Assessment of pulmonary vascular anatomy: comparing augmented reality by holograms versus standard CT images/reconstructions using surgical findings as reference standard.
Augmented reality
Holograms
Lung neoplasms
Thoracic surgery
Tomography (x-ray computed)
Journal
European radiology experimental
ISSN: 2509-9280
Titre abrégé: Eur Radiol Exp
Pays: England
ID NLM: 101721752
Informations de publication
Date de publication:
10 May 2024
10 May 2024
Historique:
received:
19
12
2023
accepted:
07
03
2024
medline:
10
5
2024
pubmed:
10
5
2024
entrez:
9
5
2024
Statut:
epublish
Résumé
We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques. Patients scheduled for lobectomy for lung cancer were prospectively included and underwent CT for staging. A patient-specific three-dimensional model was generated and visualized in an augmented reality setting. One radiologist and one thoracic surgeon evaluated CT images and holograms to count lobar arteries, having as reference standard the number of arteries recorded at surgery. The easiness of vessel identification was graded according to a Likert scale. Wilcoxon signed-rank test and κ statistics were used. Fifty-two patients were prospectively included. The two doctors detected the same number of arteries in 44/52 images (85%) and in 51/52 holograms (98%). The mean difference between the number of artery branches detected by surgery and CT images was 0.31 ± 0.98, whereas it was 0.09 ± 0.37 between surgery and HGs (p = 0.433). In particular, the mean difference in the number of arteries detected in the upper lobes was 0.67 ± 1.08 between surgery and CT images and 0.17 ± 0.46 between surgery and holograms (p = 0.029). Both radiologist and surgeon showed a higher agreement for holograms (κ = 0.99) than for CT (κ = 0.81) and found holograms easier to evaluate than CTs (p < 0.001). Augmented reality by holograms is an effective tool for preoperative vascular anatomy assessment of lungs, especially when evaluating the upper lobes, more prone to anatomical variations. ClinicalTrials.gov, NCT04227444 RELEVANCE STATEMENT: Preoperative evaluation of the lung lobe arteries through augmented reality may help the thoracic surgeons to carefully plan a lobectomy, thus contributing to optimize patients' outcomes. • Preoperative assessment of the lung arteries may help surgical planning. • Lung artery detection by augmented reality was more accurate than that by CT images, particularly for the upper lobes. • The assessment of the lung arterial vessels was easier by using holograms than CT images.
Sections du résumé
BACKGROUND
BACKGROUND
We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques.
METHODS
METHODS
Patients scheduled for lobectomy for lung cancer were prospectively included and underwent CT for staging. A patient-specific three-dimensional model was generated and visualized in an augmented reality setting. One radiologist and one thoracic surgeon evaluated CT images and holograms to count lobar arteries, having as reference standard the number of arteries recorded at surgery. The easiness of vessel identification was graded according to a Likert scale. Wilcoxon signed-rank test and κ statistics were used.
RESULTS
RESULTS
Fifty-two patients were prospectively included. The two doctors detected the same number of arteries in 44/52 images (85%) and in 51/52 holograms (98%). The mean difference between the number of artery branches detected by surgery and CT images was 0.31 ± 0.98, whereas it was 0.09 ± 0.37 between surgery and HGs (p = 0.433). In particular, the mean difference in the number of arteries detected in the upper lobes was 0.67 ± 1.08 between surgery and CT images and 0.17 ± 0.46 between surgery and holograms (p = 0.029). Both radiologist and surgeon showed a higher agreement for holograms (κ = 0.99) than for CT (κ = 0.81) and found holograms easier to evaluate than CTs (p < 0.001).
CONCLUSIONS
CONCLUSIONS
Augmented reality by holograms is an effective tool for preoperative vascular anatomy assessment of lungs, especially when evaluating the upper lobes, more prone to anatomical variations.
TRIAL REGISTRATION
BACKGROUND
ClinicalTrials.gov, NCT04227444 RELEVANCE STATEMENT: Preoperative evaluation of the lung lobe arteries through augmented reality may help the thoracic surgeons to carefully plan a lobectomy, thus contributing to optimize patients' outcomes.
KEY POINTS
CONCLUSIONS
• Preoperative assessment of the lung arteries may help surgical planning. • Lung artery detection by augmented reality was more accurate than that by CT images, particularly for the upper lobes. • The assessment of the lung arterial vessels was easier by using holograms than CT images.
