With this work, we release CLAIRE, a distributed-memory implementation of an effective solver for constrained large deformation diffeomorphic 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 effective 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 different flavors of the Demons algorithm for diffeomorphic 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$\times$ with a peak performance of up to 17$\times$ compared to our former work.
翻译:通过这项工作,我们发行了CLAIRE, 这是一个针对限制大型变形变异变异图像登记问题的有效解决方案的分布式模块, 一个用于限制大型变形变异图像登记问题的分布式模块。 我们考虑一个最佳控制配方。 我们反向了一个固定速度字段, 以参数化变形地图为参数。 我们的解析器基于一个全球化的、 先决条件的、不精确的缩小的空间高斯- 牛顿- 克里洛夫计划。 我们利用科学计算中最先进的技术开发一个有效的解决方案, 以至数千个高端集群的分布式记忆节点。 我们展示了配方, 讨论算法特征, 描述软件包, 并引入一个改进的前提条件。 我们测试了一个降低空间的Hesian的固定速度场景, 以加快解变形图的趋同速度。 我们在几个神经成形的数据集上展示了注册的准确度。 我们比较了我们的计划, 与不同调的恶魔算法的调度, 在高端组群群中, 我们研究我们的先决条件和总体算法的合并。 我们报告关于州- 平价速度的进度, 我们的运行前四级的进度数据将显示一个共标值数据。