MIC Program & Topics

The IEEE Medical Imaging Conference (MIC) is a leading international scientific meeting to discuss the latest physics, engineering and mathematical aspects of medical imaging.

Medical imaging is a continuously growing field where technological advances in detectors and instrumentation and modern computational methods lead the way towards advances in detection, diagnosis, treatment and monitoring.

MIC 2020 has a unique focus on leading-edge research in multiple modalities of imaging, design and implementation of novel hardware and software solutions, and their effective translation to clinical practice. In recent years there has been increased attention on applications such as machine learning and AI and other fast emerging research areas.

MIC is an opportunity for students, post-doctoral fellows, and junior and senior researchers from around the world to come together in collaboration and the sharing of ideas, innovations and the results of their scientific endeavors.

The scientific program of the MIC 2020 consists of oral and poster sessions, plenary sessions and a student award session. Regular sessions will be complemented by Short Courses and specialized Workshops covering all different imaging and therapy timely topics

Authors are invited to submit papers describing their original, unpublished work on one of the topics below:

MIC 2020 Topics:

  • New radiation detector technologies for medical imaging
  • Simulation and modelling of medical imaging systems
  • Clinical emission imaging systems
  • X-ray and CT imaging systems
  • Multi-modality imaging systems
  • High-resolution imaging systems (small-animal, brain, intra-operative, etc.)
  • Other imaging technologies (optical, MR, etc.)
  • Tomographic imaging reconstruction methods
  • Signal processing, image analysis, and quantitative imaging techniques
  • Imaging in radiotherapy, hadron therapy, image-guided interventions
  • Parametric imaging and tracer kinetic modeling
  • Assessment and comparison of image quality and methods

Note: abstracts utilizing deep learning and artificial intelligence will be integrated into the above topics depending on the use case

George El Fakqri
MIC Program Chair
Harvard University

Ramsey Badawi
MIC Deputy Chair
University of California Davis