Engineering and Medicine Seminars Series

Join us for our seminars, where we ask prominent researchers in their fields to share their work with us.

Join on Zoom
Join on Zoom

Previous Seminars

AEIT Seminars 2025-2026

Below you can see the list of the past BMEII Seminars, which were part of the AIET Seminar during the academic year 2025-2026.

"The Danish Precision Health Initiative (DELPHI) - A dynamic platform for personalized health innovation" by Ruth Loos, PhD

 

Bio

Ruth Loos, PhD is a leading researcher in the genetics of obesity and metabolic health, currently serving as Vice Executive Director and Professor at the Novo Nordisk Foundation Center for Basic Metabolic Research at the University of Copenhagen. Her work focuses on identifying genetic and environmental determinants of body weight regulation, contributing to major gene-discovery efforts through the GIANT consortium. She also advances precision health approaches by integrating genetic, clinical, and lifestyle data to improve prediction, prevention, and treatment of obesity. Previously, she held multiple leadership roles at the Icahn School of Medicine at Mount Sinai, where she directed research on obesity and metabolic traits.

Passcode: +g*5R$.n

"Cleansing the brain: Anatomy, physiology, and MRI of the cerebral waste clearance system" by Merel van der Thiel, PhD

Abstract

The brain’s waste clearance system (e.g., glymphatic system) is a recent anatomical discovery, identified only about a decade ago. Its impairment has been linked to ageing and neurodegenerative diseases such as Alzheimer’s disease, underscoring the need to better understand its mechanisms and to develop non-invasive imaging methods for studying cerebral waste clearance in humans.

This talk will begin with an overview of the components comprising the cerebral waste clearance system and current theories regarding waste efflux. It will continue to discuss key physiological drivers and their influence on waste clearance, with a particular focus on the role of sleep. Finally, MRI studies examining differences in waste clearance between sleep and wakefulness will be presented.

Bio

Dr. Merel van der Thiel developed a strong interest in neurodegenerative diseases during her bachelor’s degree in Psychology (Cognitive Neuroscience track) at Leiden University, the Netherlands. This interest led her to pursue two MSc degrees with distinction: first in Neuroscience in Geneva, Switzerland, followed by a specialized MSc program in Dementia at University College London (UCL).

She subsequently obtained her PhD at Maastricht University, the Netherlands, where she specialized in advanced MRI methods to quantify components of the cerebral waste clearance system in relation to Alzheimer’s disease. She was awarded an Early Career Grant from the Dutch Alzheimer Society to continue her postdoctoral research at Maastricht University, focusing on autonomic influences on cerebral waste clearance.

Dr. van der Thiel is currently a visiting researcher at the Massachusetts Institute of Technology (MIT), USA, in the laboratory of Dr. Laura Lewis, where she investigates interstitial fluid movement using diffusion MRI during wakefulness and sleep.

Passcode: S.1GNmsW

"Towards Intelligent Wearable Assistants for Everyday Physical Interaction" by Yiyue Luo, PhD

Abstract

Everyday life is shaped by continuous physical interactions, touching, grasping, and moving, that underlie human behavior, health, and well-being. Intelligent wearable assistants aim to capture and enhance these interactions by seamlessly sensing, modeling, and responding to the body’s physical dynamics, providing clinicians and patients with actionable insights that were previously inaccessible. In this talk, I will present a series of textile-based systems that move beyond passive sensing to demonstrate the potential of intelligent wearables as everyday assistants. Examples include machine-knitted tactile garments that sense and guide motion, adaptive embroidered gloves for interactive tasks, and intelligent carpets that monitor whole-body activities. Together, these prototypes illustrate how textiles can serve as scalable and versatile platforms for building intelligent assistants that integrate into daily routines, bridging human physical interactions with computational intelligence. Together, these prototypes highlight how textiles, an everyday material, can become a powerful platform for intelligent health technologies, opening new pathways for prevention, early detection, and personalized rehabilitation.

Bio

Yiyue Luo is an Assistant Professor at University of Washington ECE. She received my Ph.D. degree in EECS from MIT in 2024, advised by Professor Wojciech Matusik and Professor Tomas Palacios. Before joining MIT, she received my B.S. in Materials Science and Engineering at University of Illinois Urbana-Champaign, working with Professor John A. Roger. Her research lies at the intersection of digital fabrication, human-computer/robot interaction, and applied AI. Her research on integrated intelligent textiles has been published in interdisciplinary journals, e.g., Nature Electronics and Nature Communications, top human-computer interactions, robotics and learning venues, e.g., CHI, UIST, CVPR, ICRA. Her work has been selected as the cover of Nature Electronics, awarded Best Paper at CHI, Best Paper Honorable Mention at UIST, featured in prominent media outlets, and invited to public museums and world congress exhibitions. She has been supported by fellowship from MathWorks, Google, and Accenture, and was recently listed as 30 under 30 North American 2024. She has been awarded funding from the UW Royal Research Fund and the CoMotion Entrepreneurship Grant.

Passcode: Na4KS+1p

"CEST MRI for Molecular Brain Imaging and AI-Driven Analysis" by Jianpan Huang, PhD

Abstract

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a non‑invasive molecular imaging technique that enables in vivo detection of molecules containing exchangeable protons. It has been applied to visualize molecular changes in a range of neurological disorders, including brain tumors, Alzheimer’s disease, and multiple sclerosis. Over the past decade, CEST MRI has gained traction in numerous preclinical and clinical imaging centers worldwide. Despite its promising potential, widespread clinical translation of CEST MRI is hindered by challenges such as complex quantification methods and limited spatial resolution. Artificial intelligence (AI) in medical imaging is a rapidly evolving field that leverages neural networks to enhance, analyze, and interpret complex datasets. This talk will introduce the basic principles of CEST MRI and review its applications in imaging neurological diseases. It will also highlight recent advances in applying AI to CEST MRI, including techniques for streamlining molecular quantification and improving image quality. AI-assisted CEST MRI has the potential to improve diagnostic accuracy and support more informed clinical decision‑making.

Bio

Dr. Jianpan Huang is an assistant professor in the Department of Diagnostic Radiology and deputy director of the Tam Wing Fan Neuroimaging Research Laboratory at the Li Ka Shing Faculty of Medicine, The University of Hong Kong. He obtained his Bachelor’s and Master’s degrees from Xiamen University and Ph.D. from City University of Hong Kong. In 2022, Dr. Huang was named a Junior Fellow of the International Society for Magnetic Resonance in Medicine (ISMRM). He also received the Young Investigator Award from the Overseas Chinese Society for Magnetic Resonance in Medicine (OCSMRM) in 2020 and from the International Chemical Exchange Saturation Transfer (CEST) Workshop in 2018.

Dr. Huang has published around 50 peer-reviewed jounal papers in leading international journals, including Science Advances, Nature Communications, JMRI, MRM, and IEEE JBHI, and holds nine granted patents. He serves as a reviewer for over 20 journals, including JMRI, MRM, European Radiology and Comunications Physics, and also a grant reviewer for UK Research and Innovation and the Israel Science Foundation. His current research focuses on magnetic resonance imaging (MRI), chemical exchange saturation transfer (CEST) MRI, artificial intelligence (AI), and neuroscience.

Passcode: s3g$^YWV

"Multicentre Standardisation of Renal MRI" by Sue Francis, PhD

Abstract

This talk will outline developments on multicentre renal MRI data acquisition and analysis showing examples from the UKRIN-MAPS (MRI Acquisition and Processing Standardisation) project which aimed to develop harmonized renalMR protocols across vendors. Results of the repeatability of the UKRINMAPS multi-parametric protocol will be shown using a “travelling kidney” study, scanning healthy subjects across multiple sites. Analysis pipelines for automated analysis including AI segmentation methods will be shown. The talk will demonstrate the application of this x-vendor protocol to the AFIRM study which has collected renal MRI data in Chronic Kidney Disease patients at baseline and 2 years.

Bio

Prof Sue Francis is a physicist with 25 years’ experience on developing MRI methods for biomedical applications and translation. She works across the brain and body and across MR field strengths. Since 2005, she has led a programme of work exploiting the capabilities of functional and anatomical high and ultra-high field MRI in neuroscience, and is currently involved with the development of the 11.7T human National Facility in Nottingham which is due to be at field in 2027. In 2009, she developed a programme of imaging in quantitative imaging of the body, and leads work at Nottingham on the development of MRI methods to study renal and liver function. She initiated the UK Renal Imaging Network (UKRIN) and led the UKRIN-MAPS MRC Partnership grant to develop a harmonized approach in renal MRI across MR vendors. Her research interests include the development and application of multiparametric quantitative MRI methods, with a particular interest in Arterial Spin Labelling (ASL) methods to non-invasively assess tissue perfusion in the brain, kidney and liver. She has published widely on the application of renal MRI to study physiological modulations and disease pathophysiology.

