↓ Skip to main content

Machine Learning for Medical Image Reconstruction

Overview of attention for book
Cover of 'Machine Learning for Medical Image Reconstruction'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging
  3. Altmetric Badge
    Chapter 2 ETER-net: End to End MR Image Reconstruction Using Recurrent Neural Network
  4. Altmetric Badge
    Chapter 3 Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstruction
  5. Altmetric Badge
    Chapter 4 Complex Fully Convolutional Neural Networks for MR Image Reconstruction
  6. Altmetric Badge
    Chapter 5 Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
  7. Altmetric Badge
    Chapter 6 Improved Time-Resolved MRA Using k -Space Deep Learning
  8. Altmetric Badge
    Chapter 7 Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
  9. Altmetric Badge
    Chapter 8 Bayesian Deep Learning for Accelerated MR Image Reconstruction
  10. Altmetric Badge
    Chapter 9 Sparse-View CT Reconstruction Using Wasserstein GANs
  11. Altmetric Badge
    Chapter 10 Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees
  12. Altmetric Badge
    Chapter 11 A U-Nets Cascade for Sparse View Computed Tomography
  13. Altmetric Badge
    Chapter 12 Approximate k-Space Models and Deep Learning for Fast Photoacoustic Reconstruction
  14. Altmetric Badge
    Chapter 13 Deep Learning Based Image Reconstruction for Diffuse Optical Tomography
  15. Altmetric Badge
    Chapter 14 Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging
  16. Altmetric Badge
    Chapter 15 Towards Arbitrary Noise Augmentation—Deep Learning for Sampling from Arbitrary Probability Distributions
  17. Altmetric Badge
    Chapter 16 Left Atria Reconstruction from a Series of Sparse Catheter Paths Using Neural Networks
  18. Altmetric Badge
    Chapter 17 High Quality Ultrasonic Multi-line Transmission Through Deep Learning
Attention for Chapter 7: Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
Altmetric Badge

Mentioned by

twitter
2 X users

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
21 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
Chapter number 7
Book title
Machine Learning for Medical Image Reconstruction
Published in
arXiv, September 2018
DOI 10.1007/978-3-030-00129-2_7
Book ISBNs
978-3-03-000128-5, 978-3-03-000129-2
Authors

Chen Qin, Wenjia Bai, Jo Schlemper, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Daniel Rueckert, Qin, Chen, Bai, Wenjia, Schlemper, Jo, Petersen, Steffen E., Piechnik, Stefan K., Neubauer, Stefan, Rueckert, Daniel

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 19%
Student > Master 3 14%
Student > Ph. D. Student 3 14%
Professor > Associate Professor 2 10%
Student > Bachelor 1 5%
Other 1 5%
Unknown 7 33%
Readers by discipline Count As %
Engineering 7 33%
Medicine and Dentistry 3 14%
Computer Science 2 10%
Physics and Astronomy 2 10%
Mathematics 1 5%
Other 0 0%
Unknown 6 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 August 2019.
All research outputs
#18,649,291
of 23,103,436 outputs
Outputs from arXiv
#540,767
of 949,639 outputs
Outputs of similar age
#239,669
of 311,572 outputs
Outputs of similar age from arXiv
#18,234
of 23,986 outputs
Altmetric has tracked 23,103,436 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 949,639 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 311,572 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 23,986 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.