# Fully-deformable 3D image registration in two seconds

## TL;DR

This paper introduces a GPU-accelerated variational 3D image registration method that achieves high accuracy and speed, enabling real-time processing of large medical scans with significant speedup over CPU implementations.

## Contribution

The authors develop a fully-deformable 3D registration approach optimized for GPUs, achieving over 32x speedup and competitive accuracy on benchmark datasets.

## Key findings

- Average runtime of 1.99 seconds for full registration at 256^3 resolution
- 32.53x speedup over CPU implementation
- Ranks third on DIR-lab benchmark for landmark error

## Abstract

We present a highly parallel method for accurate and efficient variational deformable 3D image registration on a consumer-grade graphics processing unit (GPU). We build on recent matrix-free variational approaches and specialize the concepts to the massively-parallel manycore architecture provided by the GPU. Compared to a parallel and optimized CPU implementation, this allows us to achieve an average speedup of 32.53 on 986 real-world CT thorax-abdomen follow-up scans. At a resolution of approximately $256^3$ voxels, the average runtime is 1.99 seconds for the full registration. On the publicly available DIR-lab benchmark, our method ranks third with respect to average landmark error at an average runtime of 0.32 seconds.