Passcode: !QmWV2n8

"Profiling liver pathway through biomechanics, Quantifying response to therapy, and “Seeing” vascular architecture" by Ralph Sinkus, PhD

Abstract

Magnetic Resonance Elastography (MRE) has emerged as a powerful non-invasive modality to quantify tissue biomechanics, extending the clinical intuition of manual palpation into a quantitative, spatially resolved imaging biomarker. In this work, we present recent advances in gravitational transducer–based 3D MRE and demonstrate how full complex shear modulus measurements enable refined characterization of liver disease pathways, assessment of therapeutic response in oncology, and inference of underlying vascular architecture.

A robust gravitational MRE (gMRE) driver, based on an eccentric rotating mass, was developed to deliver stable, harmonic-free mechanical excitation with high efficiency. Following its translation from an EU Horizon 2020 research project into an FDA-approved clinical product, the technology enables reproducible whole-organ 3D MRE acquisitions integrated into standard clinical MRI workflows. Unlike conventional 2D MRE, which reports only the magnitude of the complex shear modulus |G*|, 3D MRE provides access to both stiffness and wave attenuation (phase angle), yielding enhanced sensitivity to microstructural changes.

In the liver, we show that 3D MRE identifies a pre-fibrotic biomechanical niche characterized by reduced shear wave attenuation despite only modest increases in stiffness, consistent with early collagen III deposition from activated hepatic stellate cells. This biomechanical signature precedes conventional fibrosis markers and correlates with biochemical tests and histology, enabling early detection of liver damage and identification of mechanical states associated with increased hepatocellular carcinoma (HCC) risk. Beyond hepatology, we demonstrate the value of MRE for early response assessment to neoadjuvant chemotherapy in breast cancer, where collagen remodeling following initial treatment cycles predicts therapeutic outcome with higher precision than current imaging standards.

Finally, leveraging wave dispersion induced by multiple scattering, we illustrate how macroscopic MRE measurements can probe microscopic vascular architecture, bridging several orders of magnitude in scale and opening new avenues for non-invasive characterization of tumor microenvironments and treatment response. Together, these results position advanced 3D MRE as a versatile, physics-informed biomarker platform for precision medicine across liver disease and oncology. 

Bio

I am a physicist with a background in high-energy and nuclear physics as well as MRI, leading a translational research team in Paris (University Paris Cité, Sorbonne Paris Cité, Bichat/Beaujon Hospitals). My career spans both academia and industry. During my PhD at DESY (Hamburg, Germany), I worked on quantum electrodynamics/chromodynamics and developed a neural-network system for electron identification in particle collisions. I then joined Philips Medical Systems, focusing on MRI and MR-elastography.

In 2004, I transitioned to academia, establishing an MRI group at ESPCI Paris and collaborating with Supersonic Imaging. I secured a permanent CNRS research director position in 2007 and developed a multidisciplinary program integrating work at molecular, animal, and clinical scales.

In 2013, I became Chair in Biomedical Engineering at King’s College London. With collaborators in modeling and oncology, we led a Horizon 2020 project to measure tumour forces as indicators of therapy response. This research led to a product developed by QED and adopted by Siemens.

Our teams in London and Paris now focus on quantifying tissue biomechanics across organs and scales—from liver, breast, and brain to neuronal and organoid systems. In Paris, we address critical questions in liver oncology: identifying precancerous niches, predicting therapy response, and developing imaging biomarkers such as tumour pressure and tissue stiffness. Our clinical partnerships enable direct hypothesis testing at the patient level.

Passcode: 63YrFEP!

"Actuator-Enhanced Imaging and Diagnostics for Personalized Healthcare and Physiological Modeling" by June Ueda, PhD

Abstract

This talk presents recent advancements in actuator-enhanced imaging and diagnostics for personalized physiological modeling, with a focus on Magnetic Resonance Elastography (MRE) and human motor system identification. Building on the concept of digital twins in healthcare, the research explores how mechanical perturbations—delivered via robotic and motion control platforms—can improve the accuracy of individualized models for both organ diagnostics and neuromuscular control. Emphasis will be placed on system identification techniques, including the generation of mechanical perturbations optimized for upper limb impedance estimation using spectral flatness criteria. The talk will also highlight innovations in actuator design, such as tunable-frequency piezoelectric systems for spinal disc imaging and MRI-compatible robotic dosimeters for implant safety assessment. Key applications include the development of automated quality control systems for MRE using deep learning to assess diagnostic image quality and enhance liver stiffness measurement. These technologies collectively advance the reliability, efficiency, and personalization of physiological diagnostics and therapeutic planning.

Bio

Dr. Jun Ueda is a Professor in the G. W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology. He received his B.S., M.S., and Ph.D. degrees in Mechanical Engineering from Kyoto University, Kyoto, Japan, in 1994, 1996, and 2002, respectively. From 1996 to 2000, he was a Research Engineer at the Advanced Technology Research and Development Center of Mitsubishi Electric Corporation in Japan. He served as an Assistant Professor at the Nara Institute of Science and Technology, Japan, from 2002 to 2008. From 2005 to 2008, he was also a visiting scholar and lecturer in the Department of Mechanical Engineering at the Massachusetts Institute of Technology. He joined the faculty at the Georgia Institute of Technology as an Assistant Professor in 2008 and was the Director of the Robotics Ph.D. Program at Georgia Tech from 2015 to 2017. He also served as the Chair of the Editorial Board for the IEEE International Conference on Advanced Intelligent Mechatronics (AIM) and as the General Chair for the 2023 IEEE/SICE International Symposium on System Integration (SII). Dr. Ueda is currently a Senior Editor for the IEEE/ASME Transactions on Mechatronics. His recognitions include the Fanuc FA Robot Foundation Best Paper Award in 2005, the IEEE Robotics and Automation Society Early Academic Career Award in 2009, the Advanced Robotics Best Paper Award in 2015, and the Nagamori Award in 2021. He is a Fellow of American Society of Mechanical Engineers (ASME).
Passcode: ym+8e4^.

"Metabolic MRI: Emerging Technologies for Clinical Translation" by Daniel Paech, MD, PhD, MSc

Abstract

Metabolic imaging technologies have the potential to transform the field of diagnostic radiology by offering novel insights into tissue function and disease. These technologies, including advanced MR spectroscopy, chemical exchange saturation transfer (CEST) MRI, and X nuclei imaging, enable non-invasive characterization of metabolic processes in vivo. This presentation will discuss recent advances in metabolic MR imaging, their clinical applications in diagnosing and monitoring diseases, and the challenges in translating these methods from research to practice. Highlights will include applications in neuro-oncology and neurodegenerative diseases. The role of new MR technologies, such as ultra-high field strength and high-performance gradient systems, will also be highlighted for their potential to further enhance metabolic imaging capabilities.

Bio

Daniel Paech, MD, PhD, MSc, is an Associate Professor of Radiology at Harvard Medical School and Medical Director of the Brigham Research Imaging Core and the Mass General Brigham (MGB) Ultra-High-Field MRI Center. He is a board-certified radiologist and neuroradiologist (diagnostic and neuro-interventional) and serves as attending neuroradiologist in the Division of Neuroradiology at MGB.

Dr. Paech’s research focuses on the development and clinical translation of novel imaging technologies, with a particular emphasis on ultra-high-field MRI. His expertise is in metabolic imaging, with applications in neuro-oncology, neurodegenerative disease, and ischemic stroke.

Seminar Recording

Passcode: O9T01G@q

"The Case of the Hidden Signatures: Designing Imaging AI to Bridge Patterns, Predictions & Precision Medicine" by Satish E. Viswanath, PhD

Abstract

Developing artificial intelligence (AI) schemes to assist the clinician towards enabling precision medicine approaches requires development of objective markers that are predictive of disease response to treatment or prognostic of longer-term patient survival. The solutions being developed in my group in this regard involve designing computational imaging features together with histology or molecular data for detailed tissue and disease characterization in vivo as well be associated with patient outcomes. The key innovation in this approach lies in “handcrafting” unique tools that can capture biologically relevant and clinically intuitive measurements from routinely acquired imaging (MRI, CT, PET) or digitized images of tissue specimens. Further, by conducting cross-scale associations between imaging, pathology, and -omics, we can not only “unlock” and integrate the information captured by these different, disparate data modalities but also develop an interpretable and intuitive understanding of what drives their performance. Specific problems addressed via the new computerized imaging markers we have developed include: (a) predicting response to treatment to identify optimal therapeutic pathways, as well as (b) evaluating therapeutic response to guide follow-up procedures. We will further examine how to account for differences between sites, scanners, and acquisition parameters to ensure generalizable performance of AI tools and computational imaging features; crucial for wider clinical translation and widespread adoption. These will be discussed in the context of clinical applications in colorectal and renal cancers, digestive diseases, as well as pediatric conditions.

