Archive for July, 2019


Gordon Lecture: Machine Learning for Real-time High-quality Biomedical Imaging

07/19/2019


Leslie Ying is currently a Professor of Biomedical Engineering and Electrical Engineering at the University at Buffalo, SUNY. She received her B.E. in Electronics Engineering from Tsinghua University, China in 1997 and both her M.S. and Ph.D. in Electrical Engineering from the University of Illinois at Urbana - Champaign in 1999 and 2003, respectively.
Below is a summary of her presentation

Machine learning has recently attracted a lot of attention in biomedical imaging. It has shown success in biomedical image classifications but only very recently been used for image reconstruction with unique features. For this talk, Dr. Ying started with compressed sensing (CS), a strategy for reconstruction from sub-Nyquist sampled data. Several machine-learning-based methods were introduced within the conventional CS framework. She then explained how the optimization algorithm underlying CS can be unrolled to a deep artificial neural network, such that parameters and prior models can be learned from training samples. Finally, end-to-end convolutional neural networks were presented based on the training data with little knowledge of the imaging system. Connections among different networks were discussed with their benefits and limitations highlighted. Although most examples provided were from MRI, the frameworks are generalizable to image reconstruction problems for most imaging modalities. The talk concluded with future outlooks.

Gordon Lecture: Learn Deeply to Advance Medical Imaging: Artificial Intelligence in MR and PET/MR


Dr. Fang Liu is an assistant scientist at the University of Wisconsin School of Medicine and Public Health.  Dr. Liu obtained his Ph.D. in 2015 from Medical Physics at the University of Wisconsin and completed two years of postdoctoral training at the Radiology department. Dr. Liu has extensive research experience in the technical development of MR imaging for MR pulse sequence design, image reconstruction, quantitative imaging, and image analysis.
Below is a summary of his presentation

Medical imaging is a research field that remains plenty of technical and clinical challenges. The recent development of Artificial Intelligence, particularly Deep Learning (DL), has demonstrated high potentials to resolve such challenges. Dr. Liu presented some of his recent work for DL theory development and applications in medical imaging and will discuss the performance, strengths, and limitations. The talk gave an overview of DL in medical imaging and discuss some recent DL applications that successfully translate new learning-based approaches into performance improvement in MR and PET/MR imaging workflow.  One primary aim is to draw tightly connections between fundamental DL concepts and clinically relevant challenges in medical imaging. Topics covered rapid MR image acquisition, reconstruction and MR quantitative mapping, and image post-processing such as image segmentation and synthesis in MR and PET/MR, and finally lead to DL augmented disease diagnosis and prediction. The talk concluded with a discussion of open problems in DL that are particularly relevant to medical imaging and the potential challenges and opportunities in this emerging field.