Close this search box.
Close this search box.
Close this search box.


NSS Program & Topics

NSS Topics

Authors are invited to submit papers describing their original, unpublished work on one of the topics below:
  • Scintillators
  • Photodetectors
  • Semiconductor Detectors
  • Gaseous Detectors
  • Neutron Detectors
  • Analog and Digital Circuits
  • DAQ, Trigger, and Front-End Readout Systems
  • Modeling, Computational methods, and Data Analysis
  • Radiation Damage Effects and Radiation-Hard Detectors
  • Synchrotron, FEL, and XFEL Detectors
  • High-Energy and Nuclear Physics Applications
  • Dosimetry and Reactor Applications
  • Astrophysics and Space Instrumentation
  • Gamma-Ray Imaging
  • Nuclear Security and Safety Applications

The IEEE Nuclear Science Symposium (NSS) brings together the very large and diverse international community of ionizing radiation detector scientists and engineers. We look forward to welcoming you in the beautiful city of Vancouver in 2023!

The NSS 2023 program incorporates the latest developments in detector technology and materials, new instrumentation techniques, their implementation in high energy and nuclear physics, astrophysics, accelerators, nuclear security, and many other applications in various types of radiation environments. The program will also include emerging fields and current hot topics in nuclear science instrumentation.

Interdisciplinary state-of-the-art developments will be included in the joint sessions with the MIC and RTSD. Special topic workshops will cover areas of specific interests and short courses will be offered on a variety of traditional and novel topics of interest to the NSS community.

NSS Plenary Sessions

Jae Sung Lee is a Professor of Nuclear Medicine and Bioengineering at Seoul National University (SNU) and the CEO of Brightonix Imaging Inc. His early academic achievements are mainly related with the PET/SPECT imaging studies for understanding the energetics and hemodynamics in brain and heart. These studies include his pioneering works to solve the blind source separation problems in dynamic PET data using unsupervised machine learning techniques, such as independent component analysis (ICA) and non-negative matrix factorization (NMF). The most notable achievement of Prof. Lee’s group since the foundation of his own lab in SNU is the development of very early PET systems based on a novel photo-sensor, silicon photomultiplier that is now widely used in clinical and preclinical PET systems. He is now focusing on the machine learning techniques again for answering challenging questions in various medical imaging modalities.

He is the editor-in-chief of Biomedical Engineering Letters (BMEL). In IEEE NSS/MIC, he was the MIC chair in 2013 and 2021. He has served as the NMISC chair and is an elected member of NPSS AdCom. In 2016, he founded Brightonix Imaging Inc. that provides PET imaging instruments and AI software solutions to the medical and molecular imaging society. He has published 14 book chapters and over 310 papers in peer-reviewed journals and received multiple research awards from various scientific societies, including 2022 IEEE NPSS Medical Imaging Technical Achievement Award.

Jae Sung Lee

In this talk, we will explore the latest technical advancements in nuclear medicine and molecular imaging systems that have been made possible through the development of new radiation detector and image processing technologies. Most notable among recent advances is the development of silicon photomultiplier tubes (SiPMs), which has revolutionized positron emission tomography (PET) and related hybrid imaging systems. Photomultiplier tubes, which have been with PET since its birth, are going down in history as digital PET is becoming mainstream with the emergence and advances of SiPM technology. This breakthrough technology has enabled simultaneous PET/MR imaging with little compromise to PET detector performance and remarkably improved time-of-flight (TOF) measurement performance. The low manufacturing cost and small size of SiPMs have paved the way for the development of clinical total body PET and organ-dedicated high-resolution PET imaging systems. Ongoing research efforts to enhance TOF measurement techniques based on SiPMs hold great promise in opening new possibilities, such as accurate attenuation and scatter corrections without requiring transmission data and generation of tomographic images without relying on image reconstruction algorithms. In addition, we will review the growing importance of new algorithms and software techniques that make nuclear medicine imaging more accurate and quantitative and improve the predictability of therapeutic effects. In various fields of PET image generation, AI technology is outperforming conventional mathematical algorithms. In addition, AI technology is contributing to the development of PET imaging technology by improving the performance of existing image correction and reconstruction algorithms or supplementing their shortcomings. Currently, these AI technologies are not limited to the field of PET image processing, but are being used in various attempts to improve the performance of PET detectors and readout technologies and enhance their functions.