Bio

Dr. Viswanath is an Associate Professor in the Departments of Pediatrics and Biomedical Engineering at Emory University, since Fall 2025. He is also a Research Scientist & Biomedical Engineer at the Cleveland VA Medical Center. Previously, he was an Associate Professor of Biomedical Engineering at Case Western Reserve University. The primary focus of his research has been developing new artificial intelligence (AI) approaches including image analytics, radiomics, and machine learning schemes; applied to problems in computer-aided diagnosis & detection, disease characterization, as well as quantitative evaluation of response to treatment; in gastrointestinal cancers and digestive diseases. He has authored over 55 peer-reviewed journal publications, over 120 conference papers & abstracts, 1 book chapter, as well as delivered over 90 invited talks and panel discussions both in the US and abroad. He has 10 issued patents in the areas of medical image analysis, computer-aided diagnosis, and pattern recognition. Dr. Viswanath is an Associate Editor or Editorial Board Member for 9 leading international peer-reviewed journals, serves as Program Committee Member or Area Chair for 3 major medical imaging conferences, and has been elected to Senior Member in the National Academy of Inventors, the IEEE, and the SPIE. He has been selected for the Fulbright Specialist Award, in addition to multiple awards from SIIM, SPIE, and Crain’s Cleveland Business. His lab’s research has been funded since 2016 through the DOD/CDMRP, the NIH (NCI, NIBIB, NINR, NHLBI), the VA, and the State of Ohio.

Seminar Recording

Passcode: hbNGs6&8

"AI-powered Diagnosis and Prognosis of Musculoskeletal Diseases" by Cem M. Deniz, PhD

Abstract: 
In this presentation, we examine the transformative impact of artificial intelligence and advanced imaging in diagnosing and predicting musculoskeletal diseases. We highlight two key conditions: osteoporosis of the hip and knee osteoarthritis. Our research shows how advanced MRI techniques, combined with deep learning models, can offer more accurate and efficient methods for assessing fracture risk and predicting disease progression compared to traditional clinical approaches. Specifically, we demonstrate the power of multimodal data fusion by integrating imaging with clinical data to improve predictive accuracy. We share key findings on using 3D convolutional neural networks (CNNs) and Vision Transformers for better anatomical segmentation and classification. The presentation will emphasize the potential of these AI-driven methods to personalize patient care and ultimately reshape the future of musculoskeletal medicine.
Bio: 
Dr. Cem M. Deniz is an Associate Professor of Radiology at the New York University Langone Health. His research focuses on integrating technical developments in deep learning with diagnostic radiology to fulfill clinical needs by identifying imaging biomarkers for musculoskeletal disorders. He develops deep learning models and software pipelines for predicting the progression of osteoarthritis patients. Dr. Deniz holds a Ph.D. in Biomedical Imaging from New York University. 
Passcode: @rQkA3Pm

"AI-enabled precision medicine for cancer research and innovations in medical devices" by Gautham Pasupuleti

Abstract
Advances in artificial intelligence are rapidly transforming precision medicine, opening unprecedented opportunities for earlier cancer detection, accurate risk stratification, targeted therapeutics, and accelerated drug discovery. This talk will explore the development and clinical translation of an AI-enabled precision medicine platform designed for oncology integrating multi-omics data, imaging, and electronic health records to enable early diagnosis, personalized treatment recommendations, and novel biomarker discovery.
We will discuss the AI-precision medicine platform’s architecture, including genomic and transcriptomic data integration, biomarker identification pipelines, predictive modeling for treatment response, and AI-assisted clinical decision support. Real-world examples in lung, breast, and prostate cancer will illustrate how these technologies can guide patient-specific therapeutic strategies and improve outcomes.
Finally, we will address the critical challenges facing AI in oncology including data heterogeneity, bias mitigation, regulatory considerations, and integration into clinical workflows and outline future directions in AI-guided drug development, adaptive clinical trials, and precision public health. The presentation will combine technical insights with translational perspectives, offering a roadmap for how engineering, data science, and medicine can converge to reshape cancer care.
Bio
Gautham Pasupuleti is an entrepreneur and innovator in the healthcare and medical technology sector. He is the Founder and CEO of Biodesign Innovation Labs, a company focused on AI-enabled precision medicine for early diagnosis and treatment of diseases like cancer, as well as the development of medical devices for critical care and respiratory support.
He has worked on AI-driven healthcare solutions, including a precision medicine platform for oncology, and has developed RespirAID and ResPAP, medical devices designed for emergency and pediatric respiratory care. His work spans AI in healthcare, bioengineering, medical devices, and healthcare innovation.
He has also been involved in teaching, research collaborations, and entrepreneurship, engaging with universities and institutions to advance innovation in healthcare. He has presented at global conferences, been part of fellowships at Halcyon, Global Good Fund, Johns Hopkins University and Medicine and received many grants, awards, patents, published research work in medical devices, AI and healthcare and is working on expanding his company to the U.S. market.

"Quantitative MRI of Adipose Oxygenation and Hypertrophy in Type 2 Diabetes" by Scott C Beeman, PhD

Abstract
This talk outlines a quantitative MRI framework to measure two hypothesized drivers of insulin resistance, including adipose hypoxia and adipocyte hypertrophy, both non-invasively and longitudinally. First, oxygen-sensitive fat relaxometry leverages the dependence of proton R₁ in lipids on dissolved O₂ to estimate tissue pO₂; I’ll cover phantom calibration, preclinical validation against invasive probes, and feasibility in human subcutaneous and visceral depots with test–retest precision and regional mapping. Second, time-dependent diffusion MRI of lipid resonances, interpreted with appropriate models of diffusion, yields indices sensitive to lipid-droplet (and by extension adipocyte) size distributions. I will highlight acquisition and modeling choices that are appropriate for lipid systems. Across proof-of-concept studies, lower adipose oxygenation and larger cell size align with markers of insulin resistance and inflammatory signaling. Together, these biomarkers show promise for the non-invasive, longitudinal study of the pathogenesis of type 2 diabetes.
Bio
My laboratory develops quantitative magnetic resonance (MRI and NMR) methods to measure tissue microstructure and metabolism in vivo. Specifically, our team integrates advanced diffusion MRI signal modelling, deuterium (2H) metabolic MRI, and oxygen sensitive MRI techniques to study neurodegenerative, metabolic, and oncologic disease. These tools form the technical foundation of a current NCI R37 MERIT award on invasive glioma subtypes and funded studies in Alzheimer’s disease, tumor metabolism, and type 2 diabetes. Central to my research program is trainee mentorship; current and past primary trainees include three post-doctoral fellows, eight PhD students, and numerous master’s and undergraduate researchers, several of whom have earned competitive fellowships and awards.
Passcode: A7diY!JS

"PET and MRI for a Holistic View of Glymphatic Function" by Chuan Huang, PhD

Abstract

The glymphatic system is increasingly recognized as a critical pathway for brain waste clearance, with implications for aging and neurodegenerative diseases. However, imaging glymphatic function in humans remains a major challenge due to the system’s complex, multiscale dynamics. In this talk, I will present a multimodal imaging approach using PET and MRI to achieve a more comprehensive and clinically translatable assessment of glymphatic function. 

I will review the limitations of current MRI-based methods—including phase-contrast MRI, DWI, and DTI-ALPS—which primarily infer fluid motion indirectly and often lack sensitivity to the slow and heterogeneous flow patterns characteristic of glymphatic transport. In contrast, PET imaging enables direct quantification of solute clearance over time, offering a complementary view of net mass transport. I will highlight recent work using dynamic 18F-FDG PET to quantify ventricular CSF clearance and demonstrate its sensitivity to age-related decline, test–retest reliability, and independence from brain metabolism.

Bio

Dr. Chuan Huang is an Associate Professor of Radiology and Imaging Sciences and the Director of PET-MRI Research at Emory University School of Medicine. He was named a Distinguished Investigator by the Academy for Radiology & Biomedical Imaging Research in 2023.

Dr. Huang’s research focuses on PET/MR neuroimaging and its broader clinical and translational applications. He has authored over 70 peer-reviewed publications and holds three patents. Prior to joining Emory in November 2022, he was a tenured Associate Professor of Radiology and Psychiatry at the State University of New York at Stony Brook. He completed his postdoctoral training at Massachusetts General Hospital and Harvard Medical School in 2014, where he was subsequently promoted to Instructor.

He previously served as Chair of the ISMRM PET/MR Study Group and was a member of the organizing committee for the 2023 SNMMI–ISMRM co-sponsored PET/MR Workshop. He currently serves on the committee of the ISMRM MR in Psychiatry Study Group.

