Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

10/05/2018
by   Xuan Gu, et al.
0

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment method. Scalar measures quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. We demonstrate how Generative Adversarial Networks (GANs) can be used to generate diffusion scalar measures from structural MR images in a single optimized step, without diffusion models and diffusion data. We show that the used Cycle-GAN model can synthesize visually realistic and quantitatively accurate diffusion-derived scalar measures.

READ FULL TEXT
research
06/20/2018

Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT

In medical imaging, a general problem is that it is costly and time cons...
research
02/24/2021

Optimized Diffusion Imaging for Brain Structural Connectome Analysis

High angular resolution diffusion imaging (HARDI), a type of diffusion m...
research
06/19/2017

Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

There is no consensus on how to construct structural brain networks from...
research
04/01/2020

Manifold-Aware CycleGAN for High Resolution Structural-to-DTI Synthesis

Unpaired image-to-image translation has been applied successfully to nat...
research
06/24/2021

Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI

Current deep learning approaches for diffusion MRI modeling circumvent t...
research
06/25/2016

A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging

Diffusion magnetic resonance imaging (dMRI) is an emerging medical techn...

Please sign up or login with your details

Forgot password? Click here to reset