Seminar: Advancing PET Image Reconstruction with Machine Learning

09/22/2017



Dr. Jing Tang received her PhD degree in Electrical Engineering from the University of Illinois at Urbana-Champaign and had her postdoctoral training in Radiology at the John Hopkins School of Medicine. She was employed as a principle imaging physicist at the Philips Healthcare before joining the Department of Electrical and Computer Engineering at Oakland University, where she currently holds an appointment as an associate professor. She has received a number of federal awards to support her research on the development and application of PET/MR imaging techniques. Dr. Tang was the guest speaker at a lecture organized by the MGH Gordon Center. Below is her presentation’s summary.

Machine learning has shown its promises to empower medical imaging, mainly in image analysis. The talk presented Dr. Tang’s investigation of integrating machine learning techniques into the other major component of medical imaging, image reconstruction. Dr. Tang proposes to advance maximum a posteriori (MAP) PET image reconstruction using two learning based methods.

The first one is through building a dictionary learning (DL) based prior and the second by designing an artificial neural network (ANN) to enhance smoothness MAP reconstructed images from different regularizing weights. The DL-based MAP reconstruction technique forms its regularization using the sparse representation of PET images based on dictionaries trained from various images. The ANN enhancement scheme solves the regression problem of mapping between multiple reconstructed image versions and an enhanced image through learning from examples.

According to Dr. Tang, both techniques have a potential for clinical applications due to their capacity to improve conventional MAP methods in reconstructing simulated and patient PET data.

Dr. Jing Tang delivering his presentation at the MGH Gordon Center

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