Seminar Recording

Passcode: ^DZq9$*6

"The MRI RF Coil Array Data Hub: From Imaging to Satellite Connectivity" by Dean Darnell, PhD

Abstract

All MRI scanners must have an RF coil or array for transmitting/receiving the RF signal during imaging regardless of field strength or magnet design. In this talk, we will explore the next generation of multi-purpose integrated RF/wireless (iRFW) coil designs, which can perform simultaneous MR signal reception and wireless data transfer by allowing RF currents at the Larmor frequency and in a wireless communication band(s) to flow on the same conductor. These coils can wirelessly transmit MRI images, enable wireless MRI clock synchronization via Global Navigation Satellite System (GNSS) signals from satellite atomic clocks, and support wireless ultrasound-based respiratory monitoring from within the bore to the scanner console or cloud. In low-field applications, the iRFW coil design, termed an iRFW-Cellular coil design, can further enable wireless data transmission of image data over cellular or satellite networks while imaging. In total, the RF coil array is not only essential for imaging but a natural data hub for wirelessly moving data out of the scanner bore.

Bio

Dr. Darnell earned his PhD in physics in 2005 and spent eight years at Apple Inc. designing antenna systems for multiple iPhone generations. Since joining Duke University’s Brain Imaging and Analysis Center in 2013, his research has focused on novel RF coil technologies for MRI, including wireless coil designs, MR-compliant in-bore computing, and wireless transmission for portable low-field MRI. His current funded projects include developing a flexible wireless coil array for neonatal imaging and a cloud based, platform independent wireless MRI system.

Seminar Recording
Please contact us if you would like to access the recording.

AEIT Seminars 2024-2025

Below you can see the list of the past BMEII Seminars, which were part of the AIET Seminar during the academic year 2024-2025.

"Imaging Alzheimer’s Disease: Real-time interrogation of Entorhinal-Hippocampal circuit breakdown" by Jae-eun Kang Miller, PhD

Abstract:
The entorhinal cortex (EC) is ground zero for Alzheimer’s disease (AD), showing early signs of tauopathy and neuroinflammation, which then spread through the entorhinal-hippocampal (EC-HPC) circuit. There are major gaps in knowledge regarding why the EC is vulnerable early in AD, and what drives disease progression from the EC to the HPC. We applied an imaging approach to image EC layer 2 projection neuron activity in vivo with 2-photon microscopy in models of tauopathy (Tau P301S mouse, PS19). We discovered EC layer 2 projections are profoundly hyperactive. This is important because neural activity causes the release of toxic forms of tau and other disease associated proteins. Thus, hyperactivity in EC projection neurons may accelerate disease. We also imaged cells in the dentate gyrus, a major target of EC layer 2 projection neurons, using the same imaging platform and found neuronal hyperactivity in dentate gyrus cells as well. Based on the findings from these imaging approaches, we propose a model that early neuronal hyperactivity in EC layer 2 projection neurons drives disease progression and cognitive decline in AD and related tauopathies.
Bio: 
Dr. Jae-eun Kang Miller is an Assistant Professor in the Department of Psychiatry at Columbia University. She is an affiliate member of the Columbia Zuckerman Institute and a member for the Columbia Doctoral Program in Neurobiology and Behavior.
Dr. Miller moved from South Korea to the US for her graduate studies at Washington University. As a graduate student in David Holtzman’s lab, she discovered that the Alzheimer’s disease associated peptide amyloid beta is regulated by sleep. Many researchers have built on this initial discovery, and the field recognizes the sleep disruption early in life as a potential risk factor for Alzheimer’s disease. During her postdoctoral studies, Dr. Miller shifted to in vivo imaging approaches with the goal of ultimately applying this to Alzheimer’s disease. Working with Erik Herzog and Timothy Holy, Dr. Miller used in vivo imaging to discovery how the olfactory system alters its responsiveness across the circadian cycle. She then moved to Rafael Yuste’s lab at Columbia and used pioneering approaches in 2-photon imaging and optogenetics to investigate the role of spontaneous activity in the primary visual cortex. Visual stimuli elicit activity of specific neuronal ensembles, and Jae-eun discovered that these same ensembles are active together spontaneously in the absence of any visual input. This suggests that sensory input recruits intrinsically generated cortical ensembles. This challenges the traditional view that cortical responses in primary sensory areas are built in a bottom-up fashion from sensory input. Dr. Miller went on to show how this spontaneous activity is altered over the course of learning.
As a principal investigator at Columbia, Dr. Miller is now applying advanced approaches in in vivo imaging and circuit manipulation to investigate the pathophysiology of neuropsychiatric illnesses. 
Recording not available.

"Wearable Ultrasound Technology" by Sheng Xu, PhD

Abstract

The use of wearable electronic devices that can acquire vital signs from the human body noninvasively and continuously is a significant trend for healthcare. The combination of materials design and advanced microfabrication techniques enables the integration of various components and devices onto a wearable platform, resulting in functional systems with minimal limitations on the human body. Physiological signals from deep tissues are particularly valuable as they have a stronger and faster correlation with the internal events within the body compared to signals obtained from the surface of the skin. In this presentation, I will demonstrate a soft ultrasonic technology that can noninvasively and continuously acquire dynamic information about deep tissues and central organs. I will also showcase examples of this technology’s use in recording blood pressure and flow waveforms in central vessels, monitoring cardiac chamber activities, and measuring core body temperatures. The soft ultrasonic technology presented represents a platform with vast potential for applications in consumer electronics, defense medicine, and clinical practices.

Bio

Dr. Sheng Xu is a Professor and Jacobs Faculty Scholar at UC San Diego. He earned his B.S. degree in Chemistry from Peking University and his Ph.D. in Materials Science and Engineering from the Georgia Institute of Technology. Subsequently, he pursued postdoctoral studies at the Materials Research Laboratory at the University of Illinois at Urbana-Champaign. His research group is interested in developing new materials and fabrication methods for soft electronics, with a particular focus on wearable ultrasound technology. His research has been presented to the United States Congress as a testimony to the importance and impact of funding from the National Institutes of Health. He has received numerous honors, including the NIH Maximizing Investigators’ Research Award, NIH Trailblazer Award, Sloan Fellowship, IEEE EMBS Technical Achievement Award, ETH Zürich Materials Research Prize for Young Investigators, MRS Outstanding Early Career Investigator Award, and a finalist of the Blavatnik National Awards for Young Scientists. He is a Fellow of AIMBE and IEEE.

Seminar Recording

Passcode: 295FIim%

"Talking to the Body via Conformable Decoders" by Canan Dagdeviren, PhD

Abstract

Multifunctional sensing capability, ‘unusual’ formats with flexible/stretchable designs, lightweight construction, and self-powered operation are desired attributes for electronics that directly interface with the human body. Today’s electronics are stiffer by up to six orders of magnitude compared to soft tissue. Thus, present systems limit intimate integration with biology. I have focused on novel microfabrication techniques and tricks to use active piezoelectric materials and required electronic components, which have the shape and the mechanical properties that match with those of human tissues, in order to allow intimate integration without any irritation and/or harm to the body.

In this talk, I describe novel materials, mechanics and device designs for emerging classes of wearable health monitoring systems and implantable, minimally invasive medical devices. These include a variety of electrodes, sensors, and energy harvesting components, with promising applications in bio-integrated electronics, such as self-powered cardiac pacemakers, wearable blood pressure sensors, modulus sensor patches, and brain injectrodes. The devices can be twisted, folded, stretched/flexed and wrapped onto curvilinear surfaces or implanted without damage or significant alteration in operation. The fabrication strategies and design concepts can be applied to various biological substrates and geometries of interest, and thus have the potential to broadly bridge the gap that exists between rigid, boxy electronics and soft, curvy biology.

Bio

Canan Dagdeviren is an Associate Professor at MIT Media Lab, where she leads the Conformable Decoders research group. The group aims to convert the patterns of nature and the human body into beneficial signals and energy.

Dagdeviren earned her Ph.D. in Materials Science and Engineering from the University of Illinois at Urbana-Champaign, where she focused on exploring patterning techniques and creating piezoelectric biomedical systems. Her collective Ph.D. research involved flexible mechanical energy harvesters, multi-functional cardiac vessel stents, wearable blood pressure sensors, and stretchable skin modulus sensing bio-patches.

As a Junior Fellow of the Society of Fellows at Harvard University, she conducted her postdoctoral research at the MIT David H. Koch Institute for Integrative Cancer Research. Here, she designed and fabricated multi-functional, minimally invasive brain probes that can simultaneously deliver drugs on demand and electrically modulate neural activity precisely and selectively for the treatment of neurological disorders, such as Parkinson’s disease.

