Smartphone Augmented Reality Outperforms Conventional Computed Tomography Guidance for Composite Ablation Margins in Phantom Models.


Journal

Journal of vascular and interventional radiology : JVIR
ISSN: 1535-7732
Titre abrégé: J Vasc Interv Radiol
Pays: United States
ID NLM: 9203369

Informations de publication

Date de publication:
16 Oct 2023
Historique:
received: 06 10 2022
revised: 23 09 2023
accepted: 08 10 2023
medline: 19 10 2023
pubmed: 19 10 2023
entrez: 18 10 2023
Statut: aheadofprint

Résumé

To develop and evaluate a smartphone augmented reality system for large 50mm liver tumor ablation with treatment planning for composite overlapping ablation zones. A smartphone AR application was developed to display tumor, probe, projected probe paths, ablated zones, and real-time percentage of target tumor volume ablated. Fiducial markers were attached to phantoms and ablation probe hub for tracking. The system was evaluated with tissue-mimicking thermochromic phantoms [1] and gel phantoms. Four operators performed 2 trials each of 3 probe insertions per trial using AR-guidance versus CT-guidance approaches, in 2 gel phantoms. Insertion points and optimal probe paths were pre-determined. On gel phantom 2, serial ablated zones were saved and continuously displayed after each probe placement/adjustment, enabling feedback and iterative planning. The percent tumor ablated for AR-guidance vs CT-guidance and with vs without display of recorded ablated zones was compared among operators with pairwise t-tests. The means of percent tumor ablated for CT freehand and AR-guidance were 36±7% and 47±4% (p=0.004), respectively. Mean composite percent ablated for AR-guidance was 43±1% (without) and 50±2% (with display of ablation zone), (p=0.033). There was no strong correlation between AR-guided percent ablation and years of experience (r<0.5), whereas CT-guided percent ablation and years of experiences had strong correlation (r>0.9). A smartphone AR guidance system for dynamic iterative large liver tumor ablation was accurate, performed better than conventional CT guidance, especially for less experienced operators, and enhanced more standardized performance across experience levels for ablation of a 50mm tumor.

Identifiants

pubmed: 37852601
pii: S1051-0443(23)00735-2
doi: 10.1016/j.jvir.2023.10.005
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023. Published by Elsevier Inc.

Auteurs

Katerina H Lee (KH)

McGovern Medical School at UTHealth, Houston, Texas, USA.

Ming Li (M)

Center for Interventional Oncology, National Institutes of Health, Bethesda, Maryland, USA.

Nicole Varble (N)

Center for Interventional Oncology, National Institutes of Health, Bethesda, Maryland, USA; Philips Research North America, Cambridge, Massachusetts, USA.

Ayele H Negussie (AH)

Center for Interventional Oncology, National Institutes of Health, Bethesda, Maryland, USA.

Michael Kassin (M)

Center for Interventional Oncology, National Institutes of Health, Bethesda, Maryland, USA.

Antonio Arrichiello (A)

Center for Interventional Oncology, National Institutes of Health, Bethesda, Maryland, USA.

Gianpaolo Carrafiello (G)

Department of Radiology, Foundation IRCCS Ca' Granda - Ospedale Maggiore Policlinico, University of Milan, Milan, Italy.

Lindsey Hazen (L)

Center for Interventional Oncology, National Institutes of Health, Bethesda, Maryland, USA.

Paul Wakim (P)

Biostatistics and Clinical Epidemiology Service, National Institutes of Health, Bethesda, Maryland, USA.

Xiaobai Li (X)

Biostatistics and Clinical Epidemiology Service, National Institutes of Health, Bethesda, Maryland, USA.

Sheng Xu (S)

Center for Interventional Oncology, National Institutes of Health, Bethesda, Maryland, USA.

Bradford J Wood (BJ)

Center for Interventional Oncology, National Institutes of Health, Bethesda, Maryland, USA.

Classifications MeSH