Sabrina Nagel received her MA in physics from the University of Texas at Austin in 2004 and, in 2009, earned her PhD from Imperial College London, where she studied short-pulse, highintensity laser plasma interactions. She joined Lawrence Livermore National Laboratory (LLNL) in 2011 and started working on X-ray detectors for the National Ignition Facility (NIF), developing novel capabilities for taking X-ray images of inertial-confinement-fusion (ICF) experiments with unprecedented temporal resolution. In 2018 she became a group leader in the Physics Division at LLNL and the lead scientist for the NIF’s Dynamic X-ray Detectors Group. As such she works closely with engineering and operations and is a key contributor for inertialconfinement-fusion and high-energy-density science campaigns on the NIF.

Her published work ranges from innovative diagnostic techniques and their experimental implementation at large-scale facilities such as the NIF, to the study of ICF implosions, and Rayleigh-Taylor and Richtmyer-Meshkov instabilities in laser-driven shock tubes. Her work on pulse dilation instrumentation was a R&D100 Finalist in the category of Market Disruptor Product in 2015 and, in 2017, she was part of the team that won the R&D100 award for their work on geometrically enhanced photocathodes.

Sabrina Nagel

On December 5th, 2022, the National Ignition Facility (NIF) in Livermore, California, USA performed the
first experiment demonstrating controlled fusion ignition in the laboratory. With a 2.05 MJ UV laser
drive energy delivered to the target, a yield of 3.15 MJ was released by the fusion reactions in the
capsule, providing a net target gain of ~1.5×. Part of that success has been facilitated by increasingly
sophisticated x-ray and nuclear diagnostics making experimental observations that have continuously
advanced our understanding, and guided experimental improvements in targets and laser drive.
Here we present several examples of the impactful ways that diagnostics helped identify and highlight
detrimental issues in implosions and describe how new or improved diagnostics capabilities and
pursuing the resulting, sometimes unexpected, observations paved the way for the recent successes on
the NIF. We will discuss the ignition result as well as the next steps for NIF and provide an outlook on
future applications and technologies, including the reinvigorated pursuit of Inertial Fusion Energy.
This work was performed under the auspices of the U.S. Department of Energy by LLNS, LLC,
under Contract No. DE-AC52- 07NA27344. LLNL-ABS-847931.

Katherine (Kate) Pachal is an experimental particle physicist and a research scientist at TRIUMF. Her work centres on searches for physics beyond the Standard Model using accelerator-based experiments, with a particular focus on signatures sensitive to dark sectors. She got her BSc from the University of Victoria (2011) and her PhD from the University of Oxford (2015), before working as a postdoc first at Simon Fraser University and then at Duke University. She was a member of the ATLAS collaboration throughout this period, but since joining TRIUMF in 2021 she has turned her focus to the DarkLight experiment.

Katherine Pachal

The search for new particles beyond the Standard Model, including those which could explain dark matter, is a key focus of the particle physics community today and relies on a wide range of experiments and detector techniques. DarkLight is one of these experiments: using an electron beam striking a fixed target, it will attempt to create dark photons and observe their decays into electrons and positrons. These outgoing charged particles will be selected by a pair of spectrometers and their tracks recorded by gas electron multiplier (GEM) detectors. A fast trigger based on plastic scintillators and silicon photomultipliers will determine when data is read out from the GEMs.This small experiment sits at the interface of particle, nuclear, and accelerator physics and is being constructed at TRIUMF in Vancouver.

As Canada’s national lab for accelerator and particle physics, TRIUMF is a key platform for the development and operation of DarkLight and similar experiments. The lab both provides experiment support for detector construction, electronics, data acquisition, and so on, and also furthers research to develop new detector and accelerator technologies. This talk will discuss the physics targets, experiment design, and challenges of DarkLight, and through them, illustrate the range of activities and facilities at TRIUMF supporting Canadian fundamental physics.