Dagdeviren’s work has been featured in many media outlets, including TIME, Washington Post, Smithsonian Magazine, Popular Mechanics, CBS News, BBC News and Physics World. In 2015, MIT Technology Review named her among the “Top 35 Innovators Under 35” and Forbes selected her as one of the “Top 30 Under 30 in Science”. Recently, Dagdeviren has been named as a Spotlight Health Scholar by Aspen Institute and World #1 in Medical Innovation Category of Ten Outstanding Young Persons of the World (TOYP) by Junior Chamber International. In 2016, Dr. Dagdeviren was awarded the Science&Sci Life Prize for Young Scientists in Translational Medicine Category and invited to attend Nobel Prize Ceremony in Stockholm, Sweden. Dr. Dagdeviren has been named as 2017 Innovation and Technology Delegate by the American Academy of Achievement. In 2019 Dr. Dagdeviren was among 87 of the nation’s brightest young engineers who have been selected to take part in the National Academy of Engineering’s (NAE) 25th annual U.S. Frontiers of Engineering (USFOE) symposium, hosted by Boeing in Charleston, South Carolina. Dagdeviren also named the BBC’s Top 100 Women List in 2023

Seminar Recording

Passcode: Pv10+a*Y

"CentileBrain for Person-specific Normative Values for Neuroimaging Measures of Brain Organization and Aging" by Sophia Frangou, MD, PhD

Abstract
Accurate and individualized neuroimaging assessments are crucial for advancing personalized medicine and understanding brain variability. CentileBrain is a cutting-edge platform designed to compute person-specific neuroimaging measures, offering enhanced precision and applicability in research settings. This presentation delineates the architecture and functionalities of CentileBrain, highlighting its integration of advanced machine learning algorithms and comprehensive neuroimaging datasets. The platform employs state-of-the-art image processing techniques to analyze structural and functional MRI data, generating tailored metrics that reflect individual brain morphology, connectivity and age-related brain changes. By leveraging normative data, CentileBrain enables the comparison of a subject’s neuroimaging measures against population-based centiles and deviation scores, facilitating the identification of atypical brain features with high sensitivity and specificity. Additionally, the platform’s user-friendly interface ensures accessibility and reliability for diverse user groups including those with limited computational skills or limited access to high-computing infrastructure. CentileBrain represents a significant advancement in neuroimaging analytics, offering a scalable solution for personalized brain assessment. This presentation underscores its potential to transform neuroimaging practices, fostering deeper insights into individual brain health and enhancing the precision of neurological interventions.
Bio
Dr. Sophia Frangou serves as Professor of Psychiatry at the Icahn School of Medicine and as Chair in Brain Health and Associate Dean for Research for the Faculty of Medicine at the University of British Columbia. Her work focuses on elucidating the brain correlates of psychopathology and cognition across the lifespan in healthy individuals and persons with schizophrenia and bipolar disorder. Dr. Frangou has published over 300 articles and has received numerous awards including the 2019 Colvin Prize for Outstanding Achievement in Mood Disorders Research from the Brain and Behavior Research Foundation and the 2020 Educator Award and the 2022 George N. Thompson Award, both from the Society of Biological Psychiatry. She is editor for Human Brain Mapping and European Psychiatry.
Passcode: a.Z54d2j

Application-Oriented Open-Source Methods For Reproducible MRS Data Analysis" by Helge J. Zöllner, PhD

Abstract: 
Proton magnetic resonance spectroscopy (1H-MRS) assesses biochemistry and cellular environment non-invasively. It quantifies signals from endogenous low-molecular-weight metabolites, lipids, and mobile macromolecules. At 3 Tesla, a lack of spectral resolution necessitates editing techniques to resolve signals of ascorbate, glutathione, GABA, or Lac through selective manipulation of the spin system of interest. While consensus efforts of the MRS community established clear analysis guidelines for MRS, only a few end-to-end analysis pipelines exist for MRS, hamstringing the application of MRS outside of expert centers. Osprey is a first-generation end-to-end MRS analysis software offering an all-in-one solution for application-oriented MRS researchers. The lecture will showcase Osprey’s modular design for modeling GABA-edited and advanced multi-metabolite-edited MRS methods, adaption in application-oriented MRS studies in brain tumors and aging research, and integration inside the HEALthy Brain and Child Development study. Finally, a novel, highly flexible model algorithm for multi-dimensional MRS data analysis and its potential for future applications will be presented.
Bio:
Dr. Helge Zöllner’s research interests concern the development and understanding of quantitative magnetic resonance methods, particularly magnetic resonance spectroscopy (MRS), and applying those methods to foster insight into biological mechanisms of the healthy and diseased brain. His Ph.D. at the Heinrich-Heine University Düsseldorf, Germany, focused on applying MRS and CEST imaging to study Hepatic Encephalopathy. His postdoctoral research at JHU has been dedicated to developing and applying advanced MRS methods, emphasizing data processing and modeling. He is the lead developer of the open-source MRS analysis software Osprey, a comprehensive toolbox for modern MRS analysis designed to facilitate reproducible MRS research. His NIH K99 career development award focuses on new MRS methods for studying healthy aging. Here, he combines novel acquisition techniques with multi-dimensional modeling to create a comprehensive neurometabolic profile, including metabolite concentrations and relaxation times.  
Passcode: Y$JbD5P!

"Data-driven stratification of cognitive decline in aging and dementia" by Eran Dayan, PhD

Abstract

Data-driven methods have shown considerable promise in biomedicine and have been employed as diagnostic, prognostic, and stratification tools across a range of clinically relevant tasks. The application of such methods is particularly compelling in the context of cognitive aging, which is characterized by substantial interindividual variability. While some individuals exhibit poor and accelerated cognitive aging, others appear more resilient to normal and pathological age-associated cognitive decline. Marked variability is also observed in the progression of cognitive decline in Alzheimer’s disease (AD), the most common form of dementia. Understanding and constraining this variability is critical for the future success of preventive and disease-modifying interventions for AD and other forms of dementia. In my talk, I will describe recent work from my lab, where we developed methods based on complex network analysis and machine learning to predict cognitive decline rates in normal and pathological aging. I will also discuss potential mechanisms underlying susceptibility to and protection against accelerated cognitive aging. Altogether, our research demonstrates the utility of data-driven and model-driven methods in enhancing our understanding of the variation in progression rates observed in normal and pathological cognitive aging.

Bio
Dr. Eran Dayan is an Associate Professor in the Department of Radiology at the University of North Carolina at Chapel Hill, where he also holds faculty positions at the Biomedical Research Imaging Center and the Neuroscience Curriculum. He completed his PhD in computational neuroscience at the Weizmann Institute of Science and then moved to the National Institute of Neurological Disorders and Stroke (NINDS), where he worked as a postdoctoral fellow and subsequently as a senior research fellow. At NINDS, he became interested in neuroinformatics and in the use of computational modeling approaches in clinical neuroscience research. Dr. Dayan joined UNC Chapel Hill in 2016, where he established the neuroinformatics laboratory. His research, primarily funded by NIA and NICHD, uses neuroimaging and a range of modeling approaches to capture mechanisms of resilience and vulnerability in aging and dementia and develop diagnostic and prognostic methods in these populations. Since joining UNC, Dr. Dayan has won several awards including the IBM Faculty Development Award and the Distinguished Investigator Award from The Academy for Radiology & Biomedical Imaging Research.
Please contact us if you would like to access the recording.

"Current status of molecular imaging of neuroinflammation: from biomarker to application" by Aisling Chaney, PhD

Abstract

Chronic inflammation and immune dysfunction have emerged as key factors in the pathogenesis of neurological disorders such as Alzheimer’s disease (AD). Non-invasive molecular imaging has the potential to reveal valuable insights into the complex neuroimmune interactions associated with neurodegeneration and injury. Here, we will summarize current and emerging biomarkers for positron emission tomography (PET) imaging of inflammation from cellular and functional specificity to radiotracer development and application.

 

Bio

Dr. Chaney is an assistant professor at Mallinckrodt Institute of Radiology (MIR) at Washington University in St. Louis. Her research is focused on the development and translation of novel non-invasive molecular imaging strategies to elucidate the inflammatory component of devastating neurological diseases. In particular, she is interested in the relationship between peripheral and central nervous system innate immune responses, and how this crosstalk affects disease development and progression. Dr. Chaney previously worked at Stanford University as a postdoctoral fellow and instructor in the radiology department. She earned her doctorate in neurosciences from the University of Manchester in the United Kingdom.

Passcode: .PoZAxM7

"Visualization of Histone Deacetylases in the Human Brain" by Changning Wang, PhD

Abstract
Accumulating evidence suggests that epigenetic changes – functional modifications to the genome that do not change the DNA sequence, influencing almost all aspects of biology – cellular differentiation, growth, development, and aging and that provide a powerful mechanism by which environmental exposure can impact gene expression. Altered histone deacetylase (HDAC) expression has been linked to CNS diseases including Alzheimer’s disease (AD), bipolar disorder, schizophrenia, and major depressive disorder from analysis of postmortem tissues.
 
