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

• URL: http://arxiv.org/abs/2310.20604v2
• Date: Tue, 28 Nov 2023 08:29:18 GMT
• Title: Enhanced Synthetic MRI Generation from CT Scans Using CycleGAN with Feature Extraction
• Authors: Saba Nikbakhsh, Lachin Naghashyar, Morteza Valizadeh, Mehdi Chehel Amirani
• Abstract summary: We propose an approach for enhanced monomodal registration using synthetic MRI images from CT scans. Our methodology shows promising results, outperforming several state-of-the-art methods.
• Score: 3.2088888904556123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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