GAN-MAT is a comprehensive framework to study brain microstructure in vivo using only the T1-weighted MRI. Out pipeline provides three main features: (1) Synthesis of 3D T2-weighted MRI from 3D T1-weighted MRI using a conditional generative adversarial network (GAN). (2) Calculation of the microstructure-sensitive proxy based on the T1w/T2w ratio using the synthesized T2-weighted MRI. (3) Computation of the ready-to-use microstructural profile covariance (MPC) matrix, microstructural gradient, and moment features.
Check out ONLINE DOCUMENTATION PAGE for installation and usages.
Paper: GAN-MAT: Generative Adversarial Network-based Microstructural Profile Covariance Analysis Toolbox
- Yeongjun Park, MIP Lab - Sungkyunkwan University
- Bo-yong Park, CAMIN Lab - Inha University
- Mi Ji Lee, Seoul National University Hospital
- Seulki Yoo, CAMIN Lab - Inha University
- Chae Yeon Kim, CAMIN Lab - Inha University
- Jong Young Namgung, CAMIN Lab - Inha University
- Yunseo Park, CAMIN Lab - Inha University
- Hyunjin Park, MIP lab - Sungkyunkwan University
- Eunjung Lee, Poderosa
- Yeodong Yun, Poderosa
- Casey Paquola, Multiscale Neuroanatomy Lab - INM-1 at Forschungzentrum Juelich
- Boris Bernhardt, MICA Lab - Montreal Neurological Institute