In the past years, we developed [11C]Martinostat, the first radiotracer that labels HDACs in living humans, has enabled the antemortem assessment of HDAC levels and distribution in the human brain. [11C]Martinostat shows specific HDAC binding with low nanomolar affinity and is actively under study in several patient populations. In this presentation, I will talk about the story of [11C]Martinostat, development and our recent human imaging studies such as AD, Parkinson’s disease (PD), Dementia with Lewy bodies (DLB), bipolar disorder, alcohol use disorder (AUD), etc. We are further developing machine learning models to better define HDACs as biomarkers.
By illuminating the contribution of shared and distinct epigenetic changes in these patient populations and shedding light on the underlying neurobiology and behavioral features, we expect to have better understanding of the biology underlying these diseases and their differentiating clinical features. In the long term, our findings may lead to clinical use of [11C]Martinostat and new HDAC-targeted treatment strategies.
Bio
Dr. Changning Wang is an Associate Professor at Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School. Dr. Wang has a multidisciplinary training background in molecular imaging, chemistry, pharmaceutical sciences and neuroscience. His research specializes in the development and application of novel imaging probes for preclinical and clinical neuroimaging investigations of human disease mechanisms and drug development, which might facilitate understanding of basic neurobiological causes or contributions to brain disorders. He has developed several new imaging probes for PET imaging, MRI imaging and optical imaging for brain targets such as protein aggregates, epigenetics and neuroinflammation.
Passcode: g&80fw#2

"Magnetic susceptibility MRI for imaging brain iron, myelin and microstructures during neurodegeneration" by Xu Li, PhD

Abstract
Brain tissue magnetic susceptibility is an intrinsic physical property that is closely related to tissue iron and myelin content. Its anisotropy in white matter is also linked to the underlying tissue microstructures. Brain tissue magnetic susceptibility, when measured with high spatial resolution and quantification accuracy, can serve as an important imaging biomarker, revealing critical pathophysiological changes during neurodevelopment and neurodegeneration.
In this presentation, I will introduce some fundamental features of tissue magnetic susceptibility and discuss how it can be used as an intrinsic MRI marker of key tissue components including iron, myelin and potentially tissue microstructures. Quantitative measurements of bulk tissue magnetic susceptibility and its anisotropy have become possible through the development of quantitative susceptibility mapping (QSM) and susceptibility tensor imaging (STI) methods, with recent advances in deep-learning-based reconstruction and community consensus. Furthermore, applications of these methods in aging and neurodegeneration have demonstrated the exciting potential for using tissue magnetic susceptibility as an imaging marker for the prognosis and monitoring of these diseases.
 
Bio
Dr. Xu Li is an Associate Professor of Radiology in the Johns Hopkins University School of Medicine. Dr. Li obtained his bachelor’s and master’s degrees in biomedical engineering from Zhejiang University in China. He then earned his PhD in Biomedical Engineering from the University of Minnesota in 2010. Following this, he joined the F.M. Kirby Research Center for Functional Brain Imaging at the Kennedy Krieger Institute and the Department of Radiology at the Johns Hopkins University as a postdoctoral fellow and later joined as a faculty member.
 
Dr. Li’s research focuses on the development of new imaging methods for mapping the magnetic properties of living systems, especially quantitative susceptibility mapping (QSM) and susceptibility tensor imaging (STI) methods using high field MRI. He has also worked on creating magnetic susceptibility based human brain atlases and applying QSM to study aging brains and various iron-related neurodegenerative diseases, such as Huntington’s disease and Alzheimer’s disease. Dr. Li’s research also extends to the use of QSM in other neural diseases, such as multiple sclerosis, restless leg syndrome and spinocerebellar ataxia.
Passcode: 7L1d%x3y

"Innovations in Hardware Technologies for Mental Health" by Yasser Khan, PhD

Abstract
Worldwide, 615 million people suffer from the common mental disorders of depression and anxiety. Yet, no existing technology accurately and objectively monitors mental health. The obstacles towards mental health care are complex and multi-faceted: social stigma, high cost, and limited or no access to local care hinder patients suffering from mental health conditions from seeking out and receiving help. To address this chronic need, using recent advances in electronic skin and wearable technologies, we designed a wearable that continuously measures physiological parameters linked to chronic stress and other mental health and wellness conditions, namely, heart rate variability, skin conductance, sweat rate, and the stress hormone, cortisol. We used additive manufacturing and flexible hybrid electronics to make the device scalable and low-cost. We can characterize mental states quantitatively by utilizing sensor data and analytics. In this talk, I will demonstrate the first-of-its-kind wearable for mental health and present a multi-part study combining user-centered design and engineering-centered data collection to inform future design efforts for mental health wearables. Such wearable devices, in conjunction with mobile technologies, enable the possibility of remote care, which can allow discrete monitoring, potentially circumventing the stigma often associated with mental health treatment. Overall, in this talk, I will discuss engineering innovations in medical devices to address one of the most pressing global health burdens.
Bio
Yasser Khan joined the Department of Electrical and Computer Engineering at the University of Southern California as an Assistant Professor in 2022. He earned his B.S. in Electrical Engineering from the University of Texas at Dallas and his M.S. from King Abdullah University of Science and Technology. Dr. Khan completed his Ph.D. in Electrical Engineering and Computer Sciences at the University of California, Berkeley. Before joining USC, he was a postdoctoral researcher in the Department of Chemical Engineering at Stanford University. Dr. Khan’s research centers on additive manufacturing and hardware AI, developing skin-like wearables, implantables, and ingestibles for precision health and psychiatry. He received the 2023 Google Research Award, the EECS departmental fellowship at UC Berkeley, the discovery scholarship and graduate fellowship at KAUST, and the academic excellence scholarship at UT Dallas. With over 50 research papers published on leading platforms, Dr. Khan’s work has been featured by BBC News, the Wall Street Journal, and NSF News.

Please contact us if you would like to access the recording.

"Mapping Sensory and Motor Function in the Spinal Cord and Brain: From Maps to Markers of Disease" by Kenneth A. Weber II, DC, PhD

Abstract
Functional MRI has greatly improved our understanding of the brain’s role in health and disease. The application of functional MRI techniques to the spinal cord, however, has been limited due to technical barriers with the acquisition and analysis of spinal cord images. Recent advancements are expanding the capabilities for spinal cord functional MRI, permitting the in vivo assessment of spinal cord function. Here I will overview the challenges and opportunities of mapping spinal cord sensory and motor function with functional MRI, and then I will discuss our recent work in simultaneous brain-spinal cord functional MRI to provide a more complete picture of the central nervous system function.
Bio
Dr. Kenneth Weber is a Senior Research Scientist in the Division of Pain Medicine at Stanford School of Medicine. Dr. Weber is trained clinically as a chiropractor and completed a PhD in neuroscience at Northwestern University. His NIH-funded research seeks to make MRI more quantitative by developing MRI-based markers that better track sensory and motor function in people with spinal conditions using machine-learning and advanced multimodal brain, spinal cord, and musculoskeletal MRI techniques.
Passcode: s$4H#?y&

"Quantitative MRI of the Kidney" by Suraj D. Serai, PhD

Abstract
This is based on the release of the recent book (Reference: https://link.springer.com/book/10.1007/978-3-031-40169-5 )
Quantitative MRI techniques reveal promising markers for renal disease and could provide additional insights into kidney pathology characterization and disease progression in patients. We have now entered an era in which it is possible to obtain rapid quantitative measurements of numerous physiologically relevant tissue properties – diffusion, perfusion, fat content, iron content, blood oxygen level, tissue elasticity, T1, T2, etc. Each property has found scientific and clinical utility in various applications. With the advancement of the computing power of modern MRI scanners, the images can be post-processed and quantitative maps be generated on the scanner console, ready for review as soon as the acquisition is complete. Such quantitative MRI methods in kidney imaging are vital because they are relatively unbiased as compared with qualitative descriptions. Presentation will include quantitative MRI techniques for renal diseases that provide additional insights into kidney pathology characterization and disease progression. We will share the protocols and post processing techniques of quantitative MRI methods for renal clinical applications. I will present renal MRI technologies emerging from research that can be easily translated to routine clinical practice.
Bio
Dr. Suraj Serai is an MR Scientist and Magnetic Resonance Safety Expert at CHOP, and an Associate Professor of Radiology at the University of Pennsylvania. He received his bachelor’s degree in engineering from the University of Mumbai, India, and his Masters of Science and Ph.D. Degrees from the University of Illinois, Chicago.
Dr. Serai has developed and applied MR imaging-based methods for iron quantification, fat quantification, elastography for fibrosis, and T1/T2 mapping techniques to suit pediatric needs. Dr. Serai provides assistance in quantitative MR project design, and interpretation and analysis of MR imaging data. These research projects are of direct relevance to the clinical practice of radiology. He has published >100 peer reviewed papers, and book chapters. Recently he edited a book titled “Advanced Clinical MRI of the Kidney: Methods and Protocols”. The purpose of this book is to openly share the protocols and post-processing methods of quantitative MRI methods for renal clinical applications. The book provides answers to common questions regarding how renal MRI technologies emerging from research can be translated to routine clinical practice. With this “from the community to the community” approach, the book is designed to enhance training in renal MRI sciences, to improve the reproducibility of renal imaging research, and to boost the comparability of renal MRI studies.
Passcode: NVMO7#x?