MIC Program & Topics

The IEEE Medical Imaging Conference (MIC)

The IEEE Medical Imaging Conference (MIC) is a leading international scientific meeting to discuss the latest physics, engineering, and mathematical innovations in medical imaging with a particular focus on applications of ionizing radiation.

Medical imaging is a continuously growing field where technical advances in detectors, instrumentation, computational methods, and integrated systems pave the way towards advances in clinical detection, diagnosis, treatment, and monitoring as well as clinical research into the underlying mechanisms of disease and treatment. In recent years, there has been increased interest in applications of machine learning, AI, and other rapidly emerging areas of research, and innovations in these areas continue to play an increasing role in medical imaging.

MIC is an opportunity for students, post-doctoral fellows, and junior and senior researchers from around the world to come together to share their new ideas and results of innovations and scientific endeavors.

The scientific program of the MIC consists of oral and poster sessions, plenary sessions, and a student award session. Regular sessions will be complemented by Short Courses and specialized workshops covering timely topics in medical imaging and therapy.

MIC Topics

  • New radiation detector technologies for medical imaging
  • Simulation and modeling of medical imaging systems
  • Total-body, whole-body, and multi-modality clinical emission systems
  • High resolution imaging systems (organ-dedicated, small animal systems)
  • X-ray imaging systems (CT, spectral CT, photo-counting CT)
  • Tomographic reconstruction techniques
  • Quantitative imaging (data corrections, parametric/kinetic modeling)
  • Signal and image processing, image assessment, standardization
  • Radionuclide therapy (image processing, theranostics, dosimetry)
  • Imaging in particle therapy and image-guided interventions
  • Emerging applications, new concepts (e.g., self-collimation in SPECT)
Authors are invited to submit papers describing their original, unpublished work on one of the topics below:

Note: abstracts utilizing deep learning and artificial intelligence will be integrated into the above topics depending on the application.

MIC Plenary Sessions

Anca Constantin, Ph.D., is a Professor of Physics and Astronomy at James Madison University, where she is conducting galactic investigations of the processes by which supermassive black holes form and grow in galaxy centers. Professor Constantin received her Bachelor of Science in physics at the University of Bucharest where she specialized in molecular and atomic physics and Astrophysics. After a few years of teaching high school physics, she completed her PhD in astrophysics at Ohio University, as well as postdoctoral positions at Drexel University and Harvard-Smithsonian Center for Astrophysics before joining JMU. Her research work most often requires new observations from space- and ground-based observatories like the James Webb Space Telescope, Hubble Space Telescope, the Chandra Space Observatory, the MMT Observatories, the W.M. Keck and the Gemini North Observatories, the Large Binocular Telescope, as well as radio dish networks like the Very Large Array and the largest steerable radio telescope in the world, the Green Bank Telescope in WV. In recent years she has been enjoying teaching thermodynamics and statistics and classical mechanics to physics majors, and fundamentals of astronomy for general education classes at JMU.

Anca Constantin

Recent substantial developments in imaging technologies are opening up exciting new opportunities for astronomers. We can now trace the dynamics of stars and gas around supermassive black holes in galaxy centers and therefore directly weigh them, we can image exoplanets directly, and we can study the structure of very distant galaxies, thanks to fast, low-noise, wide area detectors, and high spectral resolution integral field spectroscopic capabilities at multiple wavelengths. Whether with ground-based telescopes that employ low-order adaptive optics and much fainter reference stars, or with revolutionary designs that artfully combine light detected simultaneously from high-precision antennas, like the Atacama Large Millimeter-submillimetre Array, or remarkable space-based technological feats like the James Webb Space Telescope,  we have won the battle with atmosphere turbulence, achieved ~10-50 milliarcsecond resolution, and therefore can probe scales of just hundreds of light-years in galaxies that emitted their light when the universe was barely in the making, enabling us to see the signatures of physical processes that drive the mass assembly and structural transformations, answering key questions about how galaxies and their central black holes co-evolve (or not), or probing stellar population ages exceeding 10 Gyr, or learning about how stars form, or why so many atoms don’t end up in stars while some do. This presentation will describe some of these scientific achievements, the extraordinary tools used in pursuing them, the demands for even better resolution and sensitivity as we move towards even smaller scales with the currently planned larger telescopes, along with the challenges they face.

