Archive for April, 2018

FDA Approves First Use of Tracer-QC at Gordon Center


Daniel Yokell working at the PET production facility of the MGH Gordon Center

Radiopharmaceutical company Trace-Ability, Inc. announced that the FDA has approved the first use of its solution, Tracer-QC, for release testing Ammonia N-13 Injection at the MGH Gordon Center PET Core. Tracer-QC automates the positron emission tomography (PET) tracer release testing process, which the company says is known for its complexity.

“Despite the clear value of Tracer-QC confirmed by PET drug manufacturers, there has been some reluctance to adopt the technology due to its fundamental novelty and lack of precedent with the FDA,” Arkadij Elizarov, CEO of Trace-Ability, said in a prepared statement. “We appreciate the eagerness with which the MGH team participated in this project, which led to the first FDA approval of Tracer-QC use today.”

“Our goal is to help the industry transition to a more streamlined PET drug production and quality control workflow," Daniel Yokell, associate director for radiopharmacy and regulatory affairs at the Gordon Center for Medical Imaging at MGH, said in the same statement. “In turn we can hopefully expand patient access to these critical diagnostic procedures outside of large academic medical centers.”

Rethinking Convolutional Neural Networks (CNNs)


Dr. C.-C. Jay Kuo is a University of Southern California Distinguish Professor and Directory of its Media Communications Laboratory. Dr. Kuo received his Ph.D. from MIT in 1987. He has served as editor for 12 international journals and co-authored around 250 journal papers, 900 conference papers and 14 books. He was the guest speaker at a lecture organized by the MGH Gordon Center. Below is the presentation summary provided through the courtesy of Dr. Kuo.

Dr. Kuo delivering his presentation at the mgH Gordon Center

The superior performance of Convolutional Neural Networks (CNNs) has been demonstrated in many applications such as image classification, detection and processing. Yet, the CNN solution has its own weaknesses such as robustness against perturbation, scalability against the class number and portability among different datasets. Furthermore, CNN’s working principle remains a mystery. In this talk, Dr. Kuo first explained the reasons behind the superior performance of CNNs. Then, he presented an alternative solution, which is motivated by CNNs yet allows rigorous and transparent mathematical treatment, based on a data-driven Saak (Subspace approximation with augmented kernels) transform. The kernels of the Saak transform are derived from the second-order statistics of inputs in a one-pass feedforward way. Neither data labels nor backpropagation is needed in kernel determination. The pros and cons of CNNs and multi-stage Saak transforms were compared.

Three Research Fellows promoted to Instructor


We are pleased to announce that three Research Fellows in the Gordon Center have been promoted to the title of Instructor. Congratulations to Kai Bao, Fangxu Xing, and Gengyang Yuan!