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Related Concept Videos

Computed Tomography01:10

Computed Tomography

8.0K
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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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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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Related Experiment Video

Updated: Jan 16, 2026

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation
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Fully Automated Aortic Root Localization and Tilt Alignment in Cardiac Computed Tomography.

Elham Mahmoudi1,2, Vinayak Nagaraja3, Mohamad Sarraf3

  • 1Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, Minnesota.

Journal of the Society for Cardiovascular Angiography & Interventions
|September 29, 2025
PubMed
Summary

This study introduces an automated pipeline for detecting the aortic root in cardiac computed tomography (CCT) scans, improving personalized care for transcatheter aortic valve replacement (TAVR) patients. The AI model achieved high accuracy in identifying the aortic root and predicting tilt angles.

Keywords:
cardiac imagingcomputed tomographydeep learningtranscatheter aortic valve replacement

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Medical Image Analysis

Background:

  • Automated analysis of cardiac computed tomography (CCT) aids personalized management and outcome prediction in transcatheter aortic valve replacement (TAVR).
  • Current CCT analysis methods often require manual selection of the region of interest, limiting efficiency.
  • An object-oriented aortic root detection pipeline is needed to address these limitations.

Purpose of the Study:

  • To develop and evaluate a fully automated object-oriented pipeline for aortic root detection in pre-TAVR CCT studies.
  • To assess the performance of a convolutional neural network for accurate aortic root identification.
  • To evaluate an automated method for tilt angle prediction for improved procedural planning.

Main Methods:

  • Retrospective collection of CCT data from 179 eligible TAVR patients.
  • Utilized a pretrained convolutional neural network for automated aortic root detection.
  • Employed intensity thresholding, connected component, and principal component analyses for tilt alignment.

Main Results:

  • The automated pipeline achieved high detection performance with recall, precision, and F1 scores of 99.0%.
  • Mean average precision (mAP) at 50% overlap was 99.5%, with mAP 50%-95% at 60.4%.
  • The tilt prediction algorithm demonstrated a mean error of 7.9°, comparable to interobserver variability.

Conclusions:

  • A fully automated pipeline for aortic root detection and analysis in pre-TAVR CCTs shows robust performance.
  • The developed pipeline can enhance personalized patient management and outcome prediction for TAVR.
  • Further prospective studies are warranted to integrate this technology into clinical practice.