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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

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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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Quantification of Atherosclerotic Plaque Activity and Vascular Inflammation using [18-F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography FDG-PET/CT
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Training and assessing convolutional neural network performance in automatic vascular segmentation using Ga-68

R Parry1,2, K Wright3, J W Bellinge4,5

  • 1School of Medicine, The University of Western Australia, Perth, Australia. reece.parry@health.wa.gov.au.

The International Journal of Cardiovascular Imaging
|July 5, 2024
PubMed
Summary

Artificial intelligence using nnU-Net accurately assesses vascular contours, calcification, and PET tracer uptake in Ga-68 DOTATATE PET/CT scans. This AI approach significantly reduces workflow time compared to manual segmentation.

Keywords:
Artificial intelligenceAutomatic segmentationCardiovascular inflammationCoronary artery diseaseDeep learningGallium-68 DOTATATE positron emission tomographyNeural network

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Nuclear Medicine

Background:

  • Ga-68 DOTATATE PET/CT is crucial for neuroendocrine tumor imaging.
  • Accurate assessment of vascular contours, calcification, and tracer uptake is vital for diagnosis and treatment monitoring.
  • Manual segmentation is time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To evaluate the performance of a convolutional neural network (nnU-Net) for segmenting vascular contours, calcification, and PET tracer activity in Ga-68 DOTATATE PET/CT.
  • To compare AI-driven segmentation with manual segmentation by an experienced observer.
  • To assess the impact of AI segmentation on workflow efficiency.

Main Methods:

  • A nnU-Net model was trained, validated, and tested on Ga-68 DOTATATE PET/CT scans from 116 patients.
  • Manual cardiac and aortic segmentations were performed by an experienced observer.
  • Comparisons were made between manual and AI segmentation for PET tracer uptake (SUVmean) and calcium scoring.

Main Results:

  • nnU-Net demonstrated strong positive correlations (r > 0.98) with manual segmentations for vascular contours.
  • No significant differences were observed in SUVmean values between manual and AI segmentation across various aortic segments.
  • Excellent agreement (r ≥ 0.80) was found between manual and AI measures for PET tracer uptake and vascular calcium scores.
  • AI segmentation significantly reduced workflow time compared to manual segmentation.

Conclusions:

  • nnU-Net provides accurate and reliable segmentation of vascular contours, calcification, and PET tracer uptake in Ga-68 DOTATATE PET/CT.
  • AI-driven segmentation offers comparable results to experienced observers while significantly improving workflow efficiency.
  • nnU-Net holds promise for enhancing the clinical utility of Ga-68 DOTATATE PET/CT in neuroendocrine tumor assessment.