CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN

Quantitative susceptibility mapping (QSM) is a useful magnetic resonance imaging (MRI) technique which provides spatial distribution of magnetic susceptibility values of tissues. QSMs can be obtained by deconvolving the dipole kernel from phase images, but the spectral nulls in the dipole kernel make the inversion ill-posed... (read more)

PDF Abstract
No code implementations yet. Submit your code now

Tasks


Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
Residual Connection
Skip Connections
Batch Normalization
Normalization
Residual Block
Skip Connection Blocks
Convolution
Convolutions
PatchGAN
Discriminators
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
Leaky ReLU
Activation Functions
Cycle Consistency Loss
Loss Functions
ReLU
Activation Functions
GAN Least Squares Loss
Loss Functions
Instance Normalization
Normalization
CycleGAN
Generative Models