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A workflow to generate patient-specific three-dimensional augmented reality models from medical imaging data and example applications in urologic oncology
Augmented reality (AR) and virtual reality (VR) are burgeoning technologies that have the potential to greatly enhance patient care. Visualizing patient-specific three-dimensional (3D) imaging data in these enhanced virtual environments may improve surgeons’ understanding of anatomy and surgical pat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8554989/ https://www.ncbi.nlm.nih.gov/pubmed/34709482 http://dx.doi.org/10.1186/s41205-021-00125-5 |
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author | Wake, Nicole Rosenkrantz, Andrew B. Huang, William C. Wysock, James S. Taneja, Samir S. Sodickson, Daniel K. Chandarana, Hersh |
author_facet | Wake, Nicole Rosenkrantz, Andrew B. Huang, William C. Wysock, James S. Taneja, Samir S. Sodickson, Daniel K. Chandarana, Hersh |
author_sort | Wake, Nicole |
collection | PubMed |
description | Augmented reality (AR) and virtual reality (VR) are burgeoning technologies that have the potential to greatly enhance patient care. Visualizing patient-specific three-dimensional (3D) imaging data in these enhanced virtual environments may improve surgeons’ understanding of anatomy and surgical pathology, thereby allowing for improved surgical planning, superior intra-operative guidance, and ultimately improved patient care. It is important that radiologists are familiar with these technologies, especially since the number of institutions utilizing VR and AR is increasing. This article gives an overview of AR and VR and describes the workflow required to create anatomical 3D models for use in AR using the Microsoft HoloLens device. Case examples in urologic oncology (prostate cancer and renal cancer) are provided which depict how AR has been used to guide surgery at our institution. |
format | Online Article Text |
id | pubmed-8554989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-85549892021-10-29 A workflow to generate patient-specific three-dimensional augmented reality models from medical imaging data and example applications in urologic oncology Wake, Nicole Rosenkrantz, Andrew B. Huang, William C. Wysock, James S. Taneja, Samir S. Sodickson, Daniel K. Chandarana, Hersh 3D Print Med Technical Note Augmented reality (AR) and virtual reality (VR) are burgeoning technologies that have the potential to greatly enhance patient care. Visualizing patient-specific three-dimensional (3D) imaging data in these enhanced virtual environments may improve surgeons’ understanding of anatomy and surgical pathology, thereby allowing for improved surgical planning, superior intra-operative guidance, and ultimately improved patient care. It is important that radiologists are familiar with these technologies, especially since the number of institutions utilizing VR and AR is increasing. This article gives an overview of AR and VR and describes the workflow required to create anatomical 3D models for use in AR using the Microsoft HoloLens device. Case examples in urologic oncology (prostate cancer and renal cancer) are provided which depict how AR has been used to guide surgery at our institution. Springer International Publishing 2021-10-28 /pmc/articles/PMC8554989/ /pubmed/34709482 http://dx.doi.org/10.1186/s41205-021-00125-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Technical Note Wake, Nicole Rosenkrantz, Andrew B. Huang, William C. Wysock, James S. Taneja, Samir S. Sodickson, Daniel K. Chandarana, Hersh A workflow to generate patient-specific three-dimensional augmented reality models from medical imaging data and example applications in urologic oncology |
title | A workflow to generate patient-specific three-dimensional augmented reality models from medical imaging data and example applications in urologic oncology |
title_full | A workflow to generate patient-specific three-dimensional augmented reality models from medical imaging data and example applications in urologic oncology |
title_fullStr | A workflow to generate patient-specific three-dimensional augmented reality models from medical imaging data and example applications in urologic oncology |
title_full_unstemmed | A workflow to generate patient-specific three-dimensional augmented reality models from medical imaging data and example applications in urologic oncology |
title_short | A workflow to generate patient-specific three-dimensional augmented reality models from medical imaging data and example applications in urologic oncology |
title_sort | workflow to generate patient-specific three-dimensional augmented reality models from medical imaging data and example applications in urologic oncology |
topic | Technical Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8554989/ https://www.ncbi.nlm.nih.gov/pubmed/34709482 http://dx.doi.org/10.1186/s41205-021-00125-5 |
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