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Generated at Fri, 22 Nov 2019 10:08:17 GMT exp-ck: undefined; xpa: undefined;
Electrode, Comp-814103570, DC-prod-az-southcentralus-18, ENV-prod-a, PROF-PROD, VER-19.1.31, SHA-771c9ce79737366b1d5f53d21cad4086bf722e21, CID-eae2ae62-7a2-16e92956f01422, Generated: Fri, 22 Nov 2019 10:08:17 GMT

Atherosclerotic Plaque Characterization Methods Based on Coronary Imaging - eBook

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<p><em>Atherosclerotic Plaque Characterization Methods Based on Coronary Imaging</em> provides a complete review of computer methods for atherosclerotic plaque reconstruction and characterization. The authors, with their expertise from biomedical engineering, computer science, and cardiology, offer a holistic view. The focus of the book is on the presentation of major imaging techniques, including their limitations. It includes details on the mechanical characterization and properties of plaques and appropriate constitutive models to describe the mechanical behavior of plaques.</p> <p>The authors explore the challenges of using multiple coronary imaging technologies, and provide the pros and cons of invasive vs. non-invasive techniques. Methods for plaque characterization and 3D reconstruction of coronary arteries using IVUS, OCT, and CT images are described.</p> <p>This book will help readers study new trends in image processing analysis and plaque characterization, implement automated plaque characterization methodologies, understand coronary imaging drawbacks, and comprehend 3 dimensional coronary artery and plaque reconstruction methods.</p> <ul> <li>Describes the multimodality imaging techniques that are commonly used in the diagnosis of arterial diseases, including intravascular ultrasound, optical coherence tomography, angiography, computed tomography angiography, and magnetic resonance angiography</li> <li>Discusses in-depth the computational methods which can be used for the detection of different plaque types</li> <li>Explores plaque in 3D reconstruction methods and plaque modeling approaches</li> </ul>

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Atherosclerotic Plaque Characterization Methods Based on Coronary Imaging provides a complete review of computer methods for atherosclerotic plaque reconstruction and characterization. The authors, with their expertise from biomedical engineering, computer science, and cardiology, offer a holistic view. The focus of the book is on the presentation of major imaging techniques, including their limitations. It includes details on the mechanical characterization and properties of plaques and appropriate constitutive models to describe the mechanical behavior of plaques.

The authors explore the challenges of using multiple coronary imaging technologies, and provide the pros and cons of invasive vs. non-invasive techniques. Methods for plaque characterization and 3D reconstruction of coronary arteries using IVUS, OCT, and CT images are described.

This book will help readers study new trends in image processing analysis and plaque characterization, implement automated plaque characterization methodologies, understand coronary imaging drawbacks, and comprehend 3 dimensional coronary artery and plaque reconstruction methods.

  • Describes the multimodality imaging techniques that are commonly used in the diagnosis of arterial diseases, including intravascular ultrasound, optical coherence tomography, angiography, computed tomography angiography, and magnetic resonance angiography
  • Discusses in-depth the computational methods which can be used for the detection of different plaque types
  • Explores plaque in 3D reconstruction methods and plaque modeling approaches
Atherosclerotic Plaque Characterization Methods Based on Coronary Imaging - eBook

Specifications

Read This On
Desktop,Ereader,Android,IOS,Windows
Is Downloadable Content Available
Y
Digital Reader Format
Epub (Yes)
Language
en
Publisher
Kobo
Author
Lampros K Michalis, Lambros S Athanasiou, Dimitrios I Fotiadis
ISBN-13
9780128051139
ISBN-10
0128051132

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Electrode, Comp-812502102, DC-prod-az-southcentralus-17, ENV-prod-a, PROF-PROD, VER-30.0.3-ebf-2, SHA-8c8e8dc1c07e462c80c1b82096c2da2858100078, CID-507b1f10-04f-16e929d7b8057d, Generated: Fri, 22 Nov 2019 10:17:04 GMT