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  5. MR Image Based Pseudo-CT Image Synthesis Using Conditional Generative Adversarial Network

MR Image Based Pseudo-CT Image Synthesis Using Conditional Generative Adversarial Network

H Kim1,2* , T Kim3 , S Kim3 , S Ye4 , (1) Artificial Intelligence Research Institute, Republic of Korea (2) Department of Radiology, Yonsei University College of Medicine, Republic of Korea, (3) Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, USA (4) Program in Biomedical Radiation Sciences, Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Republic of Korea

Presentations

(Sunday, 7/29/2018) 4:00 PM - 4:55 PM

Room: Karl Dean Ballroom B1

Purpose: We propose a deep-learning based method for pseudo-CT image synthesis from its corresponding MR image.

Methods: We used CT and T1-weighted MR image pairs of nineteen glioblastoma patients from the Cancer Imaging Archive (TCIA) as experimental data. The CT-MR image pairs were aligned by rigid-body registration using SimpleITK. The data oversampling was done by augmentation (flip and rotation) to overcome small amount of data. Finally, the dataset was divided into three categories: train (60%), validation (20%), and test sets (20%). The Conditional GAN model was trained by collecting 2D MR-CT slice pairs from each subject's 3D MR/CT volumetric data. The model consists of two parts: G (generator) is a fully connected network to generate pseudo CT images G(x, z) from a random noise vector z under the condition x (corresponding MR image), and D (discriminator) is a convolutional neural network to discriminate between an real CT image (ground truth) and an estimated pseudo-CT image. The G tries to minimize objective function against the D which tries to maximize it. Thus, the model can be trained to learn a pix-to-pix mapping to predict pseudo CT images from their corresponding MR images. The proposed model was implemented with Pytorch. The geometric accuracy of the pseudo-CT images generated from the test MR image set was evaluated against the real CT image using dice similarity score.

Results: Generating a pseudo-CT slice for each test MR image slice with our trained model only took <1 s while the training process took 5 hours. The overall average dice similarity score was 0.934 for all test cases.

Conclusion: This result showed that our method is feasible for predicting pseudo-CT images from their corresponding MR images and is applicable for MR-only treatment planning as well.

Keywords

MRI, CT, Brain

Taxonomy

IM/TH- MRI in Radiation Therapy: MRI for treatment planning

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