"The space between: applying advanced MRI to the thoracolumbar spinal cord in multiple sclerosis" by Kristin O’Grady, PhD

Abstract
Advanced magnetic resonance imaging (MRI) techniques for evaluating the human spinal cord have largely been limited to the cervical spinal cord and applied in a small set of neurological conditions. Improved and advanced techniques, such as quantitative or functional MRI, are rarely studied in thoracolumbar regions of the spinal cord, even though there is motivation to do so. For example, it is estimated that 40% of multiple sclerosis (MS) spinal cord lesions are located in the thoracic and lumbar segments, but they are tremendously understudied in vivo. Additionally, the thoracolumbar spinal cord has a critical association with lower limb and bladder dysfunction, the most prevalent symptoms and deficits in MS. Conventional clinical MRI lacks sensitivity to lesions and microscopic pathology that may drive impairment and is limited in discerning the extent of demyelination or altered function at any spinal cord level, but these limitations are exacerbated in the lower spinal cord. The overarching goal of my lab’s recent and ongoing work is to develop and optimize advanced MRI methods for quantitative characterization of tissue structure and function in the thoracolumbar spinal cord. In this talk, I will share some of the imaging challenges posed by this anatomy and strategies for overcoming them, as well as results from anatomical, functional, and diffusion MRI studies applied in cohorts of healthy volunteers and patients with relapsing-remitting MS.
 
Bio
Kristin O’Grady is an Assistant Professor of Radiology and Radiological Sciences at Vanderbilt University Medical Center with a secondary appointment in Biomedical Engineering at Vanderbilt University. She received her PhD in Biomedical Engineering from Vanderbilt where she completed interdisciplinary research in the fields of optical imaging, advanced therapeutics, and peripheral vascular disease. Dr. O’Grady began her research training in MRI as a postdoctoral fellow at the Vanderbilt University Institute of Imaging Science, where her project on applying glutamate-sensitive MRI methods at 7T to patients with MS was supported by an NIH/NINDS fellowship. Her recent and ongoing work as faculty at VUMC seeks to develop advanced structural and functional MRI methods for the human spinal cord for clinical applications and is supported by the NIH through an NCATS R03 award and NINDS R01 award, and by the National Multiple Sclerosis Society through a Harry Weaver Scholar award.
Passcode: KXx3M+EG

Computational Medicine for Mental and Physical Health by Rose Faghih, PhD

Abstract
 
As new physiological sensing technologies become available for continuous monitoring of physiological signals, the dynamic response to external influences such as environmental inputs, medication, and surgery can be quantified. This research focuses on developing mathematical algorithms for dynamically tracking health states related to mental and physical health in the presence of different interventions. (1) Mental Health Focus: We design algorithms for a closed-loop neural wearable architecture called MINDWATCH for mental and cognitive well-being. We first infer arousal-related autonomic nervous system (ANS) activations. Then, we model and decode cognitive arousal and performance brain states where the inferred ANS activations and behavioral data are used as cognitive arousal and performance observations, respectively. We use neurofeedback to close the loop and modulate cognitive arousal and performance.(2) Physical Health Focus: We investigate clinical data from patients to study inflammation, fatigue, and metabolism using cytokines, stress hormones, and metabolic hormones, respectively. We deconvolve biochemical signals (e.g., hormones) to obtain the secretory events underlying their pulsatile production. Then, utilizing the recovered secretory events, we decode hidden health states (e.g., energy) dynamically. The ultimate goal is to design toolsets that can provide clinically relevant information using biosensors to prevent, diagnose, and manage health conditions.
Bio
 
Rose T. Faghih is an associate professor of Biomedical Engineering at the New York University (NYU) where she directs the Computational Medicine Laboratory within the NYU Langone Health’s Tech4Health Institute. She received a bachelor’s degree (summa cum laude) in Electrical Engineering (Honors Program Citation) from the University of Maryland, and S.M. and Ph.D. degrees in Electrical Engineering and Computer Science with a minor in Mathematics from Massachusetts Institute of Technology (MIT). She completed her postdoctoral training at the Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory at MIT as well as the Department of Anesthesia, Critical Care and Pain Medicine at the Massachusetts General Hospital. Rose is the recipient of various awards including a 2024 IEEE Engineering Medicine and Biology Society (EMBS) Early Career Achievement Award, a2023 National Institutes of Health (NIH) Maximizing Investigators’ Research Award for Early Stage Investigators, a2020 National Science Foundation CAREER Award, a 2020 MIT Technology Review Innovator Under 35 award, and a2016 IEEE-USA New Face of Engineering award. In 2020, she was featured by the IEEE Women in Engineering Magazine as a “Woman to Watch”. She is on the editorial board of PNAS Nexus by the National Academy of Sciences and IEEE Transactions on Neural Systems and Rehabilitation Engineering. Her research interests include wearable technologies, and medical cyber-physical systems, as well as neural and biomedical signal processing.
Passcode: 0fX7g?$3

BMEII Guest Speakers

Below you can see the list of the past BMEII Seminars

"High Resolution Dynamic Imaging: Application to DCE MRI and CEST MRI" by Jaeseok Park, PhD

Abstract

Magnetic resonance imaging has been widely utilised to investigate physiological functions, including functional brain imaging, microvascular dynamics, molecular imaging, etc. In this talk, we introduce rapid high-resolution dynamic MRI exploiting both general spatiotemporal and domain-specific priors for dynamic contrast-enhanced imaging and chemical exchange saturation transfer imaging. Subsequently, we show utilities of these developed techniques on brain cancer and Alzheimer’s Disease.

Bio

Dr. Park is a professor in the Department of Biomedical Engineering at Sungkyunkwan University in the Republic of Korea, where he leads research at the intersection of MR imaging physics, signal processing, and machine learning for brain and cardiac applications. He currently serves as Director of the Brain Korea 21 Project on Intelligent Precision Healthcare Convergence.

Dr. Park brings extensive academic and industry experience, having previously held faculty positions at Korea University and Yonsei University, as well as a research role at Siemens Medical Solutions in Germany. He is Editor-in-Chief of Investigative MRI and serves on the editorial board of the Korean Journal of Radiology. A long-standing member of ISMRM and KSMRM, he has contributed as an ad hoc reviewer for numerous leading journals in the field of medical imaging.

He earned his Ph.D. in Biomedical Engineering from Northwestern University under the mentorship of Dr. Debiao Li.

Recording not available.

"Magnetic Resonance Metabolomics: From Intact Tissues to Imaging and Live-Cell Dynamics" by Leo L. Cheng, PhD

Abstract: 
Guided by findings after our discovery of high-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy, which permits intact analysis of biological tissue and biofluids, our laboratory developed NMR metabolomics. These methodologies can analyze specimens as small as ~1mg for intact tissues, or ~10ul for biofluids (blood, cerebrospinal fluid). We further developed NMR metabolomics, as well as metabolomic imaging, to study malignancy, neurodegeneration, kidney disease, and other illnesses for which diagnosis, staging, and prognostication pose significant clinical challenges. In prostate and lung cancer studies, we showed metabolomics ability to provide important, additional biological parameters to support clinical decision-making. Most recently, our lab demonstrated that our HRMAS spectroscopy methodology can furnish a reaction chamber for monitoring live-cell, real-time dynamic processes of metabolomic reactions, with real-time monitoring of <100,000 cells over 48 hours. Our discussion will highlight metabolomic findings in human lung and prostate cancers, as well as Clostridioides difficile cell real-time dynamics.
Bio
Leo Cheng earned his PhD in 1993 from Brandeis University with Professor Judith Herzfeld and Professor Robert G. Griffin at MIT on solid state proton NMR. He started at Massachusetts General Hospital and Harvard Medical School since 1994, first as a postdoctoral fellow, where he discovered intact tissue high-resolution magic angle spinning (HRMAS) NMR, and then as faculty, where he further developed NMR-based metabolomics for human diseases including cancers and neurological disorders. Since 2003, he has served on numerous American and international committees for scientific grant reviews, including multiple chartered memberships on NIH study sections. He has served as a member of the Editorial Advisory Board for NMR in Biomedicine and other journals since 2005 and the Co-Program Chair of the WMIC 2024 for the World Molecular Imaging Society. Currently, he serves multiple leading roles in metabolomics and metabolomic imaging with various international organizations, including the Chair of the NMR Technology Task Group and the Secretary for the Metabolomics Quality Assurance & Quality Control (mQACC); the Chair of the NMR Interest Group and the Founding Chair of the Spatial Metabolomics/Lipidomics Interest Group for the Metabolomics Association of North America (MANA); the Founding Chair of the Metabolomics and Metabolomic Imaging (MMI) Study Group for the International Society of Magnetic Resonance in Medicine.
Passcode: %7E71j4z

"Diffusion Neuroimaging" by Santiago Coelho, PhD

Abstract:
Water diffusion gives rise to the micrometer-scale sensitivity of diffusion MRI (dMRI) to cellular-level tissue structure. It enables noninvasive characterization of microstructural tissue properties at the cellular scale, yielding potential biomarkers for studying neurodevelopment, aging processes, and neuropathology, converting microstructure imaging into a fast-growing area within quantitative MRI. This talk will explore different approaches focused on maximizing the sensitivity and specificity of dMRI metrics to brain microstructure through the use of biophysical models and novel signal representations, together with optimal data acquisition strategies. Future directions for speeding up data acquisitions by joint diffusion-spatial undersampling will also be discussed.
Bio:
Dr. Santiago Coelho is a postdoctoral research fellow in the Radiology Department at NYU School of Medicine, working with Professors Dmitry Novikov and Els Firemans. His work studies the advantages of non-standard diffusion MRI encodings for microstructure, developing optimal data acquisition and estimation strategies, and undersampling diffusion and image spaces for fast high-resolution microstructural imaging. Dr. Coelho is also an NIH K99 awardee.
Recording not available.

