Archive for August, 2019

Gordon Lecture: Methodology Development with Carbon-11 and Fluorine-18 for PET Applications


So Jeong Lee is currently a postdoctoral fellow at PET center in the Department of Radiology at the University of Michigan working with Prof. Peter J. H. Scott. She obtained her B.S and B.E in Chemistry and Material Science at SUNY Stony Brook University in 2010 and her Ph.D in Chemistry from Stony Brook University and BNL under Prof. Joanna S. Fowler’s mentorship in 2015.
Below is a summary of her presentation.

Positron emission tomography (PET) is a functional imaging technique that is used for clinical diagnostic imaging, as well as research applications in healthcare, the pharmaceutical industry, and even plant physiology. In this presentation, Dr. Lee discussed her work to develop rapid methods for preparing [11C]auxin and [11C]indole via [11C]cyanation for PET imaging. Automation of the synthesis of both radiotracers was conducted so they could be used for plant PET imaging with the goal of understanding plant physiology and phenotyping. The talk also covered the work in her lab developing fundamental methodology for nucleophilic C-H radiofluorination reactions with Ag18F and K18F via metal catalyzed C-H activation reactions that enable the late-stage formation of C-18F bonds to prepare PET radiotracers and radioligands. Finally, their work to design an efficient automated route to produce [18F]ASEM, a PET radioligand for imaging of α7-nAChR in the human brain, to support their clinical research was covered.

Gordon Lecture: Deep Learning MR Reconstruction from Missing Data


Jong Chul Ye is currently KAIST Endowed Chair Professor and Professor of the Dept. of Bio/Brain Engineering and Adjunct Professor at Dept. of Mathematical Sciences of Korea Advanced Institute of Science and Technology (KAIST), Korea. He received the B.Sc. and M.Sc. degrees from Seoul National University, Korea, and the Ph.D. from Purdue University, West Lafayette.
Below is a summary of his presentation

Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative reconstruction methods in accelerated MRI problems.  However, it is still unclear why these deep learning architectures work for specific problems. Moreover, in contrast to the usual evolution of signal processing theory around the classical theories, the link between deep learning and the classical image processing approaches are not yet well understood. In this talk, Dr. Ye reviewed the recent advances of deep learning approaches for accelerated MRI and their link between compressed sensing approaches. 

In particular, Dr. Ye first reviewed the variational neural network that was first proposed in MR field,  and the popular feed-forward neural network approaches using U-Net, which can remove undersampling artifacts from the aliasing artifact corrupted image. Then, he reviewed several advanced approaches such as AUTOMAP, CascadeNet, KiKi-Net, MoDL, etc. Finally, he demonstrated that the neural network approaches can be directly implemented in k-space domain to interpolate the missing k-space data.   In order to explore the theoretical origin of the success of the neural network for accelerated MRI, Dr. Ye reviewed some of the mathematical principles that have been proposed to explain the neural networks for inverse problems, which includes unfolding, convolution framelets, etc.  Then, he introduced recent mathematical discovery of the expressivity, generalization power and optimization landscape that give us hint to understand the power of AI for accelerated MRI.