Workshop on Machine Learning for Medical Image Reconstruction Call for Papers

Call for papers MLMIR  2018 
1st  Workshop on Machine Learning for Medical Image Reconstruction
(in conjunction with MICCAI 2018), 
16th  September 2018, Granada, Spain

Image reconstruction is currently undergoing a paradigm shift that is
 driven by advances in machine learning. Whereas traditionally
 transform-based or optimization-based methods have dominated methods
 for image reconstruction, machine learning has opened up the
 opportunity for new data-driven approaches which have demonstrated a
 number of advantages over traditional approaches. In particular, deep
 learning techniques have shown significant potential for image
 reconstruction and offer interesting new approaches. Finally, machine
 learning approaches also offer to the possibility for
 application-specific image reconstruction, e.g. in motion-compensated
 cardiac or fetal imaging.

This is supported by success of machine learning in other inverse
 problems by multiple groups worldwide, with encouraging results and
 increasing interest. Coincidentally, this year is the centenary of
 the Radon transform and the 250th anniversary of Joseph Fourierís
 birthday, the two transforms that provide the mathematical foundation
 for tomography and medical imaging. It seems appropriate and timely
 to consider how to invert the Radon transform and Fourier transform
 via machine learning, and have this workshop serve as a forum to
 reflect this emerging trend of image reconstruction research. In this
 respect, it will frame a fresh new way to recharge or redefine the
 reconstruction algorithms with extensive prior knowledge for superior
 diagnostic performance.

The aim of the workshop is to drive scientific discussion of advanced
 machine learning techniques for image acquisition and image
 reconstruction, opportunities for new applications as well as
 challenges in the evaluation and validation of ML based
 reconstruction approaches. Specifically, this will include the topics
 such as those listed below (but not limited to):

*      Compressed sensing methods
*      Sparsity and low-rank methods
*      Machine learning for image super-resolution
*      Machine learning for image synthesis
*      Machine learning for quantitative imaging (including MRF)
*      Deep learning for image reconstruction including
o   CNN-based approaches
o   RNN-based approaches
o   Adversarial and generative approaches
o   Fusion with traditional reconstruction techniques 
*      Machine learning for
o   X-ray CT image reconstruction (such as for low-dose imaging) ?
o   MR image reconstruction (such as for fast imaging)
o   SPECT and PET image reconstruction
o   Ultrasound and optical imaging
o   Multimodality fusion or joint image reconstruction across two or more modalities ?
*      Applications of ML for image reconstruction in
o   Neuroimaging
o   Cardiac imaging
o   Abdominal imaging
o   Fetal and/or neonatal imaging
*      Validation of ML for image reconstruction

Daniel Rueckert, Imperial College London, UK
Florian Knoll, New York University, US
Andreas Maier, University of Erlangen, Germany

Keynote  Speakers
Jong Chul Ye (KAIST)
Michael Unser (EPFL)
More to follow


MICCAI  paper results released: 22nd  May 2018
Paper submission deadline: 11th
Decisions  to authors: 2nd  July 2018
Camera-ready  submission: 17th July 2018 
Workshop: 16th  September 2018 (am), Granada, Spain