Wasserstein GANs for MR Imaging: from Paired to Unpaired Training
Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this paper leverages unpaired adversarial training for reconstruction networks, where the inputs are undersampled k-space and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset. The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality. The generator is an unrolled neural network -- a cascade of convolutional and data consistency layers. The discriminator is also a multilayer CNN that plays the role of a critic scoring the quality of reconstructed images based on the Wasserstein distance. Our experiments with knee MRI datasets demonstrate that the proposed unpaired training enables diagnostic-quality reconstruction when high-quality image labels are not available for the input types of interest, or when the amount of labels is small. In addition, our adversarial training scheme can achieve better image quality (as rated by expert radiologists) compared with the paired training schemes with pixel-wise loss.
Code (0)
등록된 구현이 없습니다.
Tasks
DiagnosticImage ReconstructionSimilar Papers 제목 키워드 기반
Deep CT to MR Synthesis using Paired and Unpaired Data
MR imaging will play a very important role in radiotherapy treatment planning for segmentation of tumor volumes and organs. However, the use of MR-based radiotherapy is limited because of the high cost and the increased …
Generative Adversarial NetworkDeep Photo Enhancer: Unpaired Learning for Image Enhancement From Photographs With GANs
This paper proposes an unpaired learning method for image enhancement. Given a set of photographs with the desired characteristics, the proposed method learns a photo enhancer which transforms an input image into an enh…
Image EnhancementBone Suppression on Chest Radiographs With Adversarial Learning
Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials, namely soft tissues, and osseous structures, to better characterize a wide variety of thoracic patholog…
Image-to-Image TranslationSSIMTranslationFast Computational Ghost Imaging using Unpaired Deep Learning and a Constrained Generative Adversarial Network
The unpaired training can be the only option available for fast deep learning-based ghost imaging, where obtaining a high signal-to-noise ratio (SNR) image copy of each low SNR ghost image could be practically time-consu…
Deep LearningGenerative Adversarial NetworkManifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis
Unpaired image-to-image translation has been applied successfully to natural images but has received very little attention for manifold-valued data such as in diffusion tensor imaging (DTI). The non-Euclidean nature of D…
Diffusion MRIImage-to-Image TranslationVocal Bursts Intensity Prediction