Literature DB >> 9179893

Use of cortical surface vessel registration for image-guided neurosurgery.

S Nakajima1, H Atsumi, R Kikinis, T M Moriarty, D C Metcalf, F A Jolesz, P M Black.   

Abstract

OBJECTIVE: We have treated patients with brain surface tumors by using video registration of a three-dimensional image to the surgical field, to identify eloquent cortices, localize the lesions, and define the tumor margins. "Skin-to-skin" registration using the skin surface to produce alignment was performed earlier but was difficult in areas with few prominent registration landmarks. For this reason, "vessel-to-vessel" registration using the cortical vessels as fiducials was applied to 17 cases, to improve accuracy. This article presents the advantages and limitations of vessel-to-vessel registration, as determined from the data for these cases. The accuracy is also estimated.
METHODS: A three-dimensional model was reconstructed from magnetic resonance imaging data, and a two-dimensional projection was superimposed on the video image of the actual surgical field. The tumor was resected with guidance from the registered video image. The two-dimensional projection accuracy of vessel-to-vessel registration was compared with that of skin-to-skin registration by using a phantom study.
RESULTS: All 17 tumors underwent gross total resection, and the patients experienced no major permanent neurological deficits. In the phantom study, the two-dimensional, projected, target registration error of a tumor with skin-to-skin registration was estimated as 8.9 +/- 5.3 mm and that with vessel-to-vessel registration was 1.3 +/- 1.4 mm (99th percentile confidence intervals, 24.8 and 5.5 mm, respectively).
CONCLUSION: Video registration using cortical surface vessels is practical and improves two-dimensional projection accuracy significantly, compared with skin registration.

Entities:  

Mesh:

Year:  1997        PMID: 9179893     DOI: 10.1097/00006123-199706000-00018

Source DB:  PubMed          Journal:  Neurosurgery        ISSN: 0148-396X            Impact factor:   4.654


  17 in total

1.  Cortical surface registration for image-guided neurosurgery using laser-range scanning.

Authors:  Michael I Miga; Tuhin K Sinha; David M Cash; Robert L Galloway; Robert J Weil
Journal:  IEEE Trans Med Imaging       Date:  2003-08       Impact factor: 10.048

2.  Validation of a hybrid Doppler ultrasound vessel-based registration algorithm for neurosurgery.

Authors:  Sean Jy-Shyang Chen; Ingerid Reinertsen; Pierrick Coupé; Charles X B Yan; Laurence Mercier; D Rolando Del Maestro; D Louis Collins
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-03-24       Impact factor: 2.924

3.  Nonrigid 3D brain registration using intensity/feature information.

Authors:  Christine DeLorenzo; Xenophon Papademetris; Kun Wu; Kenneth P Vives; Dennis Spencer; James S Duncan
Journal:  Med Image Comput Comput Assist Interv       Date:  2006

4.  A comprehensive system for intraoperative 3D brain deformation recovery.

Authors:  Christine DeLorenzo; Xenophon Papademetris; Kenneth P Vives; Dennis D Spencer; James S Duncan
Journal:  Med Image Comput Comput Assist Interv       Date:  2007

5.  Laser range scanning for image-guided neurosurgery: investigation of image-to-physical space registrations.

Authors:  Aize Cao; R C Thompson; P Dumpuri; B M Dawant; R L Galloway; S Ding; M I Miga
Journal:  Med Phys       Date:  2008-04       Impact factor: 4.071

6.  Brain-shift compensation by non-rigid registration of intra-operative ultrasound images with preoperative MR images based on residual complexity.

Authors:  P Farnia; A Ahmadian; T Shabanian; N D Serej; J Alirezaie
Journal:  Int J Comput Assist Radiol Surg       Date:  2014-07-04       Impact factor: 2.924

7.  A projected landmark method for reduction of registration error in image-guided surgery systems.

Authors:  Nasim Dadashi Serej; Alireza Ahmadian; Saeed Mohagheghi; Seyed Musa Sadrehosseini
Journal:  Int J Comput Assist Radiol Surg       Date:  2014-05-28       Impact factor: 2.924

8.  Intraoperative cortical surface characterization using laser range scanning: preliminary results.

Authors:  Tuhin K Sinha; Michael I Miga; David M Cash; Robert J Weil
Journal:  Neurosurgery       Date:  2006-10       Impact factor: 4.654

9.  Marker-less tracking of brain surface deformations by non-rigid registration integrating surface and vessel/sulci features.

Authors:  Jue Jiang; Yoshikazu Nakajima; Yoshio Sohma; Toki Saito; Taichi Kin; Horoshi Oyama; Nobuhito Saito
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-03-05       Impact factor: 2.924

10.  Semiautomatic registration of pre- and postbrain tumor resection laser range data: method and validation.

Authors:  Siyi Ding; Michael I Miga; Jack H Noble; Aize Cao; Prashanth Dumpuri; Reid C Thompson; Benoit M Dawant
Journal:  IEEE Trans Biomed Eng       Date:  2008-10-10       Impact factor: 4.538

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