Projection image-to-image translation in hybrid X-ray/MR imaging
The potential benefit of hybrid X-ray and MR imaging in the interventional environment is large due to the combination of fast imaging with high contrast variety. However, a vast amount of existing image enhancement methods requires the image information of both modalities to be present in the same domain. To unlock this potential, we present a solution to image-to-image translation from MR projections to corresponding X-ray projection images. The approach is based on a state-of-the-art image generator network that is modified to fit the specific application. Furthermore, we propose the inclusion of a gradient map in the loss function to allow the network to emphasize high-frequency details in image generation. Our approach is capable of creating X-ray projection images with natural appearance. Additionally, our extensions show clear improvement compared to the baseline method.
Code (0)
등록된 구현이 없습니다.
Tasks
Image EnhancementImage GenerationImage-to-Image TranslationTranslationSimilar Papers 제목 키워드 기반
Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging
Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in interventional medical imaging applications due to the broad variety of contrast of MRI combined with fast imaging of X-ray-based modalities. T…
Image EnhancementTranslationMR to X-Ray Projection Image Synthesis
Hybrid imaging promises large potential in medical imaging applications. To fully utilize the possibilities of corresponding information from different modalities, the information must be transferable between the domains…
Image GenerationImage-to-Image TranslationTranslationAnalytical reconstructions of full-scan multiple source-translation computed tomography under large field of views
This paper is to investigate the high-quality analytical reconstructions of multiple source-translation computed tomography (mSTCT) under an extended field of view (FOV). Under the larger FOVs, the previously proposed ba…
Roto-Translation Covariant Convolutional Networks for Medical Image Analysis
We propose a framework for rotation and translation covariant deep learning using $SE(2)$ group convolutions. The group product of the special Euclidean motion group $SE(2)$ describes how a concatenation of two roto-tran…
Data AugmentationMedical Image AnalysisTranslationAligning and Projecting Images to Class-conditional Generative Networks
We present a method for projecting an input image into the space of a class-conditional generative neural network. We propose a method that optimizes for transformation to counteract the model biases in generative neural…
Generative Adversarial NetworkTranslation