Paper Info
Title
A framework for scalable biophysics-based image analysis
Abstract
We 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, Lonestar 5 at the Texas Advanced Computing Center and Hazel Hen at the Stuttgart High Performance Computing Center. We showcase results that demonstrate that our solver can be used to solve registration problems of unprecedented scale, 40963 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.
Year
DOI
Venue
2017
10.1145/3126908.3126930
SC
Keywords
Field
DocType
Bio-Physics Based Image Analysis,Scalable Image Registration
Kernel (linear algebra),Discretization,Spline interpolation,Supercomputer,Computer science,Biophysics,Fast Fourier transform,Solver,Image registration,Scalability
Conference
ISSN
ISBN
Citations 
2167-4329
978-1-4503-5114-0
2
PageRank 
References 
Authors
0.37
31
6
Name
Order
Citations
PageRank
Amir Gholami16612.99
Andreas Mang23510.57
Klaudius Scheufele320.37
Christos Davatzikos43865335.91
Miriam Mehl510615.93
George Biros693877.86