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      Advanced Medical Use of Three-Dimensional Imaging in Congenital Heart Disease: Augmented Reality, Mixed Reality, Virtual Reality, and Three-Dimensional Printing

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          Abstract

          Three-dimensional (3D) imaging and image reconstruction play a prominent role in the diagnosis, treatment planning, and post-therapeutic monitoring of patients with congenital heart disease. More interactive and realistic medical experiences take advantage of advanced visualization techniques like augmented, mixed, and virtual reality. Further, 3D printing is now used in medicine. All these technologies improve the understanding of the complex morphologies of congenital heart disease. In this review article, we describe the technical advantages and disadvantages of various advanced visualization techniques and their medical applications in the field of congenital heart disease. In addition, unresolved issues and future perspectives of these evolving techniques are described.

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          Cardiac 3D Printing and its Future Directions.

          Marija Vukicevic, Bobak Mosadegh, James Min … (2017)
          Three-dimensional (3D) printing is at the crossroads of printer and materials engineering, noninvasive diagnostic imaging, computer-aided design, and structural heart intervention. Cardiovascular applications of this technology development include the use of patient-specific 3D models for medical teaching, exploration of valve and vessel function, surgical and catheter-based procedural planning, and early work in designing and refining the latest innovations in percutaneous structural devices. In this review, we discuss the methods and materials being used for 3D printing today. We discuss the basic principles of clinical image segmentation, including coregistration of multiple imaging datasets to create an anatomic model of interest. With applications in congenital heart disease, coronary artery disease, and surgical and catheter-based structural disease, 3D printing is a new tool that is challenging how we image, plan, and carry out cardiovascular interventions.
          Article 0 comments Cited 148 times – based on 0 reviews     Review now
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            A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.

            M Avendi, Arash Kheradvar, Hamid Jafarkhani (2016)
            Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic LV segmentation tool from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are used to infer the LV shape. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81 mm and 0.86, versus those of 79.2-95.62%, 0.87-0.9, 1.76-2.97 mm and 0.67-0.78, obtained by other methods, respectively.
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              Guidelines and protocols for cardiovascular magnetic resonance in children and adults with congenital heart disease: SCMR expert consensus group on congenital heart disease

              Sohrab Fratz, Taylor Chung, Gerald Greil … (2013)
              Cardiovascular magnetic resonance (CMR) has taken on an increasingly important role in the diagnostic evaluation and pre-procedural planning for patients with congenital heart disease. This article provides guidelines for the performance of CMR in children and adults with congenital heart disease. The first portion addresses preparation for the examination and safety issues, the second describes the primary techniques used in an examination, and the third provides disease-specific protocols. Variations in practice are highlighted and expert consensus recommendations are provided. Indications and appropriate use criteria for CMR examination are not specifically addressed.
              Article 0 comments Cited 137 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Korean J Radiol
                Journal ID (iso-abbrev): Korean J Radiol
                Journal ID (publisher-id): KJR
                Title: Korean Journal of Radiology
                Publisher: The Korean Society of Radiology
                ISSN (Print): 1229-6929
                ISSN (Electronic): 2005-8330
                Publication date (Print): February 2020
                Publication date (Electronic): 08 January 2020
                Volume: 21
                Issue: 2
                Pages: 133-145
                Affiliations
                [1 ]Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
                [2 ]Department of Radiology, Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea.
                [3 ]Department of Diagnostic Imaging, The Hospital for Sick Children, University of Toronto, Toronto, Canada.
                Author notes
                Corresponding author: Hyun Woo Goo, MD, PhD, Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea. Tel: (822) 3010-4388, Fax: (822) 476-0090, ghw68@ 123456hanmail.net
                Author information
                https://orcid.org/0000-0001-6861-5958
                Article
                DOI: 10.3348/kjr.2019.0625
                PMC ID: 6992436
                PubMed ID: 31997589
                SO-VID: 212c14f6-4bb6-41ea-9546-61737324356a
                Copyright © Copyright © 2020 The Korean Society of Radiology
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 20 August 2019
                Date accepted : 24 October 2019
                Categories
                Subject: Pediatric Imaging
                Subject: Review Article

                ScienceOpen disciplines: Radiology & Imaging
                Keywords: augmented reality,congenital heart disease,3d imaging,3d modeling,3d printing,virtual reality
                Data availability:
                ScienceOpen disciplines: Radiology & Imaging
                Keywords: augmented reality, congenital heart disease, 3d imaging, 3d modeling, 3d printing, virtual reality

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