We present our work on scalable, GPU-accelerated algorithms for diffeomorphic image registration. The associated software package is termed CLAIRE. Image registration is a non-linear inverse problem. It is about computing a spatial mapping from one image of the same object or scene to another. In diffeomorphic image registration, the set of admissible spatial transformations is restricted to maps that are smooth, one-to-one, and have a smooth inverse. We formulate diffeomorphic image registration as a variational problem governed by transport equations. We use an inexact, globalized (Gauss--)Newton--Krylov method for numerical optimization. We consider semi-Lagrangian methods for numerical time integration. Our solver features mixed-precision, hardware-accelerated computational kernels for optimal computational throughput. We use the message-passing interface for distributed-memory parallelism and deploy our code on modern high-performance computing architectures. Our solver allows us to solve clinically relevant problems in under four seconds on a single GPU. It can also be applied to large-scale 3D imaging applications with data that is discretized on meshes with billions of voxels. We demonstrate that our numerical framework yields high-fidelity results in only a few seconds, even if we search for an optimal regularization parameter.

翻译：摘要：本文提出面向微分同胚图像配准的可扩展GPU加速算法，相关软件包称为CLAIRE。图像配准是一个非线性反问题，旨在计算同一物体或场景中两幅图像间的空间映射。在微分同胚图像配准中，容许的空间变换被限制为光滑、一一对应且具有光滑逆映射的变换。我们将微分同胚图像配准建模为受输运方程约束的变分问题，采用非精确全局化（Gauss-）Newton-Krylov方法进行数值优化，并引入半拉格朗日方法进行数值时间积分。求解器采用混合精度硬件加速计算核以优化计算吞吐量，通过消息传递接口实现分布式内存并行，并部署于现代高性能计算架构。该求解器可在单GPU上于4秒内求解临床相关配准问题，也能处理网格离散化达数十亿体素的大规模三维成像数据。实验表明，即使需搜索最优正则化参数，本数值框架仍能在数秒内获得高保真配准结果。