During data acquistion and image reconstruction different types of artifacts can make their way into the final MR images. In this repository I have implemented transforms that model three such artifacts: Gibbs (or truncation) artifact, spikes (herringbone) artifact, and wraparound (alasing) artifact. Each transform is implement in the k-space version of the data.
More details on the physics of the artifacts can be found in the following publications:
AAPM/RSNA physics tutorial for residents: fundamental physics of MR imaging.
Body MRI artifacts in clinical practice: A physicist's and radiologist's perspective.
An Image-based Approach to Understanding the Physics of MR Artifacts.
For an overview of the three transforms and visualizations of their applications on the data refer to the Jupyter notebook: artifacts_transforms_visualizations.ipynb
.
I have contributed the Gibbs filter to the codebase of MONAI as various transfroms:
-
Related to Gibbs artifacts:
- GibbsNoise: applies artifact directly on the image.
- RandGibbsNoise: to apply randomly on images with uniform sampling of filter's intensity.
- GibbsNoised: to apply on group data; dictionary-style version of
GibbsNoise
. - RandGibbsNoised: dictionary-style of
RandGibbsNoise
.
-
Related to Spikes (herringbone) artifacts:
- KSpaceSpikeNoise: applies spike artifacts directly on the image.
- RandKSpaceSpikeNoise: applies the artifacts randomly on images while sampling in the intensity of the artifacts.
- KSpaceSpikeNoised: allows to apply transform both on the image and/or label; dictionary-style version of
KSpaceSpikeNoise
. - RandKSpaceSpikeNoised: dictionary-version of
RandKSpaceSpikeNoise
.
- Segmentation experiments are performed using a residual Unet.
- Training the models on stylized data results in improved performance when it comes to unseen data with different strengths of the artifacts.
- Examples of this results are shown in the notebook
stylized_models_inference.ipynb
.
- Early experiments are showing that stylized data are more robust when tested on images coming from hospitals unseen at training time.
- Reconstruction problem in the presence of undersampled data.
- Network: ReconGan.