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Improving cancer surgery with real-time image processing

Published on August 23, 2017 by Aaron Dubrow

A Slicer module developed for treatment planning of Magnetic resonance imaging-guided laser interstitial thermal therapy (MRg-LITT) procedures. The interactive environment allows the surgeon to optimally place the trajectory of the catheter to kill the target lesion and minimize any damage to surrounding critical structures. [Courtesy: David Fuentes, University of Texas MD Anderson Cancer Center]

Cancer was first diagnosed 3,500 years ago in ancient Egypt. There, doctors removed tumors in a manner not dissimilar to what is done today: by surgically excising them from the body.

Surgery remains the most frequently used approach to treat cancer, but it is not without its complications. Modern tools, from sterile, stainless steel scalpels to MRI imaging, have led to fewer complications and far better outcomes than in ancient Egypt, but haven't fundamentally altered the risks. Removing too little of a tumor can lead to a relapse; too much — especially in a critical area like the brain — can harm a patient.

A pioneering research project that ran from 2005 to 2012 used advanced computing resources to improve the precision with which doctors perform cancer surgery.

Proton resonance frequency-based MR temperature imaging is used to monitor the thermal dose delivery during laser induced thermal therapy procedures. The nearly linear relationship between temperature and length of hydrogen bonds in water molecules is the underlying physical mechanism to detect temperature changes. [Courtesy: David Fuentes, University of Texas MD Anderson Cancer Center]

Researchers from the University of Texas at Austin, the University of Texas at San Antonio and the University of Texas MD Anderson Cancer Center — one of the nation's leading cancer research centers — developed a computer-driven, interactive system for planning, predicting and dynamically altering the course of a laser treatment for patients with cancer.

In 2008, after three years of research and development in algorithms, computer codes, imaging technology, and cyberinfrastructure, scientists used the Ranger supercomputer at the Texas Advanced Computing Center (TACC) to perform a minimally invasive laser treatment on a canine prostate without the intervention of a surgeon. The work was supported by a grant from the National Science Foundation's (NSF) Dynamic Driven Application Systems initiative.

At the heart of the technology was an adaptive-feedback control system based on mathematical and computational models that used Magnetic Resonance Temperature Imaging to determine the heat transferred by a laser to the tissue and the tissue's response.

The approach enabled the automated model to select the appropriate action, moment by moment, based on criteria determined in advance.

"It's a long process before these protocols are made robust and have wide-spread use in human subjects. But this is a step along a path that will be followed," J. Tinsley Oden, director of the Institute for Computation Engineering and Sciences at UT Austin, said at the time.

David Fuentes, Assistant Professor, Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX

David Fuentes, then a post-doctoral candidate at UT Austin and now a researcher and faculty member at MD Anderson, was also involved in the project.

With continuing support from NSF, Medtronic and others, Fuentes has continued to develop treatment protocols for laser surgery on the brain. NSF-funded supercomputers at TACC have enabled Fuentes to continue to simulate bioheat transfer in brain tissue and to calculate more accurate surgical outcomes.

Laser surgery on the brain involves many variables, including blood flow, optical properties, material properties, and thermal conductivity inside the body.

"The more data and images that can be acquired, the more confidence researchers and surgeons can have in planning surgical simulations," says Fuentes.

Working in collaboration with Rice University scientists, Fuentes and his collaborators are currently adapting the methods they developed for supercomputers into a portable system for operating rooms.

Their early results suggest that someday soon, doctors may compute the results of surgeries as they occur using algorithms first tested at TACC.

Supercomputers, in this way, serve as testbeds for future medical technologies and harbingers for next-generation treatments.


This feature is part of a TACC Special Report on Cancer. From patient-specific treatments to immunology to drug discovery, advanced computing accelerates basic and applied science. Learn more about how supercomputers are being used in the fight against cancer.

Read more Cancer Special Report Features


Story Highlights

From 2005 to 2012, researchers from UT Austin, the UT San Antonio and MD Anderson developed a computer-driven, interactive system for planning, predicting and dynamically altering the course of laser cancer surgery.

With continuing support from NSF, Medtronic and others, David Fuentes, one of the original researchers, has continued to develop treatment protocols for laser surgery on the brain.

Working in collaboration with Rice University scientists, Fuentes is adapting the methods he developed for supercomputers into a portable system for operating rooms.


Contact

Faith Singer-Villalobos

Communications Manager
faith@tacc.utexas.edu | 512-232-5771

Aaron Dubrow

Science And Technology Writer
aarondubrow@tacc.utexas.edu

Jorge Salazar

Technical Writer/Editor
jorge@tacc.utexas.edu | 512-475-9411