Artificial intelligence (AI) and machine learning (ML), especially deep learning, have generated tremendous impacts throughout our society, including the tomographic medical imaging field. In contrast to computer vision and image analysis, which have been major application examples
of deep learning and deal with existing images, tomographic medical imaging mainly produces cross-sectional or volumetric images of internal structures from sensor measurements. Recently, deep learning has started being actively developed worldwide for medical imaging, including both tomographic
reconstruction and image analysis. While medical imaging is a well-established field, in which extensive teaching experience has been accumulated over the past few decades, updating the medical imaging course to reflect AI/ML influence is a new challenge given the rapidly changing landscape
of AI-based medical imaging, particularly deep tomographic imaging. In the 2019 fall semester, the medical imaging course at Rensselaer Polytechnic Institute was modified to include an AI framework with positive feedback from students. Encouragingly, many students showed a strong motivation
to learn AI in classes and hands-on projects, as confirmed in their survey reports. In the 2020 fall semester, we improved this course further, incorporating new advances. This article describes our teaching philosophy, practice, and considerations with respect to integrating deep learning,
tomographic imaging, and hands-on programming to redefine the medical imaging course. Furthermore, given the persistent pandemic, online teaching and examination have become an integral part of higher education. These needs will be addressed as well, with the hope of developing an open course
in the future.
