Generating fMRI volumes from T1-weighted volumes using 3D CycleGAN

Registration between an fMRI volume and a T1-weighted volume is challenging, since fMRI volumes contain geometric distortions. Here we present preliminary results showing that 3D CycleGAN can be used to synthesize fMRI volumes from T1-weighted volumes, and vice versa, which can facilitate registration.

[1]  Thomas E. Nichols,et al.  Reply to Brown and Behrmann, Cox, et al., and Kessler et al.: Data and code sharing is the way forward for fMRI , 2017, Proceedings of the National Academy of Sciences.

[2]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[3]  Anders Eklund,et al.  Refacing: Reconstructing Anonymized Facial Features Using Gans , 2018, ArXiv.

[4]  Hans Knutsson,et al.  Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks , 2018, SCIA.

[5]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[6]  Hiroyuki Yoshida,et al.  Cycle-consistent 3D-generative adversarial network for virtual bowel cleansing in CT colonography , 2019, Medical Imaging: Image Processing.

[7]  Lei Wang,et al.  3D cGAN based cross-modality MR image synthesis for brain tumor segmentation , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[8]  Paul Babyn,et al.  Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..

[9]  Su Ruan,et al.  Medical Image Synthesis with Context-Aware Generative Adversarial Networks , 2016, MICCAI.

[10]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Anders Eklund,et al.  Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT , 2018, ArXiv.

[12]  Bruce Fischl,et al.  Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.

[13]  Jaakko Lehtinen,et al.  Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.

[14]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[15]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.