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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zigbcra6rjdivda6lkzknwuo5q" style="color: black;">ACM/IEEE SC 2000 Conference (SC'00)</a>
We aimed to study the performance of a parallel implementation of an intraoperative nonrigid registration algorithm that accurately simulates the biomechanical properties of the brain and its deformations during surgery. The algorithm was designed to allow for improved surgical navigation and quantitative monitoring of treatment progress in order to improve the surgical outcome and to reduce the time required in the operating room. We have applied the algorithm to two neurosurgery cases with<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/sc.2000.10043">doi:10.1109/sc.2000.10043</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sc/WarfieldFGNJK00.html">dblp:conf/sc/WarfieldFGNJK00</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/agsxmvjxbfb3vnnv7fdpoddnjm">fatcat:agsxmvjxbfb3vnnv7fdpoddnjm</a> </span>
more »... mising results. High performance computing is a key enabling technology that allows the biomechanical simulation to be executed quickly enough for the algorithm to be practical. Our parallel implementation was evaluated on a symmetric multi-processor and two clusters and exhibited similar performance characteristics on each. The implementation was sufficiently fast to be used in the operating room during a neurosurgery procedure. It allowed a three-dimensional volumetric deformation to be simulated in less than ten seconds. 1
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