Recent Seminars

MGH Gordon Lecture: Intellectual Property


The conference introduced the Partners Innovation office and discussed the process of academic technology commercialization.
The presenters discussed intellectual property, such as when an invention is patentable and how to file a patent application.

Seema Basu, Ph.D.
Director of Licensing and Strategic Collaborations
As Market Sector Leader, Seema Basu, PhD, directs the strategy for open innovation and strategic research collaborations. She leads a team responsible for enabling strategic corporate alliances and new initiatives, such as the Innovation Fellows Program for career development and collaboration with industry.  The team also manages licensing and partnering of IP portfolios from Regenerative Medicine, Ragon Institute of MGH,MIT and Harvard, and MGH Center for Global Health. She represents Partners at numerous national and regional organizations.

Heonick Ha, Ph.D.
Senior Licensing Manager 
Heonick is in the Radiology Market Sector working with the Martinos Center and other units of MGH Radiology. Heonick has managed an important portfolio of complex inventions and achieved significant outcomes while fostering enduring relationship with large global corporations. 

Brian Zamarron, Ph.D.
Licensing Manager
Brian is responsible for managing and licensing technologies arising from BWH Radiology, MGH Molecular Imaging, and MGH Nuclear Medicine. 

Seema Badu and her colleagues before their presentation on intellectual property.

Gordon Lecture: Nanoparticles in Cancer Diagnosis and Treatment


Dr. Alexandre Detappe is Instructor in Medicine at the Dana-Farber Cancer Institute and Visiting Professor at the Massachusetts Institute of Technology. Dr. Detapp was the guest speaker at a lecture organized by the MGH Gordon Center. Below is his presentation summary.

Dr. Alexandre Detappe delivering his presentation at the MGH Gordon Center

Ultrasmall nanoparticles, and more specifically silica-based gadolinium nanoparticles (SiGdNP) demonstrated their ability to act as multimodal imaging agent (PET, MRI, CT) and therapeutic agents. These nanoparticles have been originally designed to act as MRI contrast agents and radiation therapy boosters. Validated preclinically in a wide selection of animal models, SiGdNP have demonstrated to be safe, non-toxic, and highly efficient radiosensitizers. These nanoparticles are currently being tested in a Phase II clinical trial to treat brain metastases. In addition, their imaging ability makes them efficient imaging biomarkers.

Dr. Detappe has focused his efforts on developing a novel imaging biomarker for early detection of Multiple Myeloma - a blood cancer - for which it was demonstrated that an early diagnostic could significantly improve the therapeutic outcomes of the patients’ treatments. In this optic, SiGdNPs were conjugated with monoclonal antibodies to improve their specificity and avoid unwanted accumulation. However, for some patients for whom the disease already degraded their kidneys, inorganic MRI contrast agents cannot be used.

To address this challenge, Dr. Detappe and his colleagues developed a novel metal-free MRI contrast agent that offers the same quality of information than usually observed. The design of this novel polymer also allowed the easy conjugation to a large selection of drugs, in order to decrease the usually observed side effects that arise when using free drugs. As a result, we developed a novel targeted platform for multimodal imaging that can also be used as therapeutic.


Molecular Pathology in Aging and AD


Dr. Aaron Schultz, PhD, Neurology, is a multi-modal neuroimaging researcher focused on aging. He is a co-leader of the Harvard Aging Brain Study (HABS) data core and advanced imaging project, as well as the leader of the functional neuroimaging group for the A4 clinical trial. Dr. Schultz was the guest speaker at a lecture organized by the MGH Gordon Center. Below is his presentation summary.  

Dr. Shultz before his lecture at the MGH Gordon Center

Dr. Schultz discussed his recent work on post-acquisition PET measurement optimization and cross-tracer harmonization. More specifically, his presentation covered his research on aging and AD in the context of molecular pathologies of amyloid-beta and paired helical filament tau. The lecture was followed by a debate of the success and challenges of functional connectivity MRI in the context of aging, AD molecular pathology, and cognitive decline.

High Resolution PET Imaging from Mouse to Human Brain


Dr. Roger Lecomte is Professor of Nuclear Medicine and Radiobiology at Université de Sherbrooke in Canada and the Scientific Head of the Sherbrooke Molecular Imaging Center ( He developed the first PET scanner based on avalanche photodiodes (APD) and established the first animal PET imaging facility in Canada. He was the co-founder in 2002 of Advanced Molecular Imaging (AMI) Inc., manufacturing the LabPET, the first APD-based, fully-digital, commercial PET scanner distributed worldwide by GE Healthcare from 2007 to 2011. Dr. Lecomte was the guest speaker at a lecture organized by the MGH Gordon Center. Below is his presentation summary.

