
|July 8, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning and deep learning show promise in cardiac MRI analysis. AI algorithms can differentiate myocardial tissue and quantify fibrosis, advancing cardiac imaging techniques.

Keywords:
AICMRICardiomyocyteContrast enhancementFibrosisNecrosis

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Cardiovascular Magnetic Resonance Imaging
  • Machine Learning and Deep Learning

Background:

  • Artificial intelligence (AI) is increasingly utilized in cardiac magnetic resonance imaging (CMRI).
  • AI applications include myocardial contrast enhancement (MCE) pattern analysis and automated ventricular segmentation.
  • This review focuses on the interplay between machine learning (ML) and deep learning (DL) within AI for CMRI.

Purpose of the Study:

  • To discuss the relationship between ML and DL in AI.
  • To explore their application in MCE patterns within CMRI.
  • To highlight advancements in analyzing myocardial tissue and fibrosis.

Main Methods:

  • Review of ML algorithms, including histogram and Gray-Level Co-occurrence Matrix (GLCM) parameters.
  • Analysis of DL algorithms, specifically Convolutional Neural Networks (CNNs).
  • Comparison of AI-driven measurements with human observer performance.

Main Results:

  • ML algorithms demonstrated significant statistical differences in diagnosing cardiomyopathy and differentiating myocardial tissues using histogram and GLCM parameters.
  • CNNs showed no significant difference compared to observers in measuring myocardial fibrosis.
  • Texture parameter analysis methods are driving a new era in AI-based medical imaging.

Conclusions:

  • Histogram and GLCM parameters are key areas for unsupervised learning in MCE image analysis.
  • CNNs offer significant advantages for automated identification and quantification of myocardial fibrosis in late gadolinium enhancement (LGE) images.
  • AI, particularly ML and DL, is revolutionizing CMRI analysis and diagnosis.