Development of a New Image-Guided Neuronavigation System: Mixed-Reality Projection Mapping Is Accurate and Feasible

2021 ◽  
Author(s):  
Tsukasa Koike ◽  
Taichi Kin ◽  
Shota Tanaka ◽  
Katsuya Sato ◽  
Tatsuya Uchida ◽  
...  
Keyword(s):  
Image Processing ◽  
Mixed Reality ◽  
Improve Patient Safety ◽  
Projection Mapping ◽  
Normalized Mutual Information ◽  
Image Guided ◽  
Brain Surface ◽  
Initial Surgery ◽  
Position And Orientation ◽  
The Brain

Abstract BACKGROUND Image-guided systems improve the safety, functional outcome, and overall survival of neurosurgery but require extensive equipment. OBJECTIVE To develop an image-guided surgery system that combines the brain surface photographic texture (BSP-T) captured during surgery with 3-dimensional computer graphics (3DCG) using projection mapping. METHODS Patients who underwent initial surgery with brain tumors were prospectively enrolled. The texture of the 3DCG (3DCG-T) was obtained from 3DCG under similar conditions as those when capturing the brain surface photographs. The position and orientation at the time of 3DCG-T acquisition were used as the reference. The correct position and orientation of the BSP-T were obtained by aligning the BSP-T with the 3DCG-T using normalized mutual information. The BSP-T was combined with and displayed on the 3DCG using projection mapping. This mixed-reality projection mapping (MRPM) was used prospectively in 15 patients (mean age 46.6 yr, 6 males). The difference between the centerlines of surface blood vessels on the BSP-T and 3DCG constituted the target registration error (TRE) and was measured in 16 fields of the craniotomy area. We also measured the time required for image processing. RESULTS The TRE was measured at 158 locations in the 15 patients, with an average of 1.19 ± 0.14 mm (mean ± standard error). The average image processing time was 16.58 min. CONCLUSION Our MRPM method does not require extensive equipment while presenting information of patients’ anatomy together with medical images in the same coordinate system. It has the potential to improve patient safety.

Computerized brain-surface voltage topographic mapping for localization of intracranial spikes from electrocorticography

2001 ◽  
Vol 94 (6) ◽  
pp. 1005-1009 ◽  
Author(s):  
Hiroshi Otsubo ◽  
Atsushi Shirasawa ◽  
Shiro Chitoku ◽  
James T. Rutka ◽  
Scott B. Wilson ◽  
...  
Keyword(s):  
High Amplitude ◽  
Topographic Mapping ◽  
Seizure Onset ◽  
Mapping Procedure ◽  
Content Type ◽  
Brain Surface ◽  
Initial Surgery ◽  
Interictal Discharges ◽  
The Brain ◽  
Source Of Information

✓ The purpose of this paper is to describe the use of computerized brain-surface voltage topographic mapping to localize and identify epileptic discharges recorded on electrocorticographic (ECoG) studies in which a subdural grid was used during intracranial video electroencephalographic (IVEEG) monitoring. The authors studied 12 children who underwent surgery for intractable extrahippocampal epilepsy. Cortical surfaces and subdural grid electrodes were photographed during the initial surgery to create an electrode map that could be superimposed onto a picture of the brain surface. Spikes were selected from ictal discharges recorded at the beginning of clinically confirmed seizures and from interictal discharges seen on ECoG studies during IVEEG recording. A computer program was used to calculate the sequential amplitude of the spikes by using squared interpolation, and they were then superimposed onto the electrode map. Interictal discharges and high-amplitude spike complexes at seizure onset were plotted on the map. This mapping procedure depicted the ictal zone in nine patients and the interictal zone in 12, and proved to be an accurate and useful source of information for planning corrective surgery.

scholarly journals Commentary: Development of a New Image-Guided Neuronavigation System: Mixed-Reality Projection Mapping Is Accurate and Feasible

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
David J. Cote ◽  
Jacob Ruzevick ◽  
Ben A. Strickland ◽  
Gabriel Zada
Keyword(s):  
Mixed Reality ◽  
Projection Mapping ◽  
Image Guided ◽  
Neuronavigation System

scholarly journals Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems

2021 ◽  
Vol 2 ◽  
Author(s):  
Fotis Drakopoulos ◽  
Christos Tsolakis ◽  
Angelos Angelopoulos ◽  
Yixun Liu ◽  
Chengjun Yao ◽  
...  
Keyword(s):  
Mixed Reality ◽  
Registration Accuracy ◽  
Registration Method ◽  
3D Slicer ◽  
B Spline ◽  
Rigid Registration ◽  
Image Guided ◽  
Preoperative Image ◽  
And Performance ◽  
The Brain

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT.Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon.Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in <2 min, achieving desirable speed for use in a clinical setting.Conclusions: The A-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room. A-PBNRR, coupled with the mixed reality system, presents a powerful and affordable solution compared to current neuronavigation systems.

