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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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Calcium Scoring at Coronary CT Angiography Using Deep Learning.

Dan Mu1, Junjie Bai1, Wenping Chen1

  • 1From the Department of Radiology, Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China (D.M., W.C., H.Y., J.L., K.Y., H.L., Z.Q., B.Z.); Keya Medical, Shenzhen, China (J.B., H.Y.Y., J.Z., Y.Y.); Medical School of Nanjing University, Nanjing, China (K.H.); National Institutes of Healthcare Data Science at Nanjing University, Nanjing, China (K.H.); University of South Carolina School of Medicine-Columbia, Columbia, SC (H.W.M.); Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC (U.J.S.); Institute of Brain Science, Nanjing University, Nanjing 210008, China (B.Z.).

Radiology
|November 23, 2021
PubMed
Summary
This summary is machine-generated.

A new deep learning method accurately quantifies coronary artery calcium (CAC) from CT angiography scans, eliminating the need for separate radiation-exposing scans and improving cardiovascular risk assessment.

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Area of Science:

  • Radiology
  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine

Background:

  • Separate noncontrast CT scans are often used to quantify coronary artery calcium (CAC) scores before coronary CT angiography (CTA).
  • This dual-scan approach increases radiation exposure for patients.
  • Quantifying CAC directly from CTA scans would reduce radiation but presents technical challenges.

Purpose of the Study:

  • To develop and validate a deep learning method for automatic CAC scoring directly from a single CTA scan.
  • To eliminate the need for a separate noncontrast CT scan for CAC quantification.

Main Methods:

  • A deep learning algorithm was trained and validated on 292 and 73 patient scans, respectively.
  • Virtual noncontrast images generated from spectral CT data were used for algorithm development.
  • The method was validated on an independent test set of 240 CTA scans using comparison with semiautomatic Agatston scores from noncontrast CT.

Main Results:

  • The deep learning CTA CAC scores showed excellent correlation with semiautomatic noncontrast CT CAC scores (Pearson correlation = 0.96, r² = 0.92).
  • Cardiovascular risk categorization agreement was excellent (weighted κ = 0.94), with 93% of scans correctly categorized.
  • No statistically significant differences were observed based on scanner, sex, or section thickness.

Conclusions:

  • A deep learning-based automatic CAC scoring method accurately quantifies coronary artery calcium from CTA images.
  • This method effectively categorizes cardiovascular risk, comparable to traditional noncontrast CT methods.
  • The approach offers a radiation-sparing alternative for CAC assessment.