A Framework for Scalable Biophysics-Based Image Analysis
SessionMultiphysics
Event Type
Paper
Applications
Scientific Computing
TimeTuesday, November 14th3:30pm -
4pm
Location301-302-303
DescriptionWe present SIBIA (Scalable Integrated Biophysics-based
Image Analysis), a framework for coupling biophysical
models with medical image analysis. It provides solvers
for an image-driven inverse brain tumor growth model and
an image registration problem, the combination of which
can eventually help in diagnosis and prognosis of brain
tumors. The two main computational kernels of SIBIA are
a Fast Fourier Transformation (FFT) implemented in the
library AccFFT to discretize differential operators, and
a cubic interpolation kernel for semi-Lagrangian based
advection. We present efficiency and scalability results
for the computational kernels, the inverse tumor solver
and image registration on two x86 systems. We showcase
results that demonstrate that our solver can be used to
solve registration problems of unprecedented scale,
4096^3 resulting in 200 billion unknowns---a problem
size that is 64x larger than the state-of-the-art. For
problem sizes of clinical interest, SIBIA is about 8x
faster than the state-of-the-art.
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