Analytical Electron Microscopy (AEM) of Meningeal Cells Exposed to Particulate Cobalt During Studies of Experimental Epilepsy

1982 ◽  
Vol 40 ◽  
pp. 186-187
Author(s):  
R.G. Frederickson ◽  
R.G. Ulrich ◽  
J.L. Culberson
Keyword(s):  
Electron Microscopy ◽  
Analytical Electron Microscopy ◽  
Experimental Epilepsy ◽  
Metallic Cobalt ◽  
Experimental Animals ◽  
X Ray ◽  
Brain Surface ◽  
Meningeal Cells ◽  
Analytical Electron ◽  
The Brain

Metallic cobalt acts as an epileptogenic agent when placed on the brain surface of some experimental animals. The mechanism by which this substance produces abnormal neuronal discharge is unknown. One potentially useful approach to this problem is to study the cellular and extracellular distribution of elemental cobalt in the meninges and adjacent cerebral cortex. Since it is possible to demonstrate the morphological localization and distribution of heavy metals, such as cobalt, by correlative x-ray analysis and electron microscopy (i.e., by AEM), we are using AEM to locate and identify elemental cobalt in phagocytic meningeal cells of young 80-day postnatal opossums following a subdural injection of cobalt particles.

A new technique for the mapping of oxygen tension on the brain surface

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S543-S543
Author(s):  
Satoshi Kimura ◽  
Keigo Matsumoto ◽  
Yoshio Imahori ◽  
Katsuyoshi Mineura ◽  
Toshiyuki Itoh
Keyword(s):  
Oxygen Tension ◽  
New Technique ◽  
Brain Surface ◽  
A New Technique ◽  
The Brain

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

Image-Guided Spinal Surgery and Robotics in MIS: Where Are We Now?

2018 ◽  
Vol 1 (2) ◽  
pp. 2
Author(s):  
Chiung Chyi Shen
Keyword(s):  
Pedicle Screw ◽  
Spinal Surgery ◽  
Surgical Navigation ◽  
Improve Patient Safety ◽  
Intraoperative Imaging ◽  
Spinal Instrumentation ◽  
Pedicle Screws ◽  
Computer Assisted ◽  
Navigation Systems ◽  
Image Guided

Use of pedicle screws is widespread in spinal surgery for degenerative, traumatic, and oncological diseases. The conventional technique is based on the recognition of anatomic landmarks, preparation and palpation of cortices of the pedicle under control of an intraoperative C-arm (iC-arm) fluoroscopy. With these conventional methods, the median pedicle screw accuracy ranges from 86.7% to 93.8%, even if perforation rates range from 21.1% to 39.8%.The development of novel intraoperative navigational techniques, commonly referred to as image-guided surgery (IGS), provide simultaneous and multiplanar views of spinal anatomy. IGS technology can increase the accuracy of spinal instrumentation procedures and improve patient safety. These systems, such as fluoroscopy-based image guidance ("virtual fluoroscopy") and computed tomography (CT)-based computer-guidance systems, have sensibly minimized risk of pedicle screw misplacement, with overall perforation rates ranging from between 14.3% and 9.3%, respectively."Virtual fluoroscopy" allows simultaneous two-dimensional (2D) guidance in multiple planes, but does not provide any axial images; quality of images is directly dependent on the resolution of the acquired fluoroscopic projections. Furthermore, computer-assisted surgical navigation systems decrease the reliance on intraoperative imaging, thus reducing the use of intraprocedure ionizing radiation. The major limitation of this technique is related to the variation of the position of the patient from the preoperative CT scan, usually obtained before surgery in a supine position, and the operative position (prone). The next technological evolution is the use of an intraoperative CT (iCT) scan, which would allow us to solve the position-dependent changes, granting a higher accuracy in the navigation system. 

