This paper presents a multi-channel approach for performing registration between magnetic resonance (MR) images with different modalities. In general, a multi-channel registration cannot be used when the moving and target images do not have analogous modalities. In this work, we address this limitation by using a random forest regression technique to synthesize the missing modalities from the available ones. This allows a single channel registration between two different modalities to be converted into a multi-channel registration with two mono-modal channels. To validate our approach, two openly available registration algorithms and five cost functions were used to compare the label transfer accuracy of the registration with (and without) our multichannel synthesis approach. Our results show that the proposed method produced statistically significant improvements in registration accuracy (at an α level of 0.001) for both algorithms and all cost functions when compared to a standard multi-modal registration using the same algorithms with mutual information. KeywordsMulti-modal image registration; Multi-channel image registration; Magnetic resonance imaging; Image synthesis DESCRIPTION OF PURPOSEImage registration, the alignment of different image spaces, is a fundamental tool in medical image processing. It is used in a number of applications, include atlasing, population analysis, classification, and segmentation. 1 In general, registration is performed between a single pair of moving and target images. However, if additional modalities exist for both the moving and target subjects, a "multi-channel" or "multivariate" framework 2-5 can be used to improve the registration accuracy and robustness. In such frameworks, the extra modalities for the moving and target images are used to evaluate a combined cost function that optimizes a single deformation field to best align all the image modalities.Further author information, send correspondence to Min Chen (mchen55@jhu.edu).. HHS Public Access Author Manuscript Author ManuscriptAuthor Manuscript Author ManuscriptOne primary restriction of multi-channel registration is that analogous modalities must be available for both the moving and target image. This prevents such algorithms from being used in cases where the data is missing modalities for specific subjects, or the modalities do not match between the moving and target images (i.e., in the case of multi-modal registration). In this work, we address this limitation by applying an image synthesis technique 6 to provide the missing modality for the multi-channel registration. The method is able to synthesize an image of one modality from another by using aligned atlases of both modalities to learn the intensity and structural transform between the modalities.Our proposed method is related to existing registration approaches that rely on modality reduction, 7,8 where both the moving and target images are converted into a single modality before registration. In particular, the use of image synthesis has recent...