ArticleLa Radiologia medica2022
Multicenter assessment of augmented reality registration methods for image-guided interventions.
Article in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 23 citations in OpenAlex.
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- Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.Diagnostics (Basel, Switzerland) · 2024Article
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- Dose Reduction Strategies for Pregnant Women in Emergency Settings.Journal of clinical medicine · 2023Review
- Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications.Journal of clinical medicine · 2023Review
- Radiation Recall Pneumonitis: The Open Challenge in Differential Diagnosis of Pneumonia Induced by Oncological Treatments.Journal of clinical medicine · 2023Review
- Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence.Biology · 2023Review
- Diagnostic Management of Gastroenteropancreatic Neuroendocrine Neoplasms: Technique Optimization and Tips and Tricks for Radiologists.Tomography (Ann Arbor, Mich.) · 2023Review
- Immunotherapy Assessment: A New Paradigm for Radiologists.Diagnostics (Basel, Switzerland) · 2023Review
- Imaging Features of Main Hepatic Resections: The Radiologist Challenging.Journal of personalized medicine · 2023Review
- Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography.Current oncology (Toronto, Ont.) · 2023Article
- Percutanous Electrochemotherapy (ECT) in Primary and Secondary Liver Malignancies: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence.Cancers · 2023Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo evaluate manual and automatic registration times and registration accuracies on HoloLens 2 for aligning a 3D CT phantom model onto a CT grid, a crucial step for intuitive 3D navigation during CT-guided interventions; to compare registration times between HoloLens 1 and 2.
methodsEighteen participants in various stages of clinical training across two academic centers performed registration of a 3D CT phantom model onto a CT grid using HoloLens 2. Registration times and accuracies were compared among different registration methods, clinical experience levels, and consecutive attempts. Registration times were also compared retrospectively to prior HoloLens 1 results.
resultsMean aggregate manual registration times were 27.7 s, 24.3 s, and 72.8 s for one-handed gesture, two-handed gesture, and Xbox controller, respectively; mean automatic registration time was 5.3 s (ANOVA p < 0.0001). No significant difference in registration times was found among attendings, residents and fellows, and medical students (p > 0.05). Significant improvements in registration times were detected across consecutive attempts using hand gestures (p < 0.01). Compared to prior HoloLens 1 data, hand gesture registration was 81.7% faster with HoloLens 2 (p < 0.05). Registration accuracies were not significantly different across manual registration methods, measuring at 5.9 mm, 9.5 mm, and 8.6 mm with one-handed gesture, two-handed gesture, and Xbox controller, respectively (p > 0.05).
conclusionsManual registration times decreased significantly on HoloLens 2, approaching those of automatic registration and outperforming Xbox controller registration. Fast, adaptive, and accurate registration of holographic models of cross-sectional imaging is paramount for the implementation of augmented reality-assisted 3D navigation during CT-guided interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.