ArticleScientific reports2024
Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Dosimetric evaluation of magnetic resonance imaging based synthetic computed tomography for head and neck photon and proton therapy.Journal of applied clinical medical physics · 2025Article
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- Validating clinical feasibility of MRCAT and deep learning-based synthetic CT images for cervical cancer patient.Journal of applied clinical medical physics · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning.Physics and imaging in radiation oncology · 2025Article
- Adversarial robustness improvement for X-ray bone segmentation using synthetic data created from computed tomography scans.Scientific reports · 2024Article
- Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions.Frontiers in robotics and AI · 2024Review
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Authors and funding
15 authors.
Funding
Abstract
This work aims to investigate the clinical feasibility of deep learning-based synthetic CT images for cervix cancer, comparing them to MR for calculating attenuation (MRCAT). Patient cohort with 50 pairs of T2-weighted MR and CT images from cervical cancer patients was split into 40 for training and 10 for testing phases. We conducted deformable image registration and Nyul intensity normalization for MR images to maximize the similarity between MR and CT images as a preprocessing step. The processed images were plugged into a deep learning model, generative adversarial network. To prove clinical feasibility, we assessed the accuracy of synthetic CT images in image similarity using structural similarity (SSIM) and mean-absolute-error (MAE) and dosimetry similarity using gamma passing rate (GPR). Dose calculation was performed on the true and synthetic CT images with a commercial Monte Carlo algorithm. Synthetic CT images generated by deep learning outperformed MRCAT images in image similarity by 1.5% in SSIM, and 18.5 HU in MAE. In dosimetry, the DL-based synthetic CT images achieved 98.71% and 96.39% in the GPR at 1% and 1 mm criterion with 10% and 60% cut-off values of the prescription dose, which were 0.9% and 5.1% greater GPRs over MRCAT images.
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Registered trials
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