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Automatic rigid image Fusion of preoperative MR and intraoperative US acquired after craniotomy

BACKGROUND: Neuronavigation of preoperative MRI is limited by several errors. Intraoperative ultrasound (iUS) with navigated probes that provide automatic superposition of pre-operative MRI and iUS and three-dimensional iUS reconstruction may overcome some of these limitations. Aim of the present st...

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Autores principales: Mazzucchi, Edoardo, Hiepe, Patrick, Langhof, Max, La Rocca, Giuseppe, Pignotti, Fabrizio, Rinaldi, Pierluigi, Sabatino, Giovanni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099637/
https://www.ncbi.nlm.nih.gov/pubmed/37055790
http://dx.doi.org/10.1186/s40644-023-00554-x
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author Mazzucchi, Edoardo
Hiepe, Patrick
Langhof, Max
La Rocca, Giuseppe
Pignotti, Fabrizio
Rinaldi, Pierluigi
Sabatino, Giovanni
author_facet Mazzucchi, Edoardo
Hiepe, Patrick
Langhof, Max
La Rocca, Giuseppe
Pignotti, Fabrizio
Rinaldi, Pierluigi
Sabatino, Giovanni
author_sort Mazzucchi, Edoardo
collection PubMed
description BACKGROUND: Neuronavigation of preoperative MRI is limited by several errors. Intraoperative ultrasound (iUS) with navigated probes that provide automatic superposition of pre-operative MRI and iUS and three-dimensional iUS reconstruction may overcome some of these limitations. Aim of the present study is to verify the accuracy of an automatic MRI – iUS fusion algorithm to improve MR-based neuronavigation accuracy. METHODS: An algorithm using Linear Correlation of Linear Combination (LC2)-based similarity metric has been retrospectively evaluated for twelve datasets acquired in patients with brain tumor. A series of landmarks were defined both in MRI and iUS scans. The Target Registration Error (TRE) was determined for each pair of landmarks before and after the automatic Rigid Image Fusion (RIF). The algorithm has been tested on two conditions of the initial image alignment: registration-based fusion (RBF), as given by the navigated ultrasound probe, and different simulated course alignments during convergence test. RESULTS: Except for one case RIF was successfully applied in all patients considering the RBF as initial alignment. Here, mean TRE after RBF was significantly reduced from 4.03 (± 1.40) mm to (2.08 ± 0.96 mm) (p = 0.002), after RIF. For convergence test, the mean TRE value after initial perturbations was 8.82 (± 0.23) mm which has been reduced to a mean TRE of 2.64 (± 1.20) mm after RIF (p < 0.001). CONCLUSIONS: The integration of an automatic image fusion method for co-registration of pre-operative MRI and iUS data may improve the accuracy in MR-based neuronavigation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-023-00554-x.
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spelling pubmed-100996372023-04-14 Automatic rigid image Fusion of preoperative MR and intraoperative US acquired after craniotomy Mazzucchi, Edoardo Hiepe, Patrick Langhof, Max La Rocca, Giuseppe Pignotti, Fabrizio Rinaldi, Pierluigi Sabatino, Giovanni Cancer Imaging Research Article BACKGROUND: Neuronavigation of preoperative MRI is limited by several errors. Intraoperative ultrasound (iUS) with navigated probes that provide automatic superposition of pre-operative MRI and iUS and three-dimensional iUS reconstruction may overcome some of these limitations. Aim of the present study is to verify the accuracy of an automatic MRI – iUS fusion algorithm to improve MR-based neuronavigation accuracy. METHODS: An algorithm using Linear Correlation of Linear Combination (LC2)-based similarity metric has been retrospectively evaluated for twelve datasets acquired in patients with brain tumor. A series of landmarks were defined both in MRI and iUS scans. The Target Registration Error (TRE) was determined for each pair of landmarks before and after the automatic Rigid Image Fusion (RIF). The algorithm has been tested on two conditions of the initial image alignment: registration-based fusion (RBF), as given by the navigated ultrasound probe, and different simulated course alignments during convergence test. RESULTS: Except for one case RIF was successfully applied in all patients considering the RBF as initial alignment. Here, mean TRE after RBF was significantly reduced from 4.03 (± 1.40) mm to (2.08 ± 0.96 mm) (p = 0.002), after RIF. For convergence test, the mean TRE value after initial perturbations was 8.82 (± 0.23) mm which has been reduced to a mean TRE of 2.64 (± 1.20) mm after RIF (p < 0.001). CONCLUSIONS: The integration of an automatic image fusion method for co-registration of pre-operative MRI and iUS data may improve the accuracy in MR-based neuronavigation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-023-00554-x. BioMed Central 2023-04-13 /pmc/articles/PMC10099637/ /pubmed/37055790 http://dx.doi.org/10.1186/s40644-023-00554-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Mazzucchi, Edoardo
Hiepe, Patrick
Langhof, Max
La Rocca, Giuseppe
Pignotti, Fabrizio
Rinaldi, Pierluigi
Sabatino, Giovanni
Automatic rigid image Fusion of preoperative MR and intraoperative US acquired after craniotomy
title Automatic rigid image Fusion of preoperative MR and intraoperative US acquired after craniotomy
title_full Automatic rigid image Fusion of preoperative MR and intraoperative US acquired after craniotomy
title_fullStr Automatic rigid image Fusion of preoperative MR and intraoperative US acquired after craniotomy
title_full_unstemmed Automatic rigid image Fusion of preoperative MR and intraoperative US acquired after craniotomy
title_short Automatic rigid image Fusion of preoperative MR and intraoperative US acquired after craniotomy
title_sort automatic rigid image fusion of preoperative mr and intraoperative us acquired after craniotomy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099637/
https://www.ncbi.nlm.nih.gov/pubmed/37055790
http://dx.doi.org/10.1186/s40644-023-00554-x
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