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Non-rigid point set registration of curves: registration of the superficial vessel centerlines of the brain
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Royal Institute of Technology (KTH), School of Technology and Health, Huddinge, Sweden.ORCID iD: 0000-0002-0442-3524
Östergötlands Läns Landsting, Anaesthetics, Operations and Specialty Surgery Center, Department of Neurosurgery.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Royal Institute of Technology (KTH), School of Technology and Health, Huddinge, Sweden.ORCID iD: 0000-0002-7750-1917
2016 (English)In: Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE - International Society for Optical Engineering, 2016, Vol. 9786, 8 p.978611-1-978611-8 p.Conference paper (Refereed)
Abstract [en]

In this study we present a non-rigid point set registration for 3D curves (composed by 3D set of points). Themethod was evaluated in the task of registration of 3D superficial vessels of the brain where it was used to matchvessel centerline points. It consists of a combination of the Coherent Point Drift (CPD) and the Thin-PlateSpline (TPS) semilandmarks. The CPD is used to perform the initial matching of centerline 3D points, whilethe semilandmark method iteratively relaxes/slides the points.

For the evaluation, a Magnetic Resonance Angiography (MRA) dataset was used. Deformations were appliedto the extracted vessels centerlines to simulate brain bulging and sinking, using a TPS deformation where afew control points were manipulated to obtain the desired transformation (T1). Once the correspondences areknown, the corresponding points are used to define a new TPS deformation(T2). The errors are measured in thedeformed space, by transforming the original points using T1 and T2 and measuring the distance between them.To simulate cases where the deformed vessel data is incomplete, parts of the reference vessels were cut and thendeformed. Furthermore, anisotropic normally distributed noise was added.

The results show that the error estimates (root mean square error and mean error) are below 1 mm, even inthe presence of noise and incomplete data.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2016. Vol. 9786, 8 p.978611-1-978611-8 p.
Series
, Progress in Biomedical Optics, ISSN 1605-7422 ; 9786
Keyword [en]
Non-rigid registration, brain shift correction, vessel registration
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-126347DOI: 10.1117/12.2208421ISI: 000382315800036ISBN: 978-1-5106-0021-8OAI: oai:DiVA.org:liu-126347DiVA: diva2:913783
Conference
Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, California, United States, February 27, 2016
Projects
ARIOR
Funder
Swedish Childhood Cancer Foundation, MT2013-0036
Available from: 2016-03-22 Created: 2016-03-22 Last updated: 2016-09-27Bibliographically approved
In thesis
1. Guidance and Visualization for Brain Tumor Surgery
Open this publication in new window or tab >>Guidance and Visualization for Brain Tumor Surgery
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Image guidance and visualization play an important role in modern surgery to help surgeons perform their surgical procedures. Here, the focus is on neurosurgery applications, in particular brain tumor surgery where a craniotomy (opening of the skull) is performed to access directly the brain region to be treated. In this type of surgery, once the skull is opened the brain can change its shape, and this deformation is known as brain shift. Moreover, the boundaries of many types of tumors are difficult to identify by the naked eye from healthy tissue. The main goal of this work was to study and develop image guidance and visualization methods for tumor surgery in order to overcome the problems faced in this type of surgery.

Due to brain shift the magnetic resonance dataset acquired before the operation (preoperatively) no longer corresponds to the anatomy of the patient during the operation (intraoperatively). For this reason, in this work methods were studied and developed to compensate for this deformation. To guide the deformation methods, information of the superficial vessel centerlines of the brain was used. A method for accurate (approximately 1 mm) reconstruction of the vessel centerlines using a multiview camera system was developed. It uses geometrical constraints, relaxation labeling, thin plate spline filtering and finally mean shift to find the correct correspondences between the camera images.

A complete non-rigid deformation pipeline was initially proposed and evaluated with an animal model. From these experiments it was observed that although the traditional non-rigid registration methods (in our case coherent point drift) were able to produce satisfactory vessel correspondences between preoperative and intraoperative vessels, in some specific areas the results were suboptimal. For this reason a new method was proposed that combined the coherent point drift and thin plate spline semilandmarks. This combination resulted in an accurate (below 1 mm) non-rigid registration method, evaluated with simulated data where artificial deformations were performed.

Besides the non-rigid registration methods, a new rigid registration method to obtain the rigid transformation between the magnetic resonance dataset and the neuronavigation coordinate systems was also developed.

Once the rigid transformation and the vessel correspondences are known, the thin plate spline can be used to perform the brain shift deformation. To do so, we have used two approaches: a direct and an indirect. With the direct approach, an image is created that represents the deformed data, and with the indirect approach, a new volume is first constructed and only after that can the deformed image be created. A comparison of these two approaches, implemented for the graphics processing units, in terms of performance and image quality, was performed. The indirect method was superior in terms of performance if the sampling along the ray is high, in comparison to the voxel grid, while the direct was superior otherwise. The image quality analysis seemed to indicate that the direct method is superior.

Furthermore, visualization studies were performed to understand how different rendering methods and parameters influence the perception of the spatial position of enclosed objects (typical situation of a tumor enclosed in the brain). To test these methods a new single-monitor-mirror stereoscopic display was constructed. Using this display, stereo images simulating a tumor inside the brain were presented to the users with two rendering methods (illustrative rendering and simple alpha blending) and different levels of opacity. For the simple alpha blending method an optimal opacity level was found, while for the illustrative rendering method all the opacity levels used seemed to perform similarly.

In conclusion, this work developed and evaluated 3D reconstruction, registration (rigid and non-rigid) and deformation methods with the purpose of minimizing the brain shift problem. Stereoscopic perception of the spatial position of enclosed objects was also studied using different rendering methods and parameter values.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. 70 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1762
National Category
Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging Surgery Computer Engineering
Identifiers
urn:nbn:se:liu:diva-130791 (URN)10.3384/diss.diva-130791 (DOI)9789176857724 (Print) (ISBN)
Public defence
2016-09-30, Hugo Theorellsallen (norra entrén), Campus US, Linköping, 09:00 (English)
Opponent
Supervisors
Funder
Swedish Foundation for Strategic Research Knowledge FoundationVINNOVAVårdal Foundation, 2009/0079Swedish Childhood Cancer Foundation, MT2013-0036
Available from: 2016-08-24 Created: 2016-08-24 Last updated: 2016-09-09Bibliographically approved

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Maria Marreiros, Filipe MiguelWang, ChunliangRossitti, SandroSmedby, Örjan
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