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In the field of radiotherapy, accurate imaging and image registration are ofutmost importance for precise treatment planning. Magnetic Resonance Imaging(MRI) offers detailed imaging without being invasive and excels in soft-tissuecontrast, making it a preferred modality for radiotherapy planning. However,the high cost of MRI, longer acquisition time, and certain healthconsiderations for patients pose challenges. Conversely, Computed Tomography(CT) scans offer a quicker and less expensive imaging solution. To bridge thesemodalities and address multimodal alignment challenges, we introduce anapproach for enhanced monomodal registration using synthetic MRI images.Utilizing unpaired data, this paper proposes a novel method to produce thesesynthetic MRI images from CT scans, leveraging CycleGANs and featureextractors. By building upon the foundational work on Cycle-ConsistentAdversarial Networks and incorporating advancements from related literature,our methodology shows promising results, outperforming several state-of-the-artmethods. The efficacy of our approach is validated by multiple comparisonmetrics.

Enhanced Synthetic MRI Generation from CT Scans Using CycleGAN with Feature Extraction