Identifiants
pubmed: 38724831
doi: 10.1186/s41747-024-00458-w
pii: 10.1186/s41747-024-00458-w
doi:
Banques de données
ClinicalTrials.gov
['NCT04227444']
Types de publication
Journal Article
Comparative Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
57Informations de copyright
© 2024. The Author(s).
Références
Smelt JLC, Suri T, Valencia O et al (2019) Operative planning in thoracic surgery: a pilot study comparing imaging techniques and three-dimensional printing. Ann Thorac Surg 107:401–406. https://doi.org/10.1016/j.athoracsur.2018.08.052
doi: 10.1016/j.athoracsur.2018.08.052
pubmed: 30316856
Fourdrain A, De Dominicis F, Blanchard C et al (2018) Three-dimensional CT angiography of anatomic variations in the pulmonary arterial tree. Surg Radiol Anat 40:45–53. https://doi.org/10.1007/s00276-017-1914-z
doi: 10.1007/s00276-017-1914-z
pubmed: 28856408
Cory R, Valentine E (1959) Varying patterns of the lobar branches of the pulmonary artery a study of 524 lungs and lobes seen at operation on 426 patients. Thorax 14:267–280. https://doi.org/10.1136/thx.14.4.267
doi: 10.1136/thx.14.4.267
pubmed: 13812149
pmcid: 1018514
Salfity H, Tong BC (2020) VATS and minimally invasive resection in early-stage NSCLC. Semin Respir Crit Care Med 41:335–345. https://doi.org/10.1055/s-0039-3401991
doi: 10.1055/s-0039-3401991
pubmed: 32450587
Hagiwara M, Shimada Y, Kato Y et al (2014) High-quality 3-dimensional image simulation for pulmonary lobectomy and segmentectomy: results of preoperative assessment of pulmonary vessels and short-term surgical outcomes in consecutive patients undergoing video-assisted thoracic surgery. Eur J Cardiothorac Surg 46:e120-6. https://doi.org/10.1093/ejcts/ezu375
doi: 10.1093/ejcts/ezu375
pubmed: 25342848
Fukuhara K, Akashi A, Nakane S, Tomita E (2008) Preoperative assessment of the pulmonary artery by three-dimensional computed tomography before video-assisted thoracic surgery lobectomy. Eur J Cardiothorac Surg 34:875–877. https://doi.org/10.1016/j.ejcts.2008.07.014
doi: 10.1016/j.ejcts.2008.07.014
pubmed: 18703345
Zheng W, Zhang M, Wu W, Zhang H, Zhang X (2022) Three-dimensional CT angiography facilitates uniportal thoracoscopic anatomic lung resection for pulmonary sequestration: a retrospective cohort study. J Cardiothorac Surg 17:218. https://doi.org/10.1186/s13019-022-01975-8
doi: 10.1186/s13019-022-01975-8
pubmed: 36042500
pmcid: 9429313
Gabor D, Kock WE, Stroke GW (1971) Holography. Science 173:11–23. https://doi.org/10.1126/science.173.3991.11
doi: 10.1126/science.173.3991.11
pubmed: 17747305
Jung C, Wolff G, Wernly B et al (2022) Virtual and augmented reality in cardiovascular care: state-of-the-art and future perspectives. JACC Cardiovasc Imaging 15:519–532. https://doi.org/10.1016/j.jcmg.2021.08.017
doi: 10.1016/j.jcmg.2021.08.017
pubmed: 34656478
Kumar RP, Pelanis E, Bugge R et al (2020) Use of mixed reality for surgery planning: assessment and development workflow. J Biomed Inform 112S:100077. https://doi.org/10.1016/j.yjbinx.2020.100077
doi: 10.1016/j.yjbinx.2020.100077
pubmed: 34417006
Dolega-Dolegowski D, Proniewska K, Dolega-Dolegowska M et al (2022) Application of holography and augmented reality based technology to visualize the internal structure of the dental root - a proof of concept. Head Face Med 18:12. https://doi.org/10.1186/s13005-022-00307-4
doi: 10.1186/s13005-022-00307-4
pubmed: 35382839
pmcid: 8981712
Cuomo S, De Michele P, Piccialli F (2014) 3D data denoising via nonlocal means filter by using parallel GPU strategies. Comput Math Methods Med 2014:52386. https://doi.org/10.1155/2014/523862
