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The study presents a deep learning framework aimed at synthesizing 3D MRIvolumes from three-dimensional ultrasound images of the brain utilizing thePix2Pix GAN model. The process involves inputting a 3D volume of ultrasoundsinto a UNET generator and patch discriminator, generating a corresponding 3Dvolume of MRI. Model performance was evaluated using losses on thediscriminator and generator applied to a dataset of 3D ultrasound and MRIimages. The results indicate that the synthesized MRI images exhibit somesimilarity to the expected outcomes. Despite challenges related to datasetsize, computational resources, and technical complexities, the methodsuccessfully generated MRI volume with a satisfactory similarity score meant toserve as a baseline for further research. It underscores the potential of deeplearning-based volume synthesis techniques for ultrasound to MRI conversion,showcasing their viability for medical applications. Further refinement andexploration are warranted for enhanced clinical relevance.

BrainVoxGen: Deep learning framework for synthesis of Ultrasound to MRI