Machine learning for coronary artery disease analysis in cardiac CT

Majd Zreik

Machine learning for coronary artery disease analysis in cardiac CT

Majd Zreik

Research output: Thesis › Doctoral thesis 1 (Research UU / Graduation UU)

3 Downloads (Pure)

Abstract

Cardiac CT angiography (CCTA) images of patients with suspected obstructive
coronary artery disease are typically used to visually characterize coronary
artery plaque and stenosis, as well as to serve as the gatekeeper for referral to invasive
coronary angiography, where the fractional flow reserve is measured to
identify functionally significant stenoses. The chapters of this thesis describe machine learning-based methods for automatic noninvasive identification of patients with functionally significant stenosis, and for automatic detection and characterization of coronary artery plaque and stenosis in CCTA images.

Original language	English
Awarding Institution	University Medical Center (UMC) Utrecht
Supervisors/Advisors	Išgum, I., Primary supervisor Viergever, Max, Supervisor Leiner, Tim, Supervisor
Award date	14 Jan 2020
Place of Publication	[Utrecht]
Publisher	Utrecht University
Print ISBNs	978-94-6323-978-3
Publication status	Published - 14 Jan 2020

Keywords

Cardiac
CT angiography
Machine learning
Deep learning
Medical image analysis
Fractional flow reserve

Access to Document

thesiszreikmajdFinal published version, 41.4 MB

Cite this

@phdthesis{21f76413c10146639442d363867d2fcd,

title = "Machine learning for coronary artery disease analysis in cardiac CT",

abstract = "Cardiac CT angiography (CCTA) images of patients with suspected obstructivecoronary artery disease are typically used to visually characterize coronaryartery plaque and stenosis, as well as to serve as the gatekeeper for referral to invasivecoronary angiography, where the fractional flow reserve is measured toidentify functionally significant stenoses. The chapters of this thesis describe machine learning-based methods for automatic noninvasive identification of patients with functionally significant stenosis, and for automatic detection and characterization of coronary artery plaque and stenosis in CCTA images.",

keywords = "Cardiac, CT angiography, Machine learning, Deep learning, Medical image analysis, Fractional flow reserve",

author = "Majd Zreik",

year = "2020",

month = jan,

day = "14",

language = "English",

isbn = "978-94-6323-978-3",

publisher = "Utrecht University",

type = "Doctoral thesis 1 (Research UU / Graduation UU)",

school = "UMC Utrecht",

}

TY - THES

T1 - Machine learning for coronary artery disease analysis in cardiac CT

AU - Zreik, Majd

PY - 2020/1/14

Y1 - 2020/1/14

N2 - Cardiac CT angiography (CCTA) images of patients with suspected obstructivecoronary artery disease are typically used to visually characterize coronaryartery plaque and stenosis, as well as to serve as the gatekeeper for referral to invasivecoronary angiography, where the fractional flow reserve is measured toidentify functionally significant stenoses. The chapters of this thesis describe machine learning-based methods for automatic noninvasive identification of patients with functionally significant stenosis, and for automatic detection and characterization of coronary artery plaque and stenosis in CCTA images.

AB - Cardiac CT angiography (CCTA) images of patients with suspected obstructivecoronary artery disease are typically used to visually characterize coronaryartery plaque and stenosis, as well as to serve as the gatekeeper for referral to invasivecoronary angiography, where the fractional flow reserve is measured toidentify functionally significant stenoses. The chapters of this thesis describe machine learning-based methods for automatic noninvasive identification of patients with functionally significant stenosis, and for automatic detection and characterization of coronary artery plaque and stenosis in CCTA images.

KW - Cardiac

KW - CT angiography

KW - Machine learning

KW - Deep learning

KW - Medical image analysis

KW - Fractional flow reserve

M3 - Doctoral thesis 1 (Research UU / Graduation UU)

SN - 978-94-6323-978-3

PB - Utrecht University

CY - [Utrecht]

ER -

Machine learning for coronary artery disease analysis in cardiac CT

Abstract

Keywords

Access to Document

Fingerprint

Cite this