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Sparsity of annotated data is a major limitation in medical image processingtasks such as registration. Registered multimodal image data are essential forthe diagnosis of medical conditions and the success of interventional medicalprocedures. To overcome the shortage of data, we present a method that allowsthe generation of annotated multimodal 4D datasets. We use a CycleGAN networkarchitecture to generate multimodal synthetic data from the 4D extendedcardiac-torso (XCAT) phantom and real patient data. Organ masks are provided bythe XCAT phantom, therefore the generated dataset can serve as ground truth forimage segmentation and registration. Realistic simulation of respiration andheartbeat is possible within the XCAT framework. To underline the usability asa registration ground truth, a proof of principle registration is performed.Compared to real patient data, the synthetic data showed good agreementregarding the image voxel intensity distribution and the noise characteristics.The generated T1-weighted magnetic resonance imaging (MRI), computed tomography(CT), and cone beam CT (CBCT) images are inherently co-registered. Thus, thesynthetic dataset allowed us to optimize registration parameters of amultimodal non-rigid registration, utilizing liver organ masks for evaluation.Our proposed framework provides not only annotated but also multimodalsynthetic data which can serve as a ground truth for various tasks in medicalimaging processing. We demonstrated the applicability of synthetic data for thedevelopment of multimodal medical image registration algorithms.

Generation of annotated multimodal ground truth datasets for abdominal medical image registration