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  1. Enhanced synthetic MRI generation from ct scans using CycleGAN with feature extraction

Enhanced synthetic MRI generation from ct scans using CycleGAN with feature extraction

Nikbakhsh, S ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1109/ICIIP61524.2023.10537701
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2023
  4. Abstract:
  5. 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. © 2023 IEEE
  6. Keywords:
  7. Computed tomography (CT) ; Cycle-consistent adversarial networks ; Image-to-image translation ; Magnetic resonance imaging (MRI) ; Medical image synthesis ; Radiotherapy
  8. Source: Proceedings of the IEEE International Conference Image Information Processing ; 2023 , Pages 683-688 ; 2640074X (ISSN); 979-835037140-6 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/10537701