doi: 10.1155/2014/523862
Botta F, Raimondi S, Rinaldi L et al (2020) Association of a CT-based clinical and radiomics score of non-small cell lung cancer (NSCLC) with lymph node status and overall survival. Cancers 12:1432. https://doi.org/10.3390/cancers12061432
doi: 10.3390/cancers12061432
pubmed: 32486453
pmcid: 7352293
Rizzo SM, Kalra MK, Schmidt B et al (2005) CT images of abdomen and pelvis: effect of nonlinear three-dimensional optimized reconstruction algorithm on image quality and lesion characteristics. Radiology 237:309–315. https://doi.org/10.1148/radiol.2371041879
doi: 10.1148/radiol.2371041879
pubmed: 16183939
Kalra MK, Rizzo S, Maher MM et al (2005) Chest CT performed with z-axis modulation: scanning protocol and radiation dose. Radiology 237:303–308. https://doi.org/10.1148/radiol.2371041227
doi: 10.1148/radiol.2371041227
pubmed: 16183938
Hernandez AM, Abbey CK, Ghazi P, Burkett G, Boone JM (2020) Effects of kV, filtration, dose, and object size on soft tissue and iodine contrast in dedicated breast CT. Med Phys 47:2869–2880. https://doi.org/10.1002/mp.14159
doi: 10.1002/mp.14159
pubmed: 32233091
Jung J, Kim K, Ahn M et al (2011) Detection of pulmonary embolism using 64-slice multidetector-row computed tomography: accuracy and reproducibility on different image reconstruction parameters. Acta Radiol 52:417–421. https://doi.org/10.1258/ar.2011.100217
doi: 10.1258/ar.2011.100217
pubmed: 21498315
Boiselle P, Reynolds K, Ernst A (2002) Multiplanar and three-dimensional imaging of the central airways with multidetector CT. AJR Am J Roentgenol 179:301–308. https://doi.org/10.2214/ajr.179.2.1790301
doi: 10.2214/ajr.179.2.1790301
pubmed: 12130424
Ong CW, Tan MCJ, Lam M, Koh VTC (2021) Applications of extended reality in ophthalmology: systematic review. J Med Internet Res 23:e24152. https://doi.org/10.2196/24152
doi: 10.2196/24152
pubmed: 34420929
pmcid: 8414293
Mangano FG, Admakin O, Lerner H, Mangano C (2023) Artificial intelligence and augmented reality for guided implant surgery planning: a proof of concept. J Dent 133:104485. https://doi.org/10.1016/j.jdent.2023.104485
doi: 10.1016/j.jdent.2023.104485
pubmed: 36965859
Ryu S, Kitagawa T, Goto K, et al. (2022) Intraoperative holographic guidance using virtual reality and mixed reality technology during laparoscopic colorectal cancer surgery. Anticancer Res. 42:4849-4856. https://doi.org/10.21873/anticanres.15990
Tokunaga T, Sugimoto M, Saito Y et al (2022) Intraoperative holographic image-guided surgery in a transanal approach for rectal cancer. Langenbecks Arch Surg 407:2579–2584. https://doi.org/10.1007/s00423-022-02607-4
doi: 10.1007/s00423-022-02607-4
pubmed: 35840706
Chessa M, Van De Bruaene A, Farooqi K et al (2022) A three-dimensional printing, holograms, computational modelling, and artificial intelligence for adult congenital heart disease care: an exciting future. Eur Heart J 43:2672–2684. https://doi.org/10.1093/eurheartj/ehac266
doi: 10.1093/eurheartj/ehac266
pubmed: 35608227
Ramesh PV, Joshua T, Ray P et al (2022) Holographic elysium of a 4D ophthalmic anatomical and pathological metaverse with extended reality/mixed reality. Indian J Ophthalmol 70:3116–3121. https://doi.org/10.4103/ijo.IJO_120_22
doi: 10.4103/ijo.IJO_120_22
pubmed: 35918983
pmcid: 9672738
García-Garrigós E, Arenas-Jiménez JJ, Sánchez-Payá J (2018) Best protocol for combined contrast-enhanced thoracic and abdominal CT for lung cancer: a single-institution randomized crossover clinical trial. AJR Am J Roentgenol 210:1226–1234. https://doi.org/10.2214/AJR.17.19185
doi: 10.2214/AJR.17.19185
pubmed: 29570376