François Bénard, M.D., is a distinguished scientist at the BC Cancer Research Institute, Professor in the Department of Radiology, and Associate Dean for Research in the Faculty of Medicine at the University of British Columbia. He holds the BC Leadership Chair in Functional Cancer Imaging. As a clinician scientist, his research interests are in positron emission tomography (PET), nuclear medicine, cancer imaging and radiopharmaceutical therapy. His team developed several new radiopharmaceuticals targeting tumour receptors, notably peptides and small molecule ligands. He initiated the program that developed cyclotron production of 99mTc, which completed clinical trials at multiple sites in Canada. He has established extensive multidisciplinary collaborations, and he and his colleagues were awarded the 2015 Brockhouse Canada Prize for Interdisciplinary Research in Science and Engineering by NSERC. He is the principal investigator of a new $23.7M Canadian initiative entitled ‘Rare Isotopes to Transform Cancer Therapy’, funded by the New Frontiers in Research Fund – Transformation program.

François Bénard

Following successful late phase clinical trials and regulatory approval of new radioactive drugs to treat neuroendocrine and prostate cancers, radiopharmaceutical therapy (RPT) is enjoying a major renaissance worldwide, with multiple new investigational products in development. RPT uses targeting molecules that bind tightly to proteins selectively expressed or overexpressed in cancer cells. The current RPT paradigm implemented in routine practice, which uses an imaging test to confirm target expression, followed by an approach that uses fixed amounts of injected activity, is crude and rudimentary compared to what is feasible with modern imaging and dosimetry approaches.

Beyond more widespread implementation of personalized dosimetry approaches and improved calculation of injected activity, we need to improve our knowledge and understanding of the dose response relationship of internally delivered radioactivity, in the context of a highly heterogenous tumour microenvironment.  In this presentation, we will review with examples how small chemical modifications can radically alter the distribution and payload delivery of therapeutic radiopharmaceuticals, and how we can leverage novel development in imaging technology to estimate the carefully tailor the initial treatment and subsequent cycles to maximize tumor dose while protecting normal organs from radiation toxicity.

RTSD Program & Topics

Room Temperature Semiconductor Detector Conference (RTSD)

The Room Temperature Semiconductor Detector Conference (RTSD) represents the largest forum of scientists and engineers developing compound semiconductor radiation detectors and imaging arrays operable at room temperature. The IEEE NSS-MIC-RTSD Vancouver will be the 30th RTSD conference since 1972.

Room-temperature semiconductor radiation detectors are finding increasing applications in such diverse fields as medicine, homeland security, radiography, astrophysics and environmental monitoring. The objective of this conference is to provide a forum for discussion of the state of the art for room-temperature-operating detector technology based on compound semiconductors, including materials improvement, material and device characterizations, fabrication, electronic readout, system development and applications. To provide a comprehensive review, oral and poster presentations representing a broad spectrum of research and development activities emphasizing compound semiconductor detectors or imaging devices are sought.

RTSD Topics

Authors are encouraged to submit abstracts on original work related to the following topics:
  • Compound Semiconductor Materials for Radiation Detection
  • Organic and Perovskite Materials for Radiation Detection
  • Crystal Growth, Materials and Defect Characterization
  • Properties of Electrical Contacts and Device Fabrication Technology
  • Radiation Damage, Long-Term Stability and Environmental Effects
  • Pixel, Strip, Frisch-Grid and Discrete Semiconductor Detectors
  • Detector/ASIC Hybridization, Interconnects and Electronics
  • Scintillator/Semiconductor Array Hybrids
  • Compound Semiconductor Neutron Detectors
  • 3D Photon Tracking Detectors and Image Reconstruction Technology
  • Use of AI/ML tools for Analysis of Detector Signals and Decision Making
  • Spectrometer Systems for Homeland Security, Nuclear Inspections, Safeguards, Portal Monitoring, and Other Uses
  • Imaging Systems based on Compound Semiconductor Detectors for Medical, Astrophysics, Non-Destructive Testing, Cargo Monitoring, Environmental Monitoring and Other Uses

