Image registration consists of aligning two (potentially 3D) images such that their contents match. In a multi-modal setting, the data are imaged through different sensors and the style and contents of the images might not correspond across modalities (e.g. CT/X-Ray/CAT scans), but still represent the same object. The goal is to recover the transformation that aligns the images semantically. Recent methods [1][2] have shown great success at efficiently and accurately aligning images of different modalities. The nature of the algorithms used (FFT, linear algebra on matrices & tensors) makes the full pipeline perfectly parallel. As such, GPUs are well suited for this task, but do require a high amount of memory to handle 3D data. We discovered some improvements to the methods listed above that improve the speed and decrease the search space of the sought-after transformations, which helps transition the method to big 3-D biomedical images. We also got access to an expansive dataset consisting of 3D objects, such as e.g. images of 16000x16000x80 (with 2 color channels). We would like to expand this research by testing multiple global optimization methods and assessing the convergence rate to the right set of parameters. [1] Öfverstedt, Johan, Joakim Lindblad, and Nataša Sladoje. "Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment." arXiv preprint arXiv:2106.14699 (2021). [2] Öfverstedt, Johan, Joakim Lindblad, and Nataša Sladoje. "Cross-Sim-NGF: FFT-Based Global Rigid Multimodal Alignment of Image Volumes using Normalized Gradient Fields." arXiv preprint arXiv:2110.10156 (2021).