"Evaluating MRI pH mapping as a tool for kidney and cancer imaging" by Michael T. McMahon, PhD

Abstract
Chemical exchange saturation transfer (CEST) MRI has been established as an outstanding tool for measuring tissue pH due to exchange-based signal amplification and multiplexed detection. FDA approved iodinated contrast agents such as iopamidol has been shown to display excellent CEST MRI pH sensitivity, enabling translation of this technology to patients. We are interested in identifying suitable patient populations and introducing refinements in this technology to enable translation. The kidneys filter blood and balance the body’s fluids, and as such play a role in the acid base balance of the body. We will show that CEST MRI pH mapping of the kidneys can detect changes in renal excretion and pH homeostasis and distinguish between obstructed and unobstructed kidney as early as one day after obstructions.
Tumors display abnormal proliferation of cells often followed by development of hypoxia – a signatory feature of tumor microenvironment (TME), which is often considered to be a predictive marker of rapid growth, metastases, recurrence and poor survival rates in solid cancers. We have developed a refinement of pH mapping, glucose-stimulated pH mapping, which leverages the established link between hypoxia, glycolytic activity, and acidosis in tumors and evaluated this technology on mouse models of breast cancer with differential expression in hypoxia-inducible factors (HIFs), particularly HIF-1α. We show that our glucose stimulation enhances the differentiation in these models.
Finally, we have acquired IRB approval for iopamidol based CEST MRI pH mapping and have developed a pH mapping protocol based around the starCEST sequence which employs a turbo-field echo (TFE) acquisition, SENSE reconstruction combined with radial and PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction) k-space sampling. We have now injected a number of volunteers, refined our imaging protocol and will present our resulting pH maps. 
Bio
Dr. Michael T. McMahon has a Ph.D. in Physical Chemistry from University of Illinois at Urbana-Champaign and has been a member of the Radiology Faculty (MR Division) at Johns Hopkins since 2003 where he is currently Full Professor. He is recognized internationally for his work on chemical exchange saturation transfer (CEST) MRI contrast agents for cancer and kidney imaging including as a President’s International Fellow of the Chinese Academy of Sciences. He is the lead editor of the only textbook on CEST MRI “Chemical Exchange Saturation Transfer Imaging: Advances and Applications”. Furthermore, in recognition of his substantial achievements in CEST MRI, Dr. McMahon was elected as Secretary, Vice Chair and Chair of ISMRM’s Molecular Imaging Study Group, served on ISMRM’s workshop organizing committee, is on the editorial board for Tomography and was previously on the editorial board for Concepts in Magnetic Resonance.
Recording not available.

"Federated AI for Early Detection and Intervention of Cancer" by Chalapathy Neti, PhD

Abstract
In this seminar, Dr. Chalapathy Neti will discuss his vision for using federated AI to transform biomedical research and healthcare, with a focus on early detection and intervention of cancer. He will draw on his experience as a senior executive at IBM, where he led several initiatives to develop AI platforms for personalized learning, healthcare transformation, and Watson Health. Dr. Neti will discuss the challenges and opportunities of deploying AI at scale for cancer research, including the need for novel federated AI and data architectures. He will also propose a research agenda to develop hierarchical and federated deep learning approaches using multi-omic, multi-scale data for early detection and intervention of cancer. This seminar will be of interest to researchers and practitioners working at the intersection of AI, biomedical research, and healthcare.
 
Bio
Chalapathy Neti, PhD is a former Head of AI, responsible for harnessing AI to reduce friction in cross border payments. He is a seasoned R&D leader with deep experience in AI (Deep Learning, NLP, Speech, Vision), to solve industry problems (e.g. Precision Medicine, Personalized Learning, Financial Crime, etc.). Prior to joining SWIFT, Dr. Neti held a number of senior management roles at IBM, including VP, IBM Watson Education, responsible for developing AI chat bots for personalized learning; Director of Healthcare Transformation, responsible for leading IBM’s initiative on Healthcare Transformation, resulting in the formation of IBM’s Watson Health business, and seeding a number of innovative AI-based solutions for clinical decision support (e.g. Watson Genomics). Dr. Neti has a Ph.D. from Johns Hopkins University specializing in neural networks (called “Deep Learning”, today). He has over 75+ publications and 30+ patents.
Passcode: %hbw5r8%

"Simultaneous multi-slice (multiband) fMRI: Challenges, Opportunities, and Applications to Clinical Neuroimaging in Schizophrenia Spectrum Disorders" by Jared Van Snellenberg, PhD

Abstract
Simultaneous multi-slice (multiband) acceleration has become a widely used technique for improving the spatial and temporal resolution of functional Magnetic Resonance Imaging (fMRI) studies of the human brain. However, following adoption of multiband fMRI in my laboratory, we discovered multiple data quality issues unique to multiband acceleration, such as signal fluctuations arising from participant respiration and a non-neural signal source of unknown origin that is shared across all simultaneously acquired slices. In this talk I will detail these issues, methods for mitigating their impact on fMRI datasets, and conclude by highlighting how the use of multiband fMRI has enhanced ongoing research into the neurobiology of cognitive deficits in patients with schizophrenia spectrum disorders. 
Bio
Dr. Van Snellenberg is an Assistant Professor of Psychiatry and Behavioral Health at Stony Brook University’s Renaissance School of Medicine, where he directs the Cognitive Neuroscience & Psychosis (CNaP) Laboratory. He obtained his PhD in Psychology from Columbia University and completed postdoctoral training at Columbia University Medical Center and the New York State Psychiatric Institute, specializing in functional Magnetic Resonance Imaging approaches to the cognitive neuroscience of working memory and psychotic symptoms of schizophrenia. His current research focuses on the development of cutting-edge data analytic approaches for high resolution multiband fMRI data, and applying these methods to translational neuroscience studies of patients with psychotic illnesses, and to furthering our understanding of how working memory and related cognitive processes are supported by the human brain.
Passcode: hH0$XsbD

"A Human Cardiovascular Micro-Organ System for Modelling Physiology, Development, and Disease" by David Sachs, PhD

Abstract

Modeling human physiology, development, and disease in vitro is currently accomplished with iPSC derived spheroids and organ-on-chip devices. However, these are limited in that organ-on-chip systems lack important cellular diversity and microphysiology, and spheroids do not capture the tube-like geometry that is essential for most organ functions and multi-organ connectivity. To address the need for more accurate human organ models, we are developing a micro-organ-on-chip system in which iPSCs are seeded into a microfluidic chip and differentiated into tube-shaped organoids in situ, in a system that is guided, but not restricted, by the microfluidic geometry. Starting with the cardiovascular system as a proof of concept, this platform has the potential to simultaneously capture cellular diversity, microphysiology, organ function, and multi-organ connectivity. Custom robotic systems were also developed to control both the seeding and monitoring processes, achieving a controllable variety of micro-organ geometries. As natural organ development leverages continuous feedback from neighboring organ systems, our current engineering efforts are to shift our automation method to deep reinforcement learning, in order to increase the repeatability and throughput of the system via real-time feedback control of organ differentiation.

Bio

David Sachs, PhD, is an Assistant Professor of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. He is developing a new micro-organ model of the human cardiovascular system,  grown from stem cells inside a microfluidic chip, under the control of a custom robotic platform. The chip will be used to study heart disease and development, including cardiovascular stiffening diseases due to microgravity, and is currently scheduled to launch to the International Space Station in March of 2025. Recent funding will expand the chip with the addition of a liver micro-organ, moving toward the longer term goal of AI guided manufacturing of a comprehensive human micro-organ-on-chip system designed to be readily accessible to a diversity of collaborators. Prior to his work at Mount Sinai, he led the Advanced Application Development group at the MEMS semiconductor startup InvenSense, including the algorithm and digital architecture design of motion sensing devices that have been distributed in billions of units. He has undergraduate degrees in physics and piano performance, a master’s degree from the MIT Media Lab, and a PhD in Biomedical Sciences from Mount Sinai.

Seminar Recording

Passcode: uv9+0k2y

Lucy G. Moses Lecture

Join us for our 11th Annual Lucy G. Moses Lecture