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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Fast GPU 3D diffeomorphic image registration

3D image registration is one of the most fundamental and computationally expensive operations in medical image analysis. Here, we present a mixed-precision, Gauss–Newton–Krylov solver for diffeomorphic registration of two images. Our work extends the publicly available CLAIRE library to GPU architectures. Despite the importance of image registration, only a few implementations of large deformation diffeomorphic registration packages support GPUs. Our contributions are new algorithms to significantly reduce the run time of the two main computational kernels in CLAIRE: calculation of derivatives and scattered-data interpolation. Additionally, we deploy (i) highly-optimized, mixed-precision GPU-kernels for the evaluation of scattered-data interpolation, (ii) replace Fast-Fourier-Transform (FFT)-based first-order derivatives with optimized 8th-order finite differences, and (iii) compare with state-of-the-art CPU and GPU implementations. As a highlight, we demonstrate that we can register clinical images in less than 6 s on a single NVIDIA Tesla V100. This amounts to over 20 speed-up over the current version of CLAIRE and over 30 speed-up over existing GPU implementations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

CLAIRE—Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications

We study the performance of CLAIRE—a diffeomorphic multi-node, multi-GPU image-registration algorithm and software—in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full-resolution registrations obtained with CLAIRE to lower resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality—but not always. For example, downsampling a synthetic image from 10243 to 2563 decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low contrast high resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in reasonable time. The highest resolution considered are CLARITY images of size 2816×3016×1162. To the best of our knowledge, this is the first study on image registration quality at such resolutions.

Himthani, Naveen↗
DOE OSTI2021

Automated CT registration, segmentation, and quantification (AutoCT) v1.0

Processing and analyzing brain imaging is crucial in both scientific development and clinical field. In this software package, we build a pipeline that integrates automatic registration, segmentation, and quantitative analysis for subjects' CT scans. Leveraging diffeomorphic transformations, we enable optimized forward and inverse mappings between an image and the reference. Furthermore, we extract localized features from deformation field based on an online template process, which advances statistical learning downstream. The created templates, atlas as well as our methods provide the brain imaging community tools for AI implementations.

Essiari, Abdelilah↗
DOE OSTI2021

Automated CT registration, segmentation, and quantification (AutoCT) v1.1

Processing and analyzing brain imaging is crucial in both scientific development and clinical field. In this software package, we build a pipeline that integrates automatic registration, segmentation, and quantitative analysis for subjects' CT scans. Leveraging diffeomorphic transofrmations, we enable optimized forward and inverse mappings between an image and the reference. Furthermore, we extract localized features from deformation field based on an online template process, which advances statistical learning downstream. The created templates, atlas as well as our methods provide the brain imaging community tools for AI implementations

Bai, Zhe↗