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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
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    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
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    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
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    Chapter 9 Sparse-View CT Reconstruction Using Wasserstein GANs
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    Chapter 10 Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees
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    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 5: Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
6 X users
patent
2 patents

Citations

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13 Dimensions

Readers on

mendeley
45 Mendeley
Summary X Patents Dimensions citations
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Chapter title
Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
Chapter number 5
Book title
Machine Learning for Medical Image Reconstruction
Published in
arXiv, September 2018
DOI 10.1007/978-3-030-00129-2_5
Book ISBNs
978-3-03-000128-5, 978-3-03-000129-2
Authors

Fabian Balsiger, Amaresha Shridhar, Shivaprasad Chikop, Vimal Chandran, Olivier Scheidegger, Sairam Geethanath, Mauricio Reyes, Amaresha Shridhar Konar, Balsiger, Fabian, Shridhar Konar, Amaresha, Chikop, Shivaprasad, Chandran, Vimal, Scheidegger, Olivier, Geethanath, Sairam, Reyes, Mauricio

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X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 27%
Student > Ph. D. Student 8 18%
Student > Master 6 13%
Student > Bachelor 4 9%
Student > Doctoral Student 2 4%
Other 1 2%
Unknown 12 27%
Readers by discipline Count As %
Computer Science 12 27%
Engineering 5 11%
Physics and Astronomy 5 11%
Mathematics 3 7%
Medicine and Dentistry 2 4%
Other 2 4%
Unknown 16 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 04 May 2022.
All research outputs
#4,008,021
of 24,226,848 outputs
Outputs from arXiv
#77,115
of 1,027,652 outputs
Outputs of similar age
#69,988
of 315,126 outputs
Outputs of similar age from arXiv
#1,989
of 24,499 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,027,652 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 92% of its peers.
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 315,126 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 24,499 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.

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