Literature DB >> 18491553

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

Aize Cao1, R C Thompson, P Dumpuri, B M Dawant, R L Galloway, S Ding, M I Miga.   

Abstract

In this article a comprehensive set of registration methods is utilized to provide image-to-physical space registration for image-guided neurosurgery in a clinical study. Central to all methods is the use of textured point clouds as provided by laser range scanning technology. The objective is to perform a systematic comparison of registration methods that include both extracranial (skin marker point-based registration (PBR), and face-based surface registration) and intracranial methods (feature PBR, cortical vessel-contour registration, a combined geometry/intensity surface registration method, and a constrained form of that method to improve robustness). The platform facilitates the selection of discrete soft-tissue landmarks that appear on the patient's intraoperative cortical surface and the preoperative gadolinium-enhanced magnetic resonance (MR) image volume, i.e., true corresponding novel targets. In an 11 patient study, data were taken to allow statistical comparison among registration methods within the context of registration error. The results indicate that intraoperative face-based surface registration is statistically equivalent to traditional skin marker registration. The four intracranial registration methods were investigated and the results demonstrated a target registration error of 1.6 +/- 0.5 mm, 1.7 +/- 0.5 mm, 3.9 +/- 3.4 mm, and 2.0 +/- 0.9 mm, for feature PBR, cortical vessel-contour registration, unconstrained geometric/intensity registration, and constrained geometric/intensity registration, respectively. When analyzing the results on a per case basis, the constrained geometric/intensity registration performed best, followed by feature PBR, and finally cortical vessel-contour registration. Interestingly, the best target registration errors are similar to targeting errors reported using bone-implanted markers within the context of rigid targets. The experience in this study as with others is that brain shift can compromise extracranial registration methods from the earliest stages. Based on the results reported here, organ-based approaches to registration would improve this, especially for shallow lesions.

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Year:  2008        PMID: 18491553      PMCID: PMC2811558          DOI: 10.1118/1.2870216

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  20 in total

1.  Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, Methodology and validation on normal subjects.

Authors:  B M Dawant; S L Hartmann; J P Thirion; F Maes; D Vandermeulen; P Demaerel
Journal:  IEEE Trans Med Imaging       Date:  1999-10       Impact factor: 10.048

2.  Image-guided surgery.

Authors:  W E Grimson; R Kikinis; F A Jolesz; P M Black
Journal:  Sci Am       Date:  1999-06       Impact factor: 2.142

3.  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

4.  A method to track cortical surface deformations using a laser range scanner.

Authors:  Tuhin K Sinha; Benoit M Dawant; Valerie Duay; David M Cash; Robert J Weil; Reid C Thompson; Kyle D Weaver; Michael I Miga
Journal:  IEEE Trans Med Imaging       Date:  2005-06       Impact factor: 10.048

5.  Predicting error in rigid-body point-based registration.

Authors:  J M Fitzpatrick; J B West; C R Maurer
Journal:  IEEE Trans Med Imaging       Date:  1998-10       Impact factor: 10.048

6.  Least-squares fitting of two 3-d point sets.

Authors:  K S Arun; T S Huang; S D Blostein
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  1987-05       Impact factor: 6.226

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

Authors:  S Nakajima; H Atsumi; R Kikinis; T M Moriarty; D C Metcalf; F A Jolesz; P M Black
Journal:  Neurosurgery       Date:  1997-06       Impact factor: 4.654

8.  Quantification of true in vivo (application) accuracy in cranial image-guided surgery: influence of mode of patient registration.

Authors:  Christopher R Mascott; Jean-Christophe Sol; Philippe Bousquet; Jacques Lagarrigue; Yves Lazorthes; Valérie Lauwers-Cances
Journal:  Neurosurgery       Date:  2006-07       Impact factor: 4.654

9.  Automated laser registration in image-guided surgery: evaluation of the correlation between laser scan resolution and navigation accuracy.

Authors:  R Marmulla; T Lüth; J Mühling; S Hassfeld
Journal:  Int J Oral Maxillofac Surg       Date:  2004-10       Impact factor: 2.789

10.  Error assessment during "image guided" and "imaging interactive" stereotactic surgery.

Authors:  H J Nauta
Journal:  Comput Med Imaging Graph       Date:  1994 Jul-Aug       Impact factor: 4.790

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  23 in total

1.  Design and evaluation of an optically-tracked single-CCD laser range scanner.

Authors:  Thomas S Pheiffer; Amber L Simpson; Brian Lennon; Reid C Thompson; Michael I Miga
Journal:  Med Phys       Date:  2012-02       Impact factor: 4.071

2.  Intraoperative brain shift compensation: accounting for dural septa.

Authors:  Ishita Chen; Aaron M Coffey; Siyi Ding; Prashanth Dumpuri; Benoit M Dawant; Reid C Thompson; Michael I Miga
Journal:  IEEE Trans Biomed Eng       Date:  2010-11-22       Impact factor: 4.538

3.  Generalized iterative most likely oriented-point (G-IMLOP) registration.

Authors:  Seth Billings; Russell Taylor
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-05-23       Impact factor: 2.924

4.  Android application for determining surgical variables in brain-tumor resection procedures.

Authors:  Rohan C Vijayan; Reid C Thompson; Lola B Chambless; Peter J Morone; Le He; Logan W Clements; Rebekah H Griesenauer; Hakmook Kang; Michael I Miga
Journal:  J Med Imaging (Bellingham)       Date:  2017-03-02

5.  Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases.

Authors:  Michael I Miga; Kay Sun; Ishita Chen; Logan W Clements; Thomas S Pheiffer; Amber L Simpson; Reid C Thompson
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-10-17       Impact factor: 2.924

6.  Intraoperative application of hand-held structured light scanning: a feasibility study.

Authors:  Brandon Chan; Jason Auyeung; John F Rudan; Randy E Ellis; Manuela Kunz
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-03-26       Impact factor: 2.924

7.  Regional-surface-based registration for image-guided neurosurgery: effects of scan modes on registration accuracy.

Authors:  Yuan Dong; Chenxi Zhang; Dafeng Ji; Manning Wang; Zhijian Song
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-05-04       Impact factor: 2.924

8.  Near Real-Time Computer Assisted Surgery for Brain Shift Correction Using Biomechanical Models.

Authors:  Kay Sun; Thomas S Pheiffer; Amber L Simpson; Jared A Weis; Reid C Thompson; Michael I Miga
Journal:  IEEE J Transl Eng Health Med       Date:  2014-04-30       Impact factor: 3.316

9.  Fiber tractography based on diffusion tensor imaging compared with high-angular-resolution diffusion imaging with compressed sensing: initial experience.

Authors:  Daniela Kuhnt; Miriam H A Bauer; Jan Egger; Mirco Richter; Tina Kapur; Jens Sommer; Dorit Merhof; Christopher Nimsky
Journal:  Neurosurgery       Date:  2013-01       Impact factor: 4.654

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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