Tutorial Program 2023
We are very pleased to present you excellent speakers for the tutorials 2023. Parts 1 to 3 are included in the Tutorial Ticket, which must be booked separately. Tutorial 4 is open to all conference participants.
Wednesday, March 22, 2023
09:00 - 10:00 Tutorial 1
Strategies and tips for the development of prototype instrumentation in an academic lab
Prof. Dr. Lawrence L. Wald | Massachusetts General Hospital, Harvard Medical School, Boston, USA
Abstract and further information
10:00 - 11:00 Tutorial 2
Machine Learning for MPI Reconstruction
Prof. Dr. Tolga Çukur | Bilkent University, Ankara, Turkey
Abstract and further information
Thursday, March 23, 2023
08:00 - 09:00 Tutorial 3
Particle magnetization models in MPI
PD Dr.- Ing. Tobias Kluth | University of Bremen, Germany
Abstract and further information
Friday, March 24, 2023: Open Tutorial
08:30 - 09:00 Open Tutorial
How to Write a Successful S10 NIH Shared Instrumentation Grant: Sharing is Caring
Prof. Dr. Jeff Bulte | Johns Hopkins University, School of Medicine, Baltimore, USA
Abstract and further information
Strategies and tips for the development of prototype instrumentation in an academic lab
Machine Learning for MPI Reconstruction | Prof. Dr. Tolga Çukur, Ankara, Turkey
Machine Learning for MPI Reconstruction
Magnetic particle imaging (MPI) promises an unparalleled combination of contrast and resolution for tracing magnetic nanoparticles. Yet, formation of images from acquired data is a heavily ill-posed problem given limits on the imaging speed and signal-to-noise ratio efficiency in MPI. Classical approaches to reconstruction of imaging data rely on hand-constructed priors that can fail to address these limitations effectively. In this talk, I will share an overview of recent efforts on devising deep learning techniques that instead adopt data-driven priors to surpass fundamental barriers. I will showcase neural network architectures and learning strategies that empower performance leaps in system matrix recovery, image reconstruction and processing.
Dr. Tolga Çukur received his Ph.D. in Electrical Engineering from Stanford University in 2009. He was a postdoctoral fellow in Helen Wills Neuroscience Institute at University of California, Berkeley till 2013. Currently, he is an Associate Professor in the Department of Electrical and Electronics Engineering, UMRAM, and Neuroscience Program at Bilkent University. His lab develops computational imaging methods for understanding the anatomy and function of biological systems in normal and disease states. His recent work focuses on novel deep learning methods for all stages of the biomedical imaging pipeline including reconstruction, synthesis, segmentation, and analysis.
Particle magnetization models in MPI | PD Dr.- Ing. Tobias Kluth, Bremen, Germany
Particle magnetization models in MPI
Finding sufficiently accurate models for the concentration-to-voltage mapping in MPI is still a challenging problem. One crucial aspect is the magnetization behavior of magnetic nanoparticles (MNPs) in the applied magnetic field. After a short general introduction to modeling aspects of magnetic particle imaging, physical models for the dynamic behavior of the MNP's magnetic moment (Brownian/Néel rotation) and their influence on the MPI signal are considered in this tutorial. The tutorial further provides an introduction to simulation techniques for solving the Fokker-Planck equation resulting from the stochastic ODEs of Brownian and Néel rotation of the particle's magnetic moment.
Tobias Kluth is a postdoc at the Center for Industrial Mathematics (ZeTeM), University of Bremen. He studied Industrial Mathematics focusing on nonlinear inverse problems and electrical impedance tomography. In 2015 he did his PhD in computer science related to neuroscience addressing aspects of neural information processing in the human visual system. Since 2016 he is a postdoc at ZeTeM working on inverse problems in imaging applications with a particular focus on MPI. In 2021 he finished his habilitation in applied mathematics. His major research interests also include learning-based methods for inverse problems, mathematical parameter identification, and mathematical modeling of MPI with a focus on image reconstruction.
How to Write a Successful S10 NIH Shared Instrumentation Grant: Sharing is Caring | Prof. Jeff W.M. Bulte, Baltimore, USA
How to Write a Successful S10 NIH Shared Instrumentation Grant: Sharing is Caring
Not everyone is lucky enough to have extra funds available for purchasing a new MPI machine. An alternative option is to submit a shared instrumentation grant (SIG) grant application to NIH, capped at $600,000.- (low-end instrumentation) or $2,000,000.- (high-end instrumentation). A successful application needs to address a proper justification of need, identify about 10 NIH-funded major users, describe the technical expertise that exists to operate the machine, provide an effective management/administration plan, a detailed siting/housing plan with or without major renovation of existing space, and a financial/business plan for the first 5 years including institutional commitment. Examples of a successful application will be shown from the Kennedy Krieger Institute together with Johns Hopkins University, along with an outline of things to say and not to say.
Jeff W.M. Bulte, Ph.D., is a Professor of Radiology, Oncology, Biomedical Engineering, and Chemical & Biomolecular Engineering at the Johns Hopkins University School of Medicine. He is the inaugural Radiology Director of Scientific Communications, and serves as Director of Cellular Imaging in the Johns Hopkins Institute for Cell Engineering. He is a Fellow and Gold Medal awardee of the ISMRM, a Fellow of WMIS, AIMBE, and IAMBE, and a Distinguished Investigator of the Academy of Radiology Research. He specializes in the development of new contrast agents and theranostics as applied to molecular and cellular imaging, with particular emphasis on in vivo cell tracking and regenerative medicine.