Uncertainty estimation for trust attribution to speed-of-sound reconstruction with variational networks [0.03%]
基于变分网络的速度重建可信度评估及不确定性估计
Sonia Laguna,Lin Zhang,Can Deniz Bezek et al.
Sonia Laguna et al.
Purpose: Speed-of-sound (SoS) is a biomechanical characteristic of tissue, and its imaging can provide a promising biomarker for diagnosis. Reconstructing SoS images from ultrasound acquisitions can be cast as a limited-a...
A Variational Network for Biomedical Images Denoising using Bayesian model and Auto-Encoder [0.03%]
基于贝叶斯模型和自动编码器的生物医学图像去噪变分网络
Aurelle Tchagna Kouanou,Issa Karambal,Yae Gaba et al.
Aurelle Tchagna Kouanou et al.
Auto-encoders have demonstrated outstanding performance in computer vision tasks such as biomedical imaging, including classification, segmentation, and denoising. Many of the current techniques for image denoising in biomedical application...
CineVN: Variational network reconstruction for rapid functional cardiac cine MRI [0.03%]
基于变分网络的心脏功能电影MRI快速重建方法
Marc Vornehm,Jens Wetzl,Daniel Giese et al.
Marc Vornehm et al.
Purpose: To develop a reconstruction method for highly accelerated cardiac cine MRI with high spatiotemporal resolution and low temporal blurring, and to demonstrate accurate estimation of ventricular volumes and myocardi...
Zhiyuan Zhang,Haoxuan Li,Chengjie Ke et al.
Zhiyuan Zhang et al.
Deep-learning-based methods play an important role in pansharpening that uses panchromatic images to enhance the spatial resolution of multispectral images while maintaining spectral features. However, most existing methods mainly consider ...
Accelerated MRI reconstructions via variational network and feature domain learning [0.03%]
基于变分网络和特征域学习的加速MRI重建算法
Ilias I Giannakopoulos,Matthew J Muckley,Jesi Kim et al.
Ilias I Giannakopoulos et al.
We introduce three architecture modifications to enhance the performance of the end-to-end (E2E) variational network (VarNet) for undersampled MRI reconstructions. We first implemented the Feature VarNet, which propagates information throug...
Zongsheng Yue,Hongwei Yong,Qian Zhao et al.
Zongsheng Yue et al.
Blind image restoration (IR) is a common yet challenging problem in computer vision. Classical model-based methods and recent deep learning (DL)-based methods represent two different methodologies for this problem, each with its own merits ...
FFVN: An explicit feature fusion-based variational network for accelerated multi-coil MRI reconstruction [0.03%]
FFVN:一种用于加速多线圈MRI重建的显式特征融合变分网络
Zhenxi Zhang,Hongwei Du,Bensheng Qiu
Zhenxi Zhang
Magnetic Resonance Imaging (MRI) is a leading diagnostic imaging modality that supports high contrast of soft tissues with no invasiveness or radiation. Nonetheless, it suffers from long scan time owing to the inherent physics in its data a...
Exploring the Acceleration Limits of Deep Learning Variational Network-based Two-dimensional Brain MRI [0.03%]
基于深度学习变分网络的二维脑部MRI加速极限探索
Alireza Radmanesh,Matthew J Muckley,Tullie Murrell et al.
Alireza Radmanesh et al.
Purpose: To explore the limits of deep learning-based brain MRI reconstruction and identify useful acceleration ranges for general-purpose imaging and potential screening. ...
Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications [0.03%]
并行MRI应用中的变分网络和欠采样模式的交替学习方法
Marcelo V W Zibetti,Florian Knoll,Ravinder R Regatte
Marcelo V W Zibetti
This work proposes an alternating learning approach to learn the sampling pattern (SP) and the parameters of variational networks (VN) in accelerated parallel magnetic resonance imaging (MRI). We investigate four variations of the learning ...
Real-time cardiac MRI using an undersampled spiral k-space trajectory and a reconstruction based on a variational network [0.03%]
基于螺旋k空间和变分网络重建的实时心脏MRI技术
Jonas Kleineisel,Julius F Heidenreich,Philipp Eirich et al.
Jonas Kleineisel et al.
Purpose: Cardiac MRI represents the gold standard to determine myocardial function. However, the current clinical standard protocol, a segmented Cartesian acquisition, is time-consuming and can lead to compromised image q...
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