RTSD Plenary Sessions

Paul Sellin is a Professor of Physics at the University of Surrey in the UK. He received his BSc from the University of Birmingham UK, and his PhD in Nuclear Physics from the University of Edinburgh. He has had more than 30 years of research experience in the development and characterisation of radiation detectors and detector materials. His work has combined fundamental studies of detector materials and their properties, together with detector applications in nuclear physics, medical imaging, and security detection. His research focuses on the characterisation of semiconductor and organic materials for use in radiation detectors (including cadmium telluride, diamond and perovskite materials) and the development of new detector devices. Recently his research has concentrated on the synthesis and characterization of perovskite semiconductors and scintillators for use as radiation detectors, including studies of single crystal metal halides, lead-free double perovskite materials, and low dimensional perovskite scintillators.

Paul Sellin

Perovskites are a fascinating and varied class of materials that have great potential in radiation detector technologies. Following their initial development by the PV and solid-state lighting communities, perovskites are being actively developed worldwide as both semiconductor and scintillator radiation detectors.

Of particular interest are lead halide perovskites where the presence of high‐Z atoms such as Cs and Pb provides high X‐ray and gamma ray efficiency, combined with good charge transport. These materials can be relatively easily fabricated using either high temperature or solution processing methods, with CsPbB3 currently the leading material for high resolution gamma spectroscopy applications. Particularly rapid progress has also been made in the development of perovskite X‐ray imaging detectors, which have demonstrated excellent X‐ray sensitivity, stability, and imaging performance. Through the use of sintered polycrystalline perovskite thick films, large area imaging detectors can be realized that are approaching the performance of traditional imaging detector materials such as a-Se and CdTe.

The almost infinite range of perovskite materials means that the field of perovskite radiation detectors continues to expand. Structured 2D and 1D perovskites offer many hybrid organic/inorganic materials that combine high resistivity and low dark currents with excellent radiation sensitivity. ‘Lead free’ double perovskites also offer environmentally beneficial materials, many of which combine good semiconductor properties with remarkably high scintillation light yields.

In this talk I will present an overview of the latest results from Surrey and our collaborators on perovskite radiation detectors, including semiconductor single crystal detectors for gamma spectroscopy and polycrystalline perovskites for X-ray imaging, plus some recent key results from perovskite scintillators.

Joint Sessions

Joint NSS/MIC/RTSD sessions will be held in the earlier part of the week following a long-established tradition. These sessions will be focussed on topics that are of relevance to more than one research community to minimize submission and presentation duplications. While abstracts will have to be submitted to one of the three conferences, the interested authors will have the opportunity to highlight the relevance of their research to a wider audience at the abstract submission stage through an appropriate check-box. A final selection of the papers that will be presented at these sessions will be made collectively by the conference program chairs.

Future Directions: Megatrends, Roadmaps and Standards

Date: Tuesday November 7
Time: 04:20 pm – 06:10 pm
Room: Ballroom A


This year we are introducing an exciting new  forward-looking  joint session: Invited speakers from our community and from the  IEEE Future Direction committee will discuss present and anticipated future technical innovations within our collective areas of expertise that have the potential for a disruptive impact on the most pressing  societal needs.  A goal of the session is to identify gaps that need to be addressed to achieve such technological advances, to enable their applications on a wide societal scale, and to interactively build a roadmap towards successfully filling such gaps.

The invited speakers are:
Zhong He (University of Michigan), representing the RTSD community
Gabriella Carini (Brookhaven National Laboratory) , representing the NSS community
Simon Cherry(UC Davis), representing the MIC community
John Verboncoeur  (Michigan State University), IEEE Technical Activity Board VP
Rakesh Kumar (Technology Connexions,Inc), representing IEEE Future Direction, Roadmaps
Dejan Milojicic (Hewlett Packard Labs), IEEE Future Directions Committee Industry Advisory Board Chair, Megatrends

This session is meant to be interactive and thought-provoking where you will have the opportunity to hear not only from experts in our own areas, but also from IEEE Future Directions representatives who will present how our efforts may integrate and complement technical advances in other IEEE engineering and technical activities.
Most importantly, you will have the chance to provide critical input and become involved in  envisioning the future of our fields!

