Detection, segmentation, simulation and visualization of aortic dissections: A review

Antonio Pepe; Jianning Li; Malte Rolf-Pissarczyk; Christina Gsaxner; Xiaojun Chen; Gerhard A Holzapfel; Jan Egger

doi:10.1016/j.media.2020.101773

Detection, segmentation, simulation and visualization of aortic dissections: A review

Med Image Anal. 2020 Oct:65:101773. doi: 10.1016/j.media.2020.101773. Epub 2020 Jul 7.

Authors

Antonio Pepe¹, Jianning Li², Malte Rolf-Pissarczyk³, Christina Gsaxner⁴, Xiaojun Chen⁵, Gerhard A Holzapfel⁶, Jan Egger⁷

Affiliations

¹ Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Stanford University, School of Medicine, Department of Radiology, 291 Campus Drive, Stanford, CA 94305, USA; Computer Algorithms for Medicine Laboratory, Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria. Electronic address: antonio.pepe@tugraz.at.
² Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria. Electronic address: jianning.li@icg.tugraz.at.
³ BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria; Institute of Biomechanics, Graz University of Technology, Stremayrgasse 16-II, 8010 Graz, Austria.
⁴ Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria; Department of Oral and Maxillofacial Surgery, Medical University of Graz, Auenbruggerplatz 2, 8036 Graz, Austria.
⁵ Shanghai Jiao Tong University, School of Mechanical Engineering, 800 Dong Chuan Road, Shanghai 200240, China.
⁶ BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria; Institute of Biomechanics, Graz University of Technology, Stremayrgasse 16-II, 8010 Graz, Austria; Norwegian University of Science and Technology (NTNU), Department of Structural Engineering, 7491 Trondheim, Norway.
⁷ Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria; Department of Oral and Maxillofacial Surgery, Medical University of Graz, Auenbruggerplatz 2, 8036 Graz, Austria; Shanghai Jiao Tong University, School of Mechanical Engineering, 800 Dong Chuan Road, Shanghai 200240, China. Electronic address: jan.egger@medunigraz.at.

PMID: 32738647
DOI: 10.1016/j.media.2020.101773

Abstract

Aortic dissection (AD) is a condition of the main artery of the human body, resulting in the formation of a new flow channel, or false lumen. The disease is usually diagnosed with a computed tomography angiography scan during the acute phase. A better understanding of the causes of AD requires knowledge of the aortic geometry (segmentation), including the true and false lumina, which is very time-consuming to reconstruct when performed manually on a slice-by-slice basis. Hence, different automatic and semi-automatic medical image analysis approaches have been proposed for this task over the last years. In this review, we present and discuss these computing techniques used to segment dissected aortas, also in regard to the detection and visualization of clinically relevant information and features from dissected aortas for customized patient-specific treatments.

Keywords: Aorta; Computed tomography; Detection; Dissection; Segmentation; Simulation; Visualization.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Aorta
Aortic Dissection* / diagnostic imaging
Computed Tomography Angiography
Computer Simulation
Humans
Tomography, X-Ray Computed