Slide 1 Image Guided Surgery. Slide 2 Conventional Surgery: Seeing surfaces Provided by Nakajima, Atsumi et al.

Slides:

Advertisements

Similar presentations

fMRI Methods Lecture1 - Introduction

Advertisements

Image Guided Therapy in Slicer3 Planning for Image Guided Neurosurgery Nobuhiko Hata, Ph.D. Slicer3 Training Compendium Neurosurgery Tutorial, N. Hata.

Image Guided Therapy in Slicer3

National Alliance for Medical Image Computing Diffusion Weighted MRI.

3D Slicer in Image Guided Therapy Nobuhiko Hata, PhD Brigham and Women’s Hospital.

Intraoperative Visualization of White Matter Tracts During Brain Tumor Resection Haytham Elhawary, Ph.D Postdoctoral Research Fellow March 19 th 2010.

Brain Scanning Techniques A look inside the Brain.

Function / ROI Viewing in Slicer2 and Slicer3 for fBIRN Data.

University of California, San Diego Structural Engineering SAN DIEGO SUPERCOMPUTER CENTER University of Zürich, Switzerland Image Guided Therapy Program.

TUD MedVis Overview

Fiber Tracking Techniques in Magnetic Resonance Diffusion Tensor Imaging Grace Michaels CSUN, Computer Science Junior.

University of California, San Diego San Diego Supercomputer Center Computational Radiology Laboratory Brigham & Women’s Hospital, Harvard Medical School.

3D Rigid/Nonrigid RegistrationRegistration 1)Known features, correspondences, transformation model – feature basedfeature based 2)Specific motion type,

Computer Assisted Knee Replacement Surgery By (insert surgeon name)

Slicer IGT and Open IGT Link

Surgical Planning Laboratory Brigham and Women’s Hospital Boston, Massachusetts USA a teaching affiliate of Harvard Medical School The AMIGO Project: An.

Computational Radiology Laboratory Harvard Medical School Brigham and Women’s Hospital Children’s Hospital Boston Massachusetts.

Computational Radiology Laboratory Harvard Medical School Children’s Hospital Department of Radiology Boston Massachusetts A Survey.

Surgical Planning Laboratory Brigham and Women’s Hospital Boston, Massachusetts USA a teaching affiliate of Harvard Medical School Visit of Barbara Alving,

Research Methods.

NA-MIC National Alliance for Medical Image Computing Slicer for Neurosurgical Planning IBMSPS th Annual World Congress for Brain.

Collaborations and Architectures mBIRN Progress at BWH.

Computer Assisted Surgery and Medical Image Analysis Computer Vision Group Artificial Intelligence Laboratory Massachusetts Institute of Technology Surgical.

NA-MIC National Alliance for Medical Image Computing Algorithms MIT PI: Polina Golland.

Computer-Assisted Knee Replacement Surgery. Knee Replacement Surgery Arthritic surfaces on the tibia and femur are removed. Arthritic surfaces on the.

Surgical Planning Laboratory Brigham and Women’s Hospital Boston, Massachusetts USA a teaching affiliate of Harvard Medical School Neurosurgery Alexandra.

SLICER: Initial Experience at Dartmouth Tara McHugh, M.A. Robert Roth, Ph.D. Brain Imaging Laboratory Dartmouth Medical School / DHMC NA-MIC National Alliance.

Workflow based Slicer IGT Module : Tutorial Andinet Enquobahrie, PhD Kitware Inc December, 2008.

NA-MIC National Alliance for Medical Image Computing Harvard Psychiatry Neuroimaging Laboratory Site PI: Martha E. Shenton, Ph.D....

NA-MIC National Alliance for Medical Image Computing NA-MIC Ron Kikinis, M.D., Professor of Radiology, Harvard Medical School, Director,

Surgical Planning Laboratory Brigham and Women’s Hospital Boston, Massachusetts USA a teaching affiliate of Harvard Medical School Overview Ron Kikinis,

Image Guided Therapy in Slicer3 Planning for Image Guided Neurosurgery Nobuhiko Hata, PhD Slicer3 Training Compendium Neurosurgery Tutorial, N. Hata.

Medical Image Analysis Image Registration Figures come from the textbook: Medical Image Analysis, by Atam P. Dhawan, IEEE Press, 2003.

Surgical Planning Laboratory Brigham and Women’s Hospital Boston, Massachusetts USA a teaching affiliate of Harvard Medical School Functional Data Analysis.

NA-MIC National Alliance for Medical Image Computing NAMIC UNC Site Update Site PI: Martin Styner Site NAMIC folks: Clement Vachet, Gwendoline.

NA-MIC National Alliance for Medical Image Computing National Alliance for Medical Image Computing: NAMIC Ron Kikinis, M.D.

Spring Meeting 2006 Tensor Atlas Morphometry BIRN Analysis, Visualization, and Interpretation.

Image Guided Therapy in Slicer3 Planning for Image Guided Neurosurgery Nobuhiko Hata, Ph.D. Slicer3 Training Compendium Neurosurgery Tutorial, N. Hata.

©2005 Surgical Planning Laboratory, ARR Slide 1 Prostate Image Processing Steven Haker, PhD.

Diffusion Tensor Analysis in Slicer3

NA-MIC National Alliance for Medical Image Computing NAMIC UNC Site Update Site PI: Martin Styner UNC Site NAMIC folks: C Vachet, G Roger,

Interventional CVMRI at Sunnybrook Graham Wright Sunnybrook Research Institute / University of Toronto University of Toronto.

NA-MIC National Alliance for Medical Image Computing Velocardiofacial Syndrome as a Genetic Model for Schizophrenia Marek Kubicki DBP2,

Triedman Patient-Specific Modeling of Defibrillation in Children Collaborators: Children’s Hospital Boston: John Triedman, Assoc. Prof. Pediatrics Matt.

Reformatting registration Fiducials segmentation registration dataset1 dataset2 dataset1 dataset2 transformation reformatted dataset transformation reformatted.

NA-MIC National Alliance for Medical Image Computing UNC/Utah-II Core 1 Guido Gerig, Casey Goodlett, Marcel Prastawa, Sylvain Gouttard.

NA-MIC National Alliance for Medical Image Computing Velocardiofacial Syndrome as a Genetic Model for Schizophrenia Marek Kubicki DBP2,

NA-MIC National Alliance for Medical Image Computing fMRI within NAMIC Sandy Wells, Polina Golland Discussion moderator: Andy Saykin.

XIX Symposium Neuroradiologicum. Neeraj Chepuri, M.D. Neuro Radiologist Consulting Radiologists, Ltd. Abbott Northwestern Hospital Minneapolis, Minnesota,

Slicer IGT : Workflow based design Andinet Enquobahrie, PhD Kitware Inc.

Detection of Spatial Connectivity via fMRI Data Analysis Checkpoint Presentation Emily C. Koch Ramesh M. Singa Dr. John Hart, Jr. (advisor) Computer Integrated.

National Alliance for Medical Image Computing Volumetric Studies.

© NIH National Center for Image-Guided Therapy, June 2008 Slicer IGT Nobuhiko Hata, PhD Sandy Wells, PhD Computation Core, NCIGT.

The past and future of virtual reality simulation in neurologic surgery Longwei F

©2005 Surgical Planning Laboratory, ARR Slide 1 AMIGO: Advanced Multimodality Image-Guided Operating room Randy Ellis, Ph.D.

Slicer 101 IGT Edition Haiying Liu, M.S. Nobuhiko Hata, Ph.D.

An Open Source Platform for Registration, Segmentation, Quantitative Analysis, and Visualization of Biomedical Image Data 3D Slicer About 3D Slicer Segmentation.

Visualization for astronomy, via medical imaging Michael Halle Initiative in Innovative Computing at Harvard & Surgical Planning Lab, Brigham and Women’s.

Polina Golland Core 1, MIT

RSNA2012 Quantitative Imaging Reading Room

Slicer IGT Module : Wizard UI Design

Ron Kikinis, MD Professor of Radiology, Harvard Medical School

Computer Assisted Surgery

Physics-based simulation for visual computing applications

Joseph I. Friedman et al. JIMG 2014;7:

Utah Algorithms Progress and Future Work

Conformal (Geodesic) Active Contours

Registration Foundations

Fig. 3. Demonstration of planning of surgical approach via preoperative DTI. Coronal MRI (A) and DTI (B) show left thalamic tumor with anteromedially displaced.

resting state fMRI scan ( 8min 10sec )

Presentation transcript:

Slide 1 Image Guided Surgery

Slide 2 Conventional Surgery: Seeing surfaces Provided by Nakajima, Atsumi et al.

Slide 3 Computer Assisted Surgery: seeing through surfaces

Slide 4 Goal: Assist Surgeons Surgical Planning & Simulation –Maximize Tumor Removal –Minimize Damage to Critical Structures Intraoperative Visualizations via 3D Slicer

Slide 5 Preoperative Image Information High resolution structural MRI SPECT images –perfusion –metabolism MR Angiography, vessel models Diffusion Tensor MRI fMRI Electro-Cortical Stimulation

Slide 6 Pre-Operative Image Processing Construct 3D Models –Semi-Automated Segmentation –DTMRI Tract Tracing Register all pre-operative data

Slide 7 Integrated Preoperative Data F. Talos

Slide 8 Patient-specific models Gering_fmri

Slide 9

Slide 10 Segmentation of Neural Structures

Slide 11 DT-MRI Tractography H.J. Park, M.E. Shenton, C.-F. Westin

Slide 12 Preoperative fMRI, Motor Experiment F. Talos

Slide 13 Oligodendroglioma – DT-Tractography + fMRI F. Talos

Slide 14 Intraoperative Image Processing Acquire one or more volumetric (interventional) MRI (iMRI) images Determine non-rigid registration of Pre- and Intra-operative data

Slide 15 Construct Intraoperative Visualization transmit image data and 3D models thru volumetric deformation integrate with iMRI images and models display with 3D Slicer LCD screen in front of surgeon in iMRI –coordinate visualization with intraoperative instruments

Slide 16 fMRI Projected into Intraoperative Brain Configuration Provided by Alida Tei

Slide 17 fMRI and DTI Info Projected into Intraoperative Brain Configuration Provided by Alida Tei

Slide 18 3D Slicer: tool for Visualization Registration Segmentation Measurements Realtime Integration Provided by D. Gering

Slide 19 3D Slicer Demo...

Slide 20 3D Slicer Demo...

Slide 21 3D Slicer Demo...

Slide 22 3D Slicer Demo...

Slide 23 3D Slicer Demo...