Unsupervised MRI Reconstruction with Generative Adversarial Networks
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground truth data for supervised training. Acquiring fully-sampled data is often either difficult or impossible, particularly for dynamic contrast enhancement (DCE), 3D cardiac cine, and 4D flow. We present a deep learning framework for MRI reconstruction without any fully-sampled data using generative adversarial networks. We test the proposed method in two scenarios: retrospectively undersampled fast spin echo knee exams and prospectively undersampled abdominal DCE. The method recovers more anatomical structure compared to conventional methods.
Code (1)
Tasks
Deep LearningImage ReconstructionMRI ReconstructionUnsupervised Image-To-Image TranslationSimilar Papers 제목 키워드 기반
Masked GANs for Unsupervised Depth and Pose Prediction with Scale Consistency
Previous work has shown that adversarial learning can be used for unsupervised monocular depth and visual odometry (VO) estimation, in which the adversarial loss and the geometric image reconstruction loss are utilized a…
Generative Adversarial NetworkImage ReconstructionPose PredictionTrajectory Prediction+1Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consi…
Image ReconstructionMRI ReconstructionUnsupervised Style-based Explicit 3D Face Reconstruction from Single Image
Inferring 3D object structures from a single image is an ill-posed task due to depth ambiguity and occlusion. Typical resolutions in the literature include leveraging 2D or 3D ground truth for supervised learning, as wel…
3D Face Reconstruction3D ReconstructionFace ModelFace Reconstruction+2Unsupervised Adversarial Image Reconstruction
We address the problem of recovering an underlying signal from lossy, inaccurate observations in an unsupervised setting. Typically, we consider situations where there is little to no background knowledge on the structur…
Image ReconstructionStochastic reconstruction of an oolitic limestone by generative adversarial networks
Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures for upscaling, numerical computation of…
Image GenerationImage ReconstructionUncertainty Quantification