International Workshop on Magnetic Particle Imaging
12th IWMPI: March 22-24, 2023 | Aachen, Germany
The International Workshop on Magnetic Particle Imaging (IWMPI) is the premier platform for magnetic nanoparticle imaging, nanoparticle spectroscopy, nanoparticle-based therapy, and nanoparticle tracking.
Researchers, developers, manufacturers and users of this new method of medical imaging are invited to attend and contribute to the conference by presenting your current research results and showcasing your developments. In March 2023, we are excited that the IWMPI was hosted by the University of Aachen. After the digital events of 2020 and 2022, цe were very happy to meet in person.
This IWMPI, March 22-24, 2023 was attended by more than 200 participants from 20 countries as well it was a record number of scientific contributions to talks and posters. The workshop provided not only the opportunity to present research work and results, but also new ideas and directions in the field of MPI to a highly interested audience of scientific, medical and application experts from universities, clinical and commercial sites, active in the field.
We look forward to having you join us with your team at IWMPI 2024.

Volkmar Schulz
Chair
RWTH Aachen, Germany

Ioana Slabu
Co-Chair
RWTH Aachen, Germany
Program Chair
Thorsten M. Buzug, University of Lübeck, Germany

Thorsten M. Buzug
Institute of Medical Engineering, University of Lübeck, Germany
Publication Chair
Tobias Knopp, University Medical Center Hamburg (UKE), Germany

Tobias Knopp
University Medical Center Hamburg-Eppendorf (UKE), Germany
Keynote Speakers 2023
We are very pleased to present you 2 excellent keynotes 2023.

Prof. Dr. Emine Ulku Saritas | Bilkent University, Ankara, Turkey

Dr. Michael Christiansen | ETH Zürich, Switzerland
Emerging Applications of Color Magnetic Particle Imaging
Prof. Dr. Emine Ulku Saritas, Ankara, Turkey
Emerging Applications of Color Magnetic Particle Imaging
Color Magnetic Particle Imaging (MPI) is a rapidly developing area within MPI, promising numerous important applications such as identifying the properties of the MNP environment and catheter tracking during cardiovascular interventions. Different MNPs are expected to induce different MPI signals based on factors such as their magnetic material and core diameter. Furthermore, the relaxation behaviors of MNPs are affected by the differences in their environmental conditions, such as temperature or viscosity. This talk will discuss recent techniques in color MPI that leverage these differences to distinguish different MNP types and/or their environmental conditions. Specifically, the presentation will concentrate on calibration-free x-space-based relaxation mapping for color MPI.

Emine Ulku Saritas is an Associate Professor of Electrical and Electronics Engineering at Bilkent University. She received her Ph.D. degree in 2010 from the Department of Electrical Engineering at Stanford University. She was a postdoctoral fellow at the Department of Bioengineering at University of California, Berkeley between 2010-2013. Her research focuses on developing novel contrast methods and high-resolution imaging techniques, particularly for magnetic particle imaging (MPI) and magnetic resonance imaging (MRI) systems. She is the recipient of the Lucent Technologies Stanford Graduate Fellowship, Siebel Stem Cell Institute Postdoctoral Fellowship, and Turkish Academy of Sciences Young Scientist Outstanding Achievement Award. Dr. Saritas is the associate director of National Magnetic Resonance Research Center (UMRAM) at Bilkent University, and is currently serving as the chair of ISMRM Turkish Chapter and the chair of IEEE Turkey EMBS.
Control and Detection Strategies for Magnetic Microrobots Inspired by Magnetic Particle Imaging
Dr. Michael Christiansen, Zurich, Switzerland
Control and Detection Strategies for Magnetic Microrobots Inspired by Magnetic Particle Imaging
In addition to underpinning several minimally invasive biomedical imaging modalities, magnetic stimuli offer a compelling means to control and power medical microrobots deployed within the body. The variety of forms that these magnetically responsive microrobots can take is continuously expanding, ranging from fabricated structures designed to efficiently locomote in response to applied magnetic fields, to naturally magnetic organisms repurposed as biohybrid microrobots. In the past, the magnetic stimuli most frequently applied to these microrobots have been directable uniform magnetic fields that can steer their intrinsic motion or gradient fields that apply forces to pull them. However, these methods entail serious practical challenges, since control often requires a complimentary form of live imaging, and the feasibility of producing adequate gradients in patients diminishes as the microrobots shrink in size. Rotating magnetic fields (RMFs), in which magnitude remains constant while direction is swept around one or more planes of rotation, offer a promising alternative for actuation. Not only do RMFs deliver mechanical energy through magnetic torque-based actuation schemes that more readily scale to patients, but our work has also shown that concepts inspired by magnetic particle imaging present unique possibilities for control and feedback when adapted to RMFs. Actuating microrobots with RMFs permits simultaneous actuation and inductive detection, which we demonstrate with setups adapted for low frequencies (Hz to 10s of Hz) and physical phase cancellation. Moreover, we have shown that a superimposed magnetostatic selection field can spatially restrict torque-based actuation to a single point. By combining these ideas, and employing signal processing techniques focused on phase decomposition, we show that spatially selective inductive signal acquisition is possible in a gating field and that the resolution is set by the relative magnitude of the magnetostatic field and RMF. These principles build toward improved methods for drug targeting with live feedback and show how concepts from MPI can be fruitfully extended to magnetic microrobotic control.

Michael Christiansen is a senior scientist in the Responsive Biomedical Systems Lab at ETH Zürich, headed by Prof. Dr. Simone Schuerle, where he was previously a postdoctoral researcher funded by an ETH Postdoctoral Fellowship. He earned his PhD in Materials Science in 2017 at the Massachusetts Institute of Technology, where he worked at the interface of materials science and neuroengineering. His thesis work developed the concept of magnetothermal multiplexing for selective cellular actuation. As a graduate student, he was awarded an NDSEG Fellowship, and as an undergraduate he was supported by the Barry M. Goldwater Scholarship, a nationally competitive Congressional scholarship in the US. His research interests primarily focus on magnetic materials in the context of emerging biomedical applications and the design of magnetic instrumentation for detection and actuation.
Tutorial Speakers 2023
We are very pleased to present you excellent speakers for the tutorials 2023.

Prof. Dr. Lawrence L. Wald | Massachusetts General Hospital, Harvard Medical School, Boston, USA

Prof. Dr. Tolga Çukur | Bilkent University, Ankara, Turkey

PD Dr.- Ing. Tobias Kluth, Bremen, Germany

Prof. Jeff W.M. Bulte, Johns Hopkins University School of Medicine, Baltimore, USA
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
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.
Program 2023
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
| ||||
|