Short Courses

Course title:

Real-time machine learning on FPGAs (hls4ml)

Course organizer:

Ben Hawks, Fermilab


Saturday, November 4 – 8:00 am – 6:00 pm  – 109 on Level 1


Christian Enns, University of Heidelberg
Peter Fischer, University of Heidelberg
Alan Owens, European Space Agency
Lothar Strüder, University of Siegen and PNSensor

Course description:

Machine learning (ML) algorithms have become essential and ubiquitous components ofp hysics experiments, especially in time-critical and resource-constrained applications in the trigger and data acquisition system, or on-detector (“edge”) systems. Often, it is necessary to deploy these algorithms in experiment-specific targeted platforms, including field-programmable gate arrays (FPGAs).


In this module, students will learn how to train an ML algorithm for an experimental physics task using Kears and TensorFlow software packages. They will be taught to design their algorithm satisfying the latency and throughput requirements and at the same time comply with the resource constraints. Students will apply quantization-aware training and parameter pruning to compress the model, making it faster and more efficient, while maintaining an acceptable accuracy. Finally, students will use the HLS4ML Python library to deploy the algorithm on a PYNQ-Z2 FPGA development board

Learning Objectives:

By the end of the course, the participants will be able to:

  1. Train a neural network using Keras and TensorFlow
  2. Convert a trained neural network into FPGA firmware using HLS4ML
  3. Optimize a neural network and its resource utilization for deployment onto an FPGA 4. Deploy a neural network onto an FPGA board
  • Introduction to Machine Learning on FPGAs
    • Basic overview of FPGAs and their underlying structure. Rationale, Motivation, and trade-offs of using FPGAs for Machine Learning.
    • Overview of common Neural Network acceleration techniques and hardware, including GPU Acceleration, Systolic Arrays, and Dataflow Architectures.
    • Considerations and parameters to tune when implementing a neural network on a FPGA. Including parallelism and the trade-off between latency and resource utilization, arbitrary bitwidth numerical representations, and potential resource bottlenecks.
  • Using HLS4ML to convert a Neural network into FPGA Firmware
    • Introduction to using the HLS4ML package, basic configuration, and neural network to firmware conversion. A hands-on walk-through of the model conversion, firmware synthesis, and bitfile generation workflow for a simple physics task.
    • Tuning the details of the implemented model, such as parallelism and precision, performing Post-Training Quantization, and determining the desired implementation strategy.
    • Advanced configuration of implementation parallelism, parameter precision, and implementation strategy. Overview of the different these values at different configuration scopes.
    • Simulation, profiling and evaluation of a model before firmware generation.
  • Optimizing your neural network for deployment onto an FPGA
    • Overview of common model compression techniques, including Quantization Aware Training (QAT), Parameter Pruning, and Knowledge Distillation.
    • A survey of commonly used Quantizaton Aware Training tool kits, their differences, and when/how to use them. Plus, an example of performing QAT on a model, and how to configure and convert a quantized model using hls4ml
    • Example and walkthrough of model pruning, and how to configure and convert a pruned model with hls4ml. Also an example and discussion of how to combine quantization and pruning, its effects on a model, and an example of converting a quantized and pruned model with hls4ml.
  • Deployment and the PYNQ software stack
    • Overview of Xilinx’s “PYNQ” Python API and OS Image, basic usage of PYNQ to interact with, manage, and configure supported devices, such as Xilinx’s “ZYNQ/ZYNQ ULTRASCALE+” and ALVEO devices, through a Python and Jupyter Notebook interface.
    • A discussion and overview of developing/supporting the PYNQ API when building a firmware image, its design requirements and considerations, and examples of more complex firmware images with a neural network built into them.
    • Deployment of a hls4ml generated firmware image onto a TUL Pynq-Z2 development board, running neural network inferences on the FPGA accelerator via the “PYNQ” API and OS, and an example of running the same project on an “ALVEO” device.

