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Kukla P, Maciejewska K, Strojna I, Zapał M, Zwierzchowski G, Bąk B. Extended Reality in Diagnostic Imaging-A Literature Review. Tomography 2023; 9:1071-1082. [PMID: 37368540 DOI: 10.3390/tomography9030088] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
The utilization of extended reality (ER) has been increasingly explored in the medical field over the past ten years. A comprehensive analysis of scientific publications was conducted to assess the applications of ER in the field of diagnostic imaging, including ultrasound, interventional radiology, and computed tomography. The study also evaluated the use of ER in patient positioning and medical education. Additionally, we explored the potential of ER as a replacement for anesthesia and sedation during examinations. The use of ER technologies in medical education has received increased attention in recent years. This technology allows for a more interactive and engaging educational experience, particularly in anatomy and patient positioning, although the question may be asked: is the technology and maintenance cost worth the investment? The results of the analyzed studies suggest that implementing augmented reality in clinical practice is a positive phenomenon that expands the diagnostic capabilities of imaging studies, education, and positioning. The results suggest that ER has significant potential to improve diagnostic imaging procedures' accuracy and efficiency and enhance the patient experience through increased visualization and understanding of medical conditions. Despite these promising advancements, further research is needed to fully realize the potential of ER in the medical field and to address the challenges and limitations associated with its integration into clinical practice.
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Affiliation(s)
- Paulina Kukla
- Department of Electroradiology, Poznan University of Medical Sciences, 61-866 Poznan, Poland
| | - Karolina Maciejewska
- Department of Electroradiology, Poznan University of Medical Sciences, 61-866 Poznan, Poland
| | - Iga Strojna
- Department of Electroradiology, Poznan University of Medical Sciences, 61-866 Poznan, Poland
| | - Małgorzata Zapał
- Department of Electroradiology, Poznan University of Medical Sciences, 61-866 Poznan, Poland
- Department of Adult Neurology, Medical University of Gdansk, 80-210 Gdansk, Poland
| | - Grzegorz Zwierzchowski
- Department of Electroradiology, Poznan University of Medical Sciences, 61-866 Poznan, Poland
- Department of Medical Physics, Greater Poland Cancer Centre, 61-866 Poznan, Poland
| | - Bartosz Bąk
- Department of Electroradiology, Poznan University of Medical Sciences, 61-866 Poznan, Poland
- Department of Radiotherapy II, Greater Poland Cancer Centre, 61-866 Poznan, Poland
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Zhou L, Fan M, Hansen C, Johnson CR, Weiskopf D. A Review of Three-Dimensional Medical Image Visualization. HEALTH DATA SCIENCE 2022; 2022:9840519. [PMID: 38487486 PMCID: PMC10880180 DOI: 10.34133/2022/9840519] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 03/17/2022] [Indexed: 03/17/2024]
Abstract
Importance. Medical images are essential for modern medicine and an important research subject in visualization. However, medical experts are often not aware of the many advanced three-dimensional (3D) medical image visualization techniques that could increase their capabilities in data analysis and assist the decision-making process for specific medical problems. Our paper provides a review of 3D visualization techniques for medical images, intending to bridge the gap between medical experts and visualization researchers.Highlights. Fundamental visualization techniques are revisited for various medical imaging modalities, from computational tomography to diffusion tensor imaging, featuring techniques that enhance spatial perception, which is critical for medical practices. The state-of-the-art of medical visualization is reviewed based on a procedure-oriented classification of medical problems for studies of individuals and populations. This paper summarizes free software tools for different modalities of medical images designed for various purposes, including visualization, analysis, and segmentation, and it provides respective Internet links.Conclusions. Visualization techniques are a useful tool for medical experts to tackle specific medical problems in their daily work. Our review provides a quick reference to such techniques given the medical problem and modalities of associated medical images. We summarize fundamental techniques and readily available visualization tools to help medical experts to better understand and utilize medical imaging data. This paper could contribute to the joint effort of the medical and visualization communities to advance precision medicine.
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Affiliation(s)
- Liang Zhou
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Mengjie Fan
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Charles Hansen
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA
| | - Chris R. Johnson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA
| | - Daniel Weiskopf
- Visualization Research Center (VISUS), University of Stuttgart, Stuttgart, Germany
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Jadhav S, Dmitriev K, Marino J, Barish M, Kaufman AE. 3D Virtual Pancreatography. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:1457-1468. [PMID: 32870794 PMCID: PMC8884473 DOI: 10.1109/tvcg.2020.3020958] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We present 3D virtual pancreatography (VP), a novel visualization procedure and application for non-invasive diagnosis and classification of pancreatic lesions, the precursors of pancreatic cancer. Currently, non-invasive screening of patients is performed through visual inspection of 2D axis-aligned CT images, though the relevant features are often not clearly visible nor automatically detected. VP is an end-to-end visual diagnosis system that includes: A machine learning based automatic segmentation of the pancreatic gland and the lesions, a semi-automatic approach to extract the primary pancreatic duct, a machine learning based automatic classification of lesions into four prominent types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. We combine volume rendering with pancreas- and lesion-centric visualizations and measurements for effective diagnosis. We designed VP through close collaboration and feedback from expert radiologists, and evaluated it on multiple real-world CT datasets with various pancreatic lesions and case studies examined by the expert radiologists.
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Maloca PM, Faludi B, Zelechowski M, Jud C, Vollmar T, Hug S, Müller PL, de Carvalho ER, Zarranz-Ventura J, Reich M, Lange C, Egan C, Tufail A, Hasler PW, Scholl HPN, Cattin PC. Validation of virtual reality orbitometry bridges digital and physical worlds. Sci Rep 2020; 10:11815. [PMID: 32678297 PMCID: PMC7366721 DOI: 10.1038/s41598-020-68867-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 06/22/2020] [Indexed: 11/09/2022] Open
Abstract
Clinical science and medical imaging technology are traditionally displayed in two dimensions (2D) on a computer monitor. In contrast, three-dimensional (3D) virtual reality (VR) expands the realm of 2D image visualization, enabling an immersive VR experience with unhindered spatial interaction by the user. Thus far, analysis of data extracted from VR applications was mainly qualitative. In this study, we enhance VR and provide evidence for quantitative VR research by validating digital VR display of computed tomography (CT) data of the orbit. Volumetric CT data were transferred and rendered into a VR environment. Subsequently, seven graders performed repeated and blinded diameter measurements. The intergrader variability of the measurements in VR was much lower compared to measurements in the physical world and measurements were reasonably consistent with their corresponding elements in the real context. The overall VR measurements were 5.49% higher. As such, this study attests the ability of VR to provide similar quantitative data alongside the added benefit of VR interfaces. VR entails a lot of potential for the future research in ophthalmology and beyond in any scientific field that uses three-dimensional data.
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Affiliation(s)
- Peter M Maloca
- Institute of Molecular and Clinical Ophthalmology Basel (IOB), 4031, Basel, Switzerland. .,OCTlab, Department of Ophthalmology, University Hospital Basel, 4031, Basel, Switzerland. .,Department of Ophthalmology, University of Basel, 4031, Basel, Switzerland. .,Moorfields Eye Hospital NHS Foundation Trust, London, EC1V 2PD, UK.
| | - Balázs Faludi
- Center for Medical Image Analysis & Navigation, University of Basel, 4031, Basel, Switzerland
| | - Marek Zelechowski
- Center for Medical Image Analysis & Navigation, University of Basel, 4031, Basel, Switzerland
| | - Christoph Jud
- Center for Medical Image Analysis & Navigation, University of Basel, 4031, Basel, Switzerland
| | - Theo Vollmar
- MRZ Medical Radiology Center, 6004, Lucerne, Switzerland
| | - Sibylle Hug
- MRZ Medical Radiology Center, 6004, Lucerne, Switzerland
| | - Philipp L Müller
- Moorfields Eye Hospital NHS Foundation Trust, London, EC1V 2PD, UK
| | | | | | - Michael Reich
- Faculty of Medicine, Eye Center, Albert-Ludwigs University Freiburg, 79085, Freiburg, Germany
| | - Clemens Lange
- Faculty of Medicine, Eye Center, Albert-Ludwigs University Freiburg, 79085, Freiburg, Germany
| | - Catherine Egan
- Moorfields Eye Hospital NHS Foundation Trust, London, EC1V 2PD, UK
| | - Adnan Tufail
- Moorfields Eye Hospital NHS Foundation Trust, London, EC1V 2PD, UK
| | - Pascal W Hasler
- OCTlab, Department of Ophthalmology, University Hospital Basel, 4031, Basel, Switzerland.,Department of Ophthalmology, University of Basel, 4031, Basel, Switzerland
| | - Hendrik P N Scholl
- Institute of Molecular and Clinical Ophthalmology Basel (IOB), 4031, Basel, Switzerland.,Department of Ophthalmology, University of Basel, 4031, Basel, Switzerland.,Wilmer Eye Institute, Johns Hopkins University, Baltimore, 21287, USA
| | - Philippe C Cattin
- Center for Medical Image Analysis & Navigation, University of Basel, 4031, Basel, Switzerland
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