Frontier concept of rabi chip for intelligent humanoid

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Preecha Yupapin ◽  
Amiri I. S. ◽  
Ali J. ◽  
Ponsuwancharoen N. ◽  
Youplao P.
Keyword(s):  
Human Brain ◽  
Brain Function ◽  
Rabi Oscillation ◽  
Liquid Core ◽  
Brain Surface ◽  
Dipole Oscillation ◽  
On Chip ◽  
The Brain ◽  
Robotic Applications ◽  
Atom System

The sequence of the human brain can be configured by the originated strongly coupling fields to a pair of the ionic substances(bio-cells) within the microtubules. From which the dipole oscillation begins and transports by the strong trapped force, which is known as a tweezer. The tweezers are the trapped polaritons, which are the electrical charges with information. They will be collected on the brain surface and transport via the liquid core guide wave, which is the mixture of blood content and water. The oscillation frequency is called the Rabi frequency, is formed by the two-level atom system. Our aim will manipulate the Rabi oscillation by an on-chip device, where the quantum outputs may help to form the realistic human brain function for humanoid robotic applications.

scholarly journals CNN Training Using 3D Virtual Models for Assisted Assembly with Mixed Reality and Collaborative Robots

2021 ◽  
Vol 11 (9) ◽  
pp. 4269
Author(s):  
Kamil Židek ◽  
Ján Piteľ ◽  
Michal Balog ◽  
Alexander Hošovský ◽  
Vratislav Hladký ◽  
...  
Keyword(s):  
Mixed Reality ◽  
Current Trend ◽  
Robot Vision ◽  
Processing Unit ◽  
Single Shot ◽  
Vision Systems ◽  
Collaborative Robots ◽  
Training Time ◽  
Position And Orientation ◽  
Collaborative Robot

The assisted assembly of customized products supported by collaborative robots combined with mixed reality devices is the current trend in the Industry 4.0 concept. This article introduces an experimental work cell with the implementation of the assisted assembly process for customized cam switches as a case study. The research is aimed to design a methodology for this complex task with full digitalization and transformation data to digital twin models from all vision systems. Recognition of position and orientation of assembled parts during manual assembly are marked and checked by convolutional neural network (CNN) model. Training of CNN was based on a new approach using virtual training samples with single shot detection and instance segmentation. The trained CNN model was transferred to an embedded artificial processing unit with a high-resolution camera sensor. The embedded device redistributes data with parts detected position and orientation into mixed reality devices and collaborative robot. This approach to assisted assembly using mixed reality, collaborative robot, vision systems, and CNN models can significantly decrease assembly and training time in real production.

scholarly journals Fast Nonsupervised 3D Registration of PET and MR Images of the Brain

1994 ◽  
Vol 14 (5) ◽  
pp. 749-762 ◽  
Author(s):  
Jean-François Mangin ◽  
Vincent Frouin ◽  
Isabelle Bloch ◽  
Bernard Bendriem ◽  
Jaime Lopez-Krahe
Keyword(s):  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Surface Matching ◽  
3D Registration ◽  
Matching Algorithm ◽  
Distance Map ◽  
Brain Surface ◽  
Discrete Surfaces ◽  
Positron Emission ◽  
The Brain

We propose a fully nonsupervised methodology dedicated to the fast registration of positron emission tomography (PET) and magnetic resonance images of the brain. First, discrete representations of the surfaces of interest (head or brain surface) are automatically extracted from both images. Then, a shape-independent surface-matching algorithm gives a rigid body transformation, which allows the transfer of information between both modalities. A three-dimensional (3D) extension of the chamfer-matching principle makes up the core of this surface-matching algorithm. The optimal transformation is inferred from the minimization of a quadratic generalized distance between discrete surfaces, taking into account between-modality differences in the localization of the segmented surfaces. The minimization process is efficiently performed via the precomputation of a 3D distance map. Validation studies using a dedicated brain-shaped phantom have shown that the maximum registration error was of the order of the PET pixel size (2 mm) for the wide variety of tested configurations. The software is routinely used today in a clinical context by the physicians of the Service Hospitalier Frédéric Joliot (>150 registrations performed). The entire registration process requires ∼5 min on a conventional workstation.

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