Required for this course: Intermediate experience with the Python programming language, basic understanding of Machine Learning/Neural Networks.

Recommended for this course: Basic understanding of FPGAs
For each of the lab stations, we will require the following hardware and software:

  • A Laptop running Windows or Linux with wireless networking capabilities, at least 1 x USB-A port available, at least 1 x RJ-45 Ethernet port available, and the ability to read and write to a SD/MicroSD card.
    Note: Using external USB Hubs/devices to meet these requirements is acceptable so long as they are tested and verified working beforehand.
    Note: Windows and Linux are both acceptable, but regardless of OS, you must be able to open a serial connection over USB. How this is done is OS dependent, but typically on Linux systems, a common requirement is to add your user account to the ‘dialout’ group.
  • A browser that is supported by Jupyter Notebook
    Xilinx Vivado Design Suite 2019.2
  • A GitHub user account
    All software specified above is expected to be installed and ready to use upon arrival to the short course.
Instructors’ Biographies:

Ben Hawks is an AI Researcher at Fermi National Accelerator Laboratory in Batavia, Illinois. He focuses primar- ily on neural network design and optimization techniques to compress neural networks while remaining performant for use in real time physics applications. Ben has experience building and optimizing networks for deployment on FPGA based trigger systems in particle physics experiments. Ben is also exploring tech- niques and metrics for designing and training robust, error resistant neural networks, along with exploring co-design techniques for effective neural network and machine learning accelerator hardware.

Elham E Khoda is a postdoctoral scholar at the University of Washington, Seattle, and a fellow at the NSF Accelerated Artificial Algorithms for Data-Driven Discovery (A3D3) institute. Elham is a particle physicist searching for new fundamental particles analyzing the data collected by the ATLAS experiment at the Large Hadron Collider at CERN. He did his Ph.D. at the University of British Columbia in physics. Currently, Elham is working on accelerating ML algorithms for physics and neuroscience applications using FPGAs and GPUs. He has experience implementing Recursive Neural Network (RNN)-based algorithm on an FPGA using HLS4ML for real-time application in the trigger systems in a particle collider experiment. Currently, he is working on real-time applications of transformer-based models. Elham is also developing ML algorithms for particle physics applications.

Course title:

Fast Timing Detectors and Readout

Course organizer:

Etiennette Auffray, CERN, Geneva, Switzerland


Saturday, November 4 – 8:00 am – 6:00 pm  – 110 on Level 1


Christian Enns, University of Heidelberg
Peter Fischer, University of Heidelberg
Alan Owens, European Space Agency
Lothar Strüder, University of Siegen and PNSensor

Course description:

The precise measurement of the time of arrival of optical and high-energetic photons has received a lot of attention in the past years as many applications require fast timing performance, such as detectors at future high energy experiments, time-of-flight positron emission tomography (TOF-PET) and photon counting CT, hard X-ray imaging, tagging of optical, ultraviolet and infrared photons.

Over the last two decades, the timing performance of scintillator-based detectors has seen striking improvements. For instance, whereas PET in the late 2000s had almost no time-of-flight (TOF) capability, with time resolutions around 500 ps, nowadays clinical systems achieve 200 ps FWHM. New technologies and materials deployed in advanced laboratory systems make it even possible to detect the annihilation photon pairs with a time resolution of 30 – 50 ps FWHM. These advancements were possible by developments in all detector aspects, i.e. the scintillation material, scintillation light transport, photodetectors (SiPMs) and readout electronics. However, despite these recent progress, many questions are still open, especially in view of achieving highest time resolution.

To understand and improve the time resolution, always the entire detection chain has to be considered, for which this course will give a detailed overview of the full detection chain.

Scintillation (E. Auffray) : The theory of scintillation will be described and the timing limits in view of the photostatistics and light transport be given. A larger overview of a variety of well know scintillators and new materials will be discussed, with a given focus on applications in PET and high-energy physics.