Dr. Roger Lecomte delivering his presentation at the MGH Gordon Center

Preclinical PET plays an important role in biomedical research by enabling in vivo investigation of molecular processes in animal models. According to Dr. Lecomte, same assays can eventually be translated into powerful diagnostic tools for guiding therapy and assessing treatment outcome in clinical trials and clinical practice. PET imaging in rodents raises special challenges due to the small size of animal organs and the sensitivity required to measure rapid dynamic processes in real time.  Dr. Lecomte has been working to address these issues through a variety of creative solutions. While the theoretical limit of spatial resolution has nearly been reached with current detector technology, further gains in resolution and sensitivity can still be foreseen in clinical PET through technological breakthroughs. One such leap forward is the use of preclinical PET detector technology for human brain imaging.  Another significant progress would be ultra-high resolution time-of-flight acquisition (~10 ps).  In Dr. Lecomte's presentation, developments of the preclinical PET instrumentation were also reviewed along with application examples.

Elmaleh Annual Lecture: Pre-Clinical and Clinical Molecular Imaging in Cancer Research


Dr. Peter Conti is a Professor at the University of Southern California with academic appointments in the Departments of Radiology, Biomedical Engineering, and Pharmaceutical Sciences. He received his medical degree from Cornell University and his Ph.D. in Biophysics from Memorial Sloan-Kettering Cancer Center. He is board certified in Nuclear Medicine (ABNM) and Diagnostic Radiology (ABR). Dr. Conti has been the Director of the USC PET Imaging Science Center since its inception in 1991. His research activities have focused on the development of novel PET and hybrid imaging agents for diagnostic and theranostic applications in cancer and other diseases.

Peter Conti presenting the Elmaleh Annual Lecture

For the Elmaleh Annual Lecture, Dr. Conti was invited to speak about how molecular imaging has become an essential component of cancer research and patient management. Various modalities constitute the field of molecular imaging, including PET, SPECT, Optical imaging, MR and others. Preclinical and clinical applications in molecular imaging are heavily dependent on the use of either exogenous or endogenous biologically relevant molecules and contrast agents. Dr. Conti spoke about how targeting of imaging agents to disease specific processes is highly relevant for development of useful diagnostic tools as well as for probing the molecular basis of disease.

Mathematical modelling of amyloid-β in Alzheimer’s disease


Alex Whittington, PhD, is a Neuro-PET R&D scientist at Invicro LLC. His research focuses on using mathematical modelling of PET data to better understand amyloid and tau accumulation in neurodegenerative diseases. 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. Whittington.

Dr. Whittington delivering his presentation at the MGH Gordon Center

Neuritic plaques formed primarily of amyloid-β (Aβ) are one of the two pathological hallmarks of Alzheimer’s disease (AD) and can be non-invasively imaged and quantified in vivo using Aβ-positron emission tomography (PET). Imaging studies over the last decade have shown a consistent spatial accumulation pattern of Aβ in AD.

Spatiotemporal modelling of Aβ-PET imaging data can be used to provide evidence that Aβ accumulation starts in all brain regions simultaneously and that its spatiotemporal distribution is a result of heterogeneous regional carrying capacities (regional maximum possible concentration of Aβ) for the aggregated protein rather than longer term spreading from seed regions.

Further, using this modelling process a novel sensitive imaging outcome measure, AβL can be derived which accurately quantifies the Aβ burden for an individual Aβ-PET scan. In future Aβ imaging studies, using AβL will substantial increase in power over currently employed quantification methods.

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.

Data Analytics in Operations Management


Dr. Oleg S. Pianykh is the Director of Medical Analytics at the Department of Imaging, Massachusetts General Hospital, and Assistant Professor at Harvard Medical School. With academic background in applied computer science, He has been actively working in the field of innovative healthcare for the past 20 years. His scientific work ranged from research on digital imaging and data-driven clinical workflow to publishing and teaching advanced graduate courses at Harvard and other leading universities. On the applied side, Dr. Pianykh has served as a CIO for a state-wide healthcare network, and participated in many consulting/startup initiatives. His current interests include bid data analysis, operations management, and information technology in healthcare.
Dr. Pianykh was the guest speaker at a lecture organized by the MGH Gordon Center. Below is his presentation’s summary.

With medical technology becoming increasingly complex, data volumes – increasingly high, and expected outcomes – more demanding, the cost of medical errors, processing delays and guesswork becomes prohibitively high. To deal with these challenges, contemporary radiology has to learn how to use its data to produce optimal decisions and operational strategies. Transforming radiology data into the most effective and objective problem solver was the main idea behind the Medical Analytics Group (MAG) project, launched by the Department of Radiology at Massachusetts General Hospital. The principal purpose of MAG is to apply data science to routine problems, looking for the best possible solutions. Their current projects include identifying hidden operational bottlenecks, building predictive workflow models, developing optimal scheduling strategies, analyzing utilization and productivity limits, studying processing quality and satisfaction, and many more: MAG work is driven by current needs, not limited to any particular domain. All MAG projects have to be implemented in real clinical environments; all have to be verified and proven to work with objective data analysis. In this presentation Dr. Pianykh shared the Medical Analytics Group's most interesting results, important successes, and thought-provoking challenges.

Dr. Pianykh delivering his presentation at the MGH Gordon Center