Automated analysis of four-dimensional magnetic resonance images of the human aorta

  • Ryan K. Johnson
  • Senthil Premraj
  • Sonali S. Patel
  • Nicholas Walker
  • Andreas Wahle
  • Milan Sonka
  • Thomas D. Scholz
Original Paper

Abstract

The purpose of the study was to demonstrate the accuracy and clinical utility of an automated method of image analysis of 4D (3D + time) magnetic resonance (MR) imaging of the human aorta. Serial MR images of the entire thoracic aorta were acquired on 32 healthy individuals. Graph theory based segmentation was applied to the images and cross sectional area (CSA) was determined for the entire length of thoracic aorta. Mean CSA was compared between the 3 years. CSA values at the level of sinuses of Valsalva and sino-tubular junction were used to calculate average diameters for comparison to Roman-Devereux norms. A robust automated segmentation method was developed that accurately reproduced CSA measurements for the entire length of thoracic aorta in serially acquired scans with a 1% error compared to expert tracing. Calculated aortic root diameters based on CSA correlated with Roman-Devereux norms. Mean CSA for the aortic root agreed well with previously published manually derived values. Automated analysis of 4D MR images of the thoracic aorta provides accurate and reproducible results for CSA in healthy human subjects. The ability to simultaneously analyze the entire length of thoracic aorta throughout the cardiac cycle opens the door to the calculation of novel indices of aortic biophysical properties. These novel indices may lead to earlier detection of patients at risk for adverse events.

Keywords

Magnetic resonance imaging Aorta Connective tissue disease Cross sectional area Eccentricity Automated image analysis 

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Copyright information

© Springer Science+Business Media, B.V. 2010

Authors and Affiliations

  • Ryan K. Johnson
    • 1
  • Senthil Premraj
    • 2
  • Sonali S. Patel
    • 1
  • Nicholas Walker
    • 1
  • Andreas Wahle
    • 2
  • Milan Sonka
    • 2
  • Thomas D. Scholz
    • 1
    • 3
  1. 1.Department of Pediatrics, Carver College of MedicineUniversity of IowaIowa CityUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of IowaIowa CityUSA
  3. 3.Department of Pediatrics, Division of Pediatric CardiologyUniversity of Iowa Children’s HospitalIowa CityUSA

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