Photodetector (S. Gundacker): The silicon photomultiplier (SiPM) has become the standard device in many time critical applications, ranging from TOF-PET to single photon detection. The basic working principles of the SiPM will be introduced and the most important parameters like the photon detection efficiency, single photon time resolution and noise sources discussed. Their impact on timing will be examined when applied to scintillation, Cherenkov and in single photon detection.

Electronics (C. de La Taille): With new developments in scintillation and photodetection fast readout electronics is crucial. There are several ways of amplifying and treating the signals from the detector, for which an integrated overview with their advantages and limits will be given . A focus will be set on SiPM readout, but not only limited to. Fast digitization will be discussed and a summary of available and developed readout solutions presented.

Instructors’ Biographies:

Etiennette Auffray is senior physicist at CERN (Geneva, Switzerland). She has spent over thirty years in the field of scintillators and their applications in particular in high energy physics and medical applications. She was actively involved in the construction of the electromagnetic calorimeter of the CMS experiment at CERN made of 75848 crystals of PWO and now in its operation. She is involved in research activities on scintillating materials for future detetctor in highe energy physics and in the development of PET and TOFPET through the Crystal Clear collaboration of which she is the spokesperson since 2010. In last 10 years years she has coordinated several European projects related to scintillating materials and their applications in particular in recent years for fast timing detectors. She is author and co-author of more than 300 papers. She is member of IEEE since 2007 and has been member of the RISC committee

Stefan Gundacker is working on the development of novel analog detector modules for time-of-flight positron emission tomography (TOF-PET) at RWTH Aachen University. He obtained his doctoral degree (with highest honors) in 2014 from the Vienna University of Technology. He then was a research fellow at CERN working on understanding and improving the time resolution of scintillator based detectors, read out by silicon-photomultipliers (SiPMs). He was among the first to break time resolution limits with these detectors, in high-energy physics and TOF-PET. Currently he is leading the group, time-of-flight detectors and physics, focused on improving ultra-fast emission mechanisms and light transport in inorganic scintillators, improving the timing performance of SiPMs and of the associated front-end readout with an emphasis on system integrability. He has (co-)authored more than 70 peer-reviewed papers on topics related to fast timing and is frequently invited speaker.

Christophe de La Taille is professor of microelectronics at Ecole Polytechnique (Palaiseau France) and researcher at OMEGA microelectronics laboratory (CNRS/IN2P3 ). After receiving engineering and Ph.D. degree from Ecole Polytechnique, he joined LAL Orsay and worked on the readout of the ATLAS calorimeter at CERN/LHC and other high energy physics experiments. He was subsequently CTO of IN2P3 and director of OMEGA laboratory. He is now coordinator of CMS HGCAL electronics and also works for ATLAS HGTD timing detector. His research interests are in the field of detectors and mixed signal ASIC design. He is author and co-author of about 250 publications and has been an IEEE member since 2003.

Course title:

Integrated circuits for detector signal processing and radiation hardened design

Course organizer:

Paul O’Connor, Brookhaven National Laboratory


Sunday, November 5 – 8:00 am – 6:00 pm  – 109 on Level 1


Christian Enns, University of Heidelberg
Peter Fischer, University of Heidelberg
Alan Owens, European Space Agency
Lothar Strüder, University of Siegen and PNSensor

Course description:

This one-day course is intended to introduce physicists and detector specialists to the fundamentals of integrated circuits (IC), front end design, and radiation-hardened design.

The course provides an overview of analog design methodologies and semiconductor devices and then delves into the details of implementing practical circuits in modern CMOS technology. In the second part of the course, the participants will learn state-of-the-art design techniques to implement radiation-hardened reconfigurable digital circuits in the radiation detection readout.


Prerequisites: A basic knowledge of detectors and electronics is assumed.

Lecture1: Design of Signal Processing Circuits in CMOS Technology (Paul O’Connor)

  • Brief introduction to design methodology (CAD tools and foundry access for research-scale projects)
  • Analog circuit design
  • Basic architectures with elementary amplifiers
  • Building blocks for the analog channel: charge-sensitive and pulse-shaping amplifiers, baseline stabilizers, peak detectors, track/hold, multiplexers, output stages
  • Feature extraction