A Dynamic Data Driven Grid System for Intra-operative Image Guided Neurosurgery

Transcription

1 A Dynamic Data Driven Grid System for Intra-operative Image Guided Neurosurgery A Majumdar 1, A Birnbaum 1, D Choi 1, A Trivedi 2, S. K. Warfield 3, K. Baldridge 1, and Petr Krysl 2 1 S & 2 Structural Engineering Dept University of California San Diego 3 Computational Radiology Lab Brigham and Women s Hospital Harvard Medical School Grants: NSF: ITR , ; NIH:P41 RR13218, P01 CA67165, LM , I3 grant (IBM)

2 Neurosurgery Challenge Challenges : Remove as much tumor tissue as possible Minimize the removal of healthy tissue Avoid the disruption of critical anatomical structures Know when to stop the resection process Compounded by the intra-operative brain deformation as a result of the surgical process Important to quantify and correct for these deformations while surgery is in progress Real-time constraints provide images ~once/hour within few mins during surgery lasting 6 to 8 hours

3 Intraoperative MRI Scanner at BWH (0.5 T)

4 Brain Deformation Before surgery After surgery

5 Overall Process Before image guided neurosurgery Preoperative Data Acquisition Segmentation and Visualization During image guided neurosurgery Preoperative Planning of Surgical Trajectory Preoperative data Intraoperative MRI Segmentation Registration Surface matching Solve biomechanical Model for volumetric deformation Visualization Surgical process

6 Timing During Surgery Time (min) Before surgery Preop segmentation During surgery Intraop MRI Segmentation Registration Surface displacement Biomechanical simulation Visualization Surgical progress

7 Current Prototype DDDAS Inside Hospital Pre and Intra-op 3D MRI (once/hr) Intra-op surgical decision and steer Once every hour or two for a 6 or 8 hour surgery Segmentation, Registration, Surface Matching for BC Merge pre and intra-op viz Local computer at BWH Crude linear elastic FEM solution

8 Two Research Aspects Grid Architecture grid scheduling, on demand remote access to multi-teraflop machines, data transfer/sharing Development of detailed advanced non-linear scalable viscoelastic biomechanical model

9 Intra-op MRI with pre-op fmri

10 Queue Delay Experiment on TeraGrid Clusters TeraGrid is a NSF funded grid infrastructure across multiple research and academic sites Queue delays at SDSC and NCSA TG were measured over 3 days for 5 mins wall clock time on 2 to 64 CPUs Single job submitted at a time If job didn t start within 10 mins, job terminated, next one processed What is the likelihood of job running 313 jobs to NCSA TG cluster and 332 to SDSC TG cluster 50 to 56 jobs of each size on each cluster

11 TeraGrid Experiment Results % of submitted tasks that run as a function of CPUs requested Average queue delay for tasks that began running within10 mins

12 Data Transfer We are investigating grid based data transfer mechanisms such as globus-url-copy, SRB All hospitals have firewalls for security and patient data privacy single port of entry to internal machines Transfer direction Globus-urlcopy SRB Scp Scp C TG to BWH BWH to TG Transfer time in seconds for 20 MB file

13 Mesh Model with Brain Segmentation

14 Current and New Biomechanical Models Current linear elastic material model RTBM Advanced biomechanical model FAMULS (AMR) Advanced model is based on conforming adaptive refinement method Inspired by the theory of wavelets this refinement produces globally compatible meshes by construction Replicate the linear elastic result produced by RTBM using FAMULS

15 FEM Mesh : FAMULS & RTBM FAMULS (AMR) RTBM (Uniform)

16 Deformation Simulation After Cut No AMR FAMULS 3 level AMR FAMULS RTBM

17 Advanced Biomechanical Model The current solver is based on small strain isotropic elastic principle New biomechanical model Inhomogeneous scalable non-linear viscoelastic model with AMR Increase resolution close to the level of MRI voxels i.e. millions of FEM meshes New high resolution complex model still has to meet the real time constraint of neurosurgery Requires fast access to remote multi-tflop systems

18 Parallel Registration Performance 3000 Elapsed Time (sec) patient1 patient # of CPUs

19 Parallel Rendering Performance

20 Parallel RTBM Performance (43584 meshes, tetrahedral elements) Elapsed Time (sec) IBM Power3 IA64 TeraGrid IBM Power # of CPUs

21 End to End (BWH SDSC BWH) Timing RTBM not during surgery Rendering - during Surgery

22 End-to-end Timing of RTBM Timing of transferring ~20 MB files from BWH to SDSC, running simulations on 16 nodes (32 procs), transferring files back to BWH = 9 + (60 + 7) + 50 = 124 sec. Capable of providing biomechanical brain deformation simulation results (using the linear elastic model) to the surgery room at BWH within ~2 mins using TG machines at SDSC

23 End-to-end Timing of Rendering DURING SURGERY Intra-op MRI data sent from BWH to SDSC during a surgery, parallel rendering performed at SDSC, rendered viz sent back to BWH (but not shown to surgeons) Total time (for two sets of data) = 2*53 + 2* = sec

24 Current and Future DDDAS Research Continuing research and development in grid architecture, on demand computing, data transfer Continuing development of advanced biomechanical model and parallel algorithm Future DDDAS - near-continuous instead of once an hour 3-D MRI based Scanner at BWH can provide one 2-D slice every 3 sec or three orthogonal 2-D slices every 6 sec Near-continuous DDDAS architecture Requires major research, development and implementation work in the biomechanical application domain Requires research in the closed loop system of dynamic image driven continuous biomechanical simulation and 3-D volumetric FEM results based surgical navigation and steering