A Novel Grid Architecture Integrating Real-Time Data and Intervention During Image Guided Therapy

1. Scheduling and On-demand Computing: • MCP scheduler submits the same job to multiple machines • As soon as one job starts MCP kills redundant jobs Timing Results of Current Parallel Finite Element Code 60.00 50.00 IBM Power3 40.00 Time (sec) 30.00 Itanium2 TeraGrid 20.00 IBM Power4 10.00 - 1 2 4 8 16 32 # of CPUs A Novel Grid Architecture Integrating Real-Time Data and Intervention During Image Guided Therapy PROBLEM:Neuro-surgeons seek to remove as much tumor tissue as possible while minimizing removal of healthy brain tissue Brain deforms during surgery Surgeons must align preoperative brain image with intra-operative images to provide surgeons the best opportunity for intra-surgical navigation Radiologists and neurosurgeons at Brigham and Women’s Hospital, Harvard Medical School transfer 30/40 MB brain images (generated during surgery) to SDSC and other HPC centers for simulation using the MCP scheduler NSF-ITR Grant 0427183 – SDSC, UCSD K. Baldridge, A. Majumdar, D. Choi, A. Birnbaum (SDSC), Petr Krysl (UCSD), A. Trivedi (gradstudent)NSF-ITR Grant 0426558 – Brigham and Women’s Hospital, Harvard S. K. Warfield, R. Kikinis, N. Archip (postdoc), S. Haker, neurosurgeons, radiologists Transmission repeated every hour during 6-8 hour surgery Transmission and FEM simulation must take on the order of minutes Data transfer :Globus-url-copy and SRB Data transfer :Globus-url-copy and SRB Brain shift ParallelFinite element simulation of biomechanical model for volumetric deformation performed on HPC machine; output results are sent to BWH where updated images are shown to surgeons during surgery

2. Progress Todate and Future Research Direction On-demand resource:Experiment on SDSC and NCSA TG clusterover 3 day period forrequest of 5 mins timefrom 2 to 64 CPUs. Jobwas terminated if it didn’t start in10 minsand next request processed. Total of 600jobs submitted; ~50 foreach bar on the plots. Results I: both clustersshow decreasing likeli-hood of success with increasing # of requests. Results II: relationshipbetween the size of requestand the length of queue delay Fusion of pre-op fMRI (green, red, yellow), pre-op MRI, and intra-op MRI; tumor is dark round region atthe lower right bottom Famuls FEM AMR deformation simulation using elastic solver; additional tetra-hedra, reducing error, in the interior • Additions to the ITR project • Initial small funding from IBM (I3) – innovation award • Grad student summer intern ( Prf. D. Gannon, Indiana U.) • Undergrad from Berkeley – REU summer scholarship • Multiple talks, SC demos, presentation, interest from other groups • Grid Infrastructure roadmap • Software to experiment with queue wait time, data transfer, network -flooding versus selective approach • security (patient privacy, encoding), data (meta-data, archive), visualization • NMI workflow integration • Parallel FEM roadmap • Viscoelastic, viscoplastic: constitutive equations • Implement parallel sparse solver; domain decomposition of Famuls FEM code • Accuracy at the MRI voxel level – orders of magnitude increase in data, compute, network • Dynamic multiscale image update ITR DDDAS Major cyberinfrastructure research: 100s of ORs and 10s of TFLOP centers