Evidence map›Paper›PMID 35737194›Full record

ArticleLa Radiologia medica2022

Multicenter assessment of augmented reality registration methods for image-guided interventions.

Ningcheng Li, Jonathan Wakim, Yilun Koethe, Timothy Huber, Ryan Schenning, Terence P Gade, Stephen J Hunt, Brian J Park

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.2field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 23 citations in OpenAlex.

  1. Manual Registration in AR-Assisted Surgical Navigation: A Comparative Evaluation.International journal of computer assisted radiology and surgery · 2026
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  15. Immunotherapy Assessment: A New Paradigm for Radiologists.Diagnostics (Basel, Switzerland) · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Ningcheng LiDotter Department of Interventional Radiology, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd, Portland, OR, 97239, USA.ORCID http://orcid.org/0000-0003-0143-8648
Jonathan WakimDepartment of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA, 19104, USA.
Yilun KoetheDotter Department of Interventional Radiology, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd, Portland, OR, 97239, USA.
Timothy HuberDotter Department of Interventional Radiology, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd, Portland, OR, 97239, USA.
Ryan SchenningDotter Department of Interventional Radiology, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd, Portland, OR, 97239, USA.
Terence P GadeDepartment of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA, 19104, USA.
Stephen J HuntDepartment of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA, 19104, USA.
Brian J ParkDotter Department of Interventional Radiology, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd, Portland, OR, 97239, USA. parkbr@ohsu.edu.
Oregon Health & Science University · USUniversity of Pennsylvania · US

Funding

Foundation for the National Institutes of Health 5T32EB004311
6 · The paper itself

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.

Indexed as

Augmented RealitySurgery, Computer-AssistedHumansImaging, Three-DimensionalRetrospective Studies3D visualizationAugmented realityCT-guided interventionHoloLensInterventional radiologyMixed realityRegistration

Identifiers

PMID35737194
OpenAlexW3112029917

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.