TY - JOUR
T1 - Virtual and Augmented Reality in Interventional Radiology
T2 - Current Applications, Challenges, and Future Directions
AU - Elsakka, Ahmed
AU - Park, Brian J.
AU - Marinelli, Brett
AU - Swinburne, Nathaniel C.
AU - Schefflein, Javin
N1 - Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2023/9
Y1 - 2023/9
N2 - Virtual reality (VR) and augmented Reality (AR) are emerging technologies with the potential to revolutionize Interventional radiology (IR). These innovations offer advantages in patient care, interventional planning, and educational training by improving the visualization and navigation of medical images. Despite progress, several challenges hinder their widespread adoption, including limitations in navigation systems, cost, clinical acceptance, and technical constraints of AR/VR equipment. However, ongoing research holds promise with recent advancements such as shape-sensing needles and improved organ deformation modeling. The development of deep learning techniques, particularly for medical imaging segmentation, presents a promising avenue to address existing accuracy and precision issues. Future applications of AR/VR in IR include simulation-based training, preprocedural planning, intraprocedural guidance, and increased patient engagement. As these technologies advance, they are expected to facilitate telemedicine, enhance operational efficiency, and improve patient outcomes, marking a new frontier in interventional radiology.
AB - Virtual reality (VR) and augmented Reality (AR) are emerging technologies with the potential to revolutionize Interventional radiology (IR). These innovations offer advantages in patient care, interventional planning, and educational training by improving the visualization and navigation of medical images. Despite progress, several challenges hinder their widespread adoption, including limitations in navigation systems, cost, clinical acceptance, and technical constraints of AR/VR equipment. However, ongoing research holds promise with recent advancements such as shape-sensing needles and improved organ deformation modeling. The development of deep learning techniques, particularly for medical imaging segmentation, presents a promising avenue to address existing accuracy and precision issues. Future applications of AR/VR in IR include simulation-based training, preprocedural planning, intraprocedural guidance, and increased patient engagement. As these technologies advance, they are expected to facilitate telemedicine, enhance operational efficiency, and improve patient outcomes, marking a new frontier in interventional radiology.
KW - augmented reality
KW - interventional oncology
KW - interventional radiology
KW - mixed reality
KW - virtual reality
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U2 - 10.1016/j.tvir.2023.100919
DO - 10.1016/j.tvir.2023.100919
M3 - Article
C2 - 38071031
AN - SCOPUS:85176096597
SN - 1089-2516
VL - 26
JO - Techniques in Vascular and Interventional Radiology
JF - Techniques in Vascular and Interventional Radiology
IS - 3
M1 - 100919
ER -