3D conditional GAN with transfer learning for pediatric MR to CT image synthesis combining adult and pediatric patient data

Soyoung Park; Sahaja Acharya; Matthew Ladra; Junghoon Lee

doi:10.1117/12.3008811

29 March 2024 3D conditional GAN with transfer learning for pediatric MR to CT image synthesis combining adult and pediatric patient data

Soyoung Park, Sahaja Acharya, Matthew Ladra, Junghoon Lee

Author Affiliations +

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 129282H (2024) https://doi.org/10.1117/12.3008811
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

CT image synthesis from MR images is necessary for MR-only treatment planning, MRI-based quality assurance (QA), and treatment assessment in radiation therapy (RT). For pediatric cancer patients, reducing ionizing radiation from CT scans is preferred for which MRI-based RT planning and assessment are truly beneficial. Recently, deep learning-based synthetic CT (sCT) generation have demonstrated promising results on adult data. Generally, it is challenging to develop a pediatric sCT generation model due to significant anatomical variability and relatively smaller number of available pediatric data compared to adult. In this study, we investigated a 3D conditional generative adversarial network (cGAN)-based transfer learning approach for accurate pediatric sCT generation. Our model was first trained using adult data with augmentation by scaling to simulate pediatric data, followed by fine-tuning on pediatric data. We compared three different training scenarios; (1) training on 50 adult patient data with scaling augmentation, (2) training on combined 50 adult and 50 pediatric patient data, and (3) fine-tuning on 50 pediatric data using the pre-trained model on 50 adult data. 3D cGAN with transfer learning showed significantly better synthesis performance than the other models with average mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) index of 51.99 HU, 24.74, and 0.80, respectively. The proposed 3D cGAN-based transfer learning was able to accurately synthesize pediatric CT images from MRI, allowing us to realize pediatric MR-only RT planning, QA, and treatment assessment.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Soyoung Park, Sahaja Acharya, Matthew Ladra, and Junghoon Lee "3D conditional GAN with transfer learning for pediatric MR to CT image synthesis combining adult and pediatric patient data", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 129282H (29 March 2024); https://doi.org/10.1117/12.3008811

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