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