Titlebook: Machine Learning for Medical Image Reconstruction; Second International Florian Knoll,Andreas Maier,Jong Chul Ye Conference proceedings 201

T-cell

书目名称Machine Learning for Medical Image Reconstruction影响因子(影响力)




书目名称Machine Learning for Medical Image Reconstruction影响因子(影响力)学科排名




书目名称Machine Learning for Medical Image Reconstruction网络公开度




书目名称Machine Learning for Medical Image Reconstruction网络公开度学科排名




书目名称Machine Learning for Medical Image Reconstruction被引频次




书目名称Machine Learning for Medical Image Reconstruction被引频次学科排名




书目名称Machine Learning for Medical Image Reconstruction年度引用




书目名称Machine Learning for Medical Image Reconstruction年度引用学科排名




书目名称Machine Learning for Medical Image Reconstruction读者反馈




书目名称Machine Learning for Medical Image Reconstruction读者反馈学科排名

Hyperalgesia

Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imagingsparsely selected 2D images using integrated reconstruction and total variation loss. We evaluate the classification accuracy on 5 simulated images and compare our results with the SVR method in adult abdominal and in-utero MRI scans. The results show that the proposed pipeline can accurately estima

晚来的提名

Fibrin

APIR-Net: Autocalibrated Parallel Imaging Reconstruction Using a Neural Networkinear relations between sampled and unsampled positions in k-space. The proposed method was compared to the start-of-the-art ESPIRiT and RAKI methods in terms of noise amplification and visual image quality in both phantom and in-vivo experiments. The experiments indicate that APIR-Net provides a pr

Measured

TRAWL

辞职

Modeling and Analysis Brain Development via Discriminative Dictionary Learningrs(ADMM). The effectiveness of the proposed approach is tested on brain age prediction problems by exploring the cortical status, and the experiments are conducted on the PING dataset. The proposed approach produces competitive results. Further, we were able for the first time to capture the status

忙碌

大厅

LAST

Deep Learning Based Metal Inpainting in the Projection Domain: Initial Results. The network architectures show promising inpainting results with smooth transitions with the non-metal areas of the images and thus homogeneous image impressions. Furthermore, this paper shows that providing additional input data to the network, in form of a metal mask, increases the inpainting pe