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, which is a mathematical foundation for tomography. 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
  • CNN-based approaches
  • RNN-based approaches
  • Adversarial and generative approaches
• Machine learning for
  • X-ray CT image reconstruction (such as for low-dose imaging)
  • MR image reconstruction (such as for fast imaging)
  • SPECT and PET image reconstruction
  • Ultrasound and optical imaging
  • Multimodality fusion or joint image reconstruction across two or more modalities
• Applications of ML for image reconstruction in
  • Neuroimaging
  • Cardiac imaging
  • Abdominal imaging
  • Fetal and/or neonatal imaging
• Validation of ML for image reconstruction