CLAIRE: A DISTRIBUTED-MEMORY SOLVER FOR CONSTRAINED LARGE DEFORMATION DIFFEOMORPHIC IMAGE REGISTRATION.
CLAIRE: A DISTRIBUTED-MEMORY SOLVER FOR CONSTRAINED LARGE DEFORMATION DIFFEOMORPHIC IMAGE REGISTRATION.
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DOI：
10.1137/18m1207818
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Biros G
中科院分区：
文献类型：
--
作者：
Mang A;Gholami A;Davatzikos C;Biros G
通过这项工作，我们释放了一个有效的求解器的分布式求解器，用于在三个维度中使用二氧化型图像登记问题基于一种全球化的，预处理的，不切实际的空间高斯 - 诺夫顿（Newton），我们利用科学计算中的最新技术来开发一个表面上的求解器，该求解器可扩展到数千个在高端簇上的分布式存储器。我们提出公式，讨论算法特征，描述软件包，并为减少的空间Hessian提出改进的预处理，以加快我们在合成和真实数据上测试求解器的收敛性。数据集我们可以在具有20个核心的标准计算节点上解决临床相关的数据大小的注册问题，并在目前的工作中获得了出色的数据保真度。与我们以前的作品相比，达到17倍。
With this work we release CLAIRE, a distributed-memory implementation of an effective solver for constrained large deformation diifeomorphic image registration problems in three dimensions. We consider an optimal control formulation. We invert for a stationary velocity field that parameterizes the deformation map. Our solver is based on a globalized, preconditioned, inexact reduced space Gauss‒Newton‒Krylov scheme. We exploit state-of-the-art techniques in scientific computing to develop an eifective solver that scales to thousands of distributed memory nodes on high-end clusters. We present the formulation, discuss algorithmic features, describe the software package, and introduce an improved preconditioner for the reduced space Hessian to speed up the convergence of our solver. We test registration performance on synthetic and real data. We Demonstrate registration accuracy on several neuroimaging datasets. We compare the performance of our scheme against diiferent flavors of the Demons algorithm for diifeomorphic image registration. We study convergence of our preconditioner and our overall algorithm. We report scalability results on state-of-the-art supercomputing platforms. We Demonstrate that we can solve registration problems for clinically relevant data sizes in two to four minutes on a standard compute node with 20 cores, attaining excellent data fidelity. With the present work we achieve a speedup of (on average) 5× with a peak performance of up to 17× compared to our former work.
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影响因子：
3.1
作者：
Alexanderian, Alen;Petra, Noemi;Ghattas, Omar
通讯作者：
Ghattas, Omar
DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
Beg, MF;Miller, MI;Younes, L
通讯作者：
Younes, L
影响因子：
3.1
作者：
Biros, G;Ghattas, O
通讯作者：
Ghattas, O
影响因子：
3.1
作者：
Adavani, Santi S.;Biros, George
通讯作者：
Biros, George
影响因子：
5.7
作者：
Ashburner J;Friston KJ
通讯作者：
Friston KJ