Dr. Prashanth Dumpuri Profile

Dr. Prashanth Dumpuri

at Vanderbilt Univ

SPIE Involvement:

Author

Publications (9)

Proceedings Article | 2 March 2011 Paper

The sparse data extrapolation problem: strategies for soft-tissue correction for image-guided liver surgery

Michael Miga, Prashanth Dumpuri, Amber Simpson, Jared Weis, William Jarnagin

Proceedings Volume 7964, 79640C (2011) https://doi.org/10.1117/12.878696

KEYWORDS: Liver, Surgery, Image-guided intervention, Natural surfaces, Image registration, Rigid registration, Data modeling, Data acquisition, Computed tomography, Laser range finders

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Proceedings Article | 3 March 2010 Paper

Model-updated image-guided liver surgery: preliminary results using intra-operative surface characterization

Prashanth Dumpuri, Logan Clements, Benoit Dawant, Michael Miga

Proceedings Volume 7625, 76251H (2010) https://doi.org/10.1117/12.844290

KEYWORDS: Liver, Surgery, Rigid registration, Image registration, Image-guided intervention, Laser range finders, Image segmentation, Detection and tracking algorithms, Computed tomography, Spatial filters

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Proceedings Article | 27 February 2010 Paper

Enhancement of subsurface brain shift model accuracy: a preliminary study

Ishita Garg, Siyi Ding, Aaron Coffey, Prashanth Dumpuri, Reid Thompson, Benoit Dawant, Michael Miga

Proceedings Volume 7625, 76250J (2010) https://doi.org/10.1117/12.845630

KEYWORDS: Brain, Data modeling, Tumors, Image segmentation, Tissues, Image registration, Neuroimaging, Magnetic resonance imaging, Surgery, Lawrencium

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Proceedings Article | 13 March 2009 Paper

Comparison of pre/post-operative CT image volumes to preoperative digitization of partial hepatectomies: a feasibility study in surgical validation

Prashanth Dumpuri, Logan Clements, Rui Li, Jonathan Waite, James Stefansic, David Geller, Michael Miga, Benoit Dawant

Proceedings Volume 7261, 726122 (2009) https://doi.org/10.1117/12.813771

KEYWORDS: Liver, Surgery, Computed tomography, Tumors, Image segmentation, Visualization, Tissues, Natural surfaces, Image-guided intervention, 3D image processing

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Proceedings Article | 13 March 2009 Paper

A dual compute resource strategy for computational model-assisted therapeutic interventions

Douglas Hackworth, Prashanth Dumpuri, Michael Miga

Proceedings Volume 7261, 72612R (2009) https://doi.org/10.1117/12.811922

KEYWORDS: Liver, Data modeling, Image registration, Computing systems, Surgery, Data processing, Image-guided intervention, Data acquisition, Process modeling, Data integration

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Acquiring and incorporating intraoperative data into image-guided surgical systems has been shown to increase the accuracy of these systems and the accuracy of image-guided surgical procedures. Even with the advent of powerful computers and parallel clusters, the ability to integrate highly resolved computer model information in the planning and execution of image-guided surgery is challenging. More often than not, the computational times required to process preoperative models and incorporate intraoperative data for feedback are too cumbersome and do not meet the real time constraints of surgery, for both planning and intraoperative guidance. To decrease the computational time for the surgeon and minimize the resources in the operating room, we have developed a dual compute node framework for image-guided surgical procedures: (i) a high-capability compute resource which acts as a server to facilitate preoperative planning, and (ii) a low-capability compute resource which acts as a server node/compute node to process the intraoperative data and rapidly integrate the model-based analysis for therapeutic/surgical feedback. In this framework, the preoperative planning utilities and intraoperative guidance system act as client-nodes/graphics-nodes that are assisted by the model-assistant. Processed data is transferred back to the graphics node for planning display or intraoperative feedback depending on which resource is engaged. In order to efficiently manage the data and the computational resources we also developed a novel software manager. This dual-capability resource compute node concept and the software manager are reported in this work, and the low-capability resource compute node is investigated within the context of image-guided liver surgery using data acquired during hepatic tumor resection therapies. Preliminary results indicate that the dual node concept can significantly decrease the computational resources and time required for image-guided surgical procedures.

Proceedings Article | 22 March 2007 Paper

Atlas-based method for model updating in image-guided liver surgery

Logan Clements, Prashanth Dumpuri, William Chapman, Robert Galloway, Michael Miga

Proceedings Volume 6509, 650917 (2007) https://doi.org/10.1117/12.710496

KEYWORDS: Liver, Data modeling, Finite element methods, Tissues, Lawrencium, Tumors, Surgery, Image-guided intervention, Computer simulations, Natural surfaces

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