Representation of developmental gradients in biological structures requires visualization of storage compounds, metabolites or mRNA hybridization patterns in a 3D morphological framework. NMR imaging can generate such a 3D framework by non-invasive scanning of living structures. Histology provides the distribution of developmental markers as 2D cross-sections. Multimodal alignment tries to put such different image modalities into correspondence. Here we compare different methods for rigid registration of 3D NMR datasets and 2D cross-sections of developing barley grains. As metrics for similarity measurements mutual information, cross correlation and overlap index are used. In addition, different filters are applied to the images before the alignment. The algorithms are parallelized, partially vectorized and implemented on the Cell Broadband Engine processor in a Playstation® 3. Evaluation is done by a comparison of the results to a manually defined gold standard of a NMR dataset and a corresponding 2D cross-section of the same grain. The results show, that best alignment is achieved by application of mutual information on sobel-filtered images and, compared to the implementation on a standard single-core CPU, the computation is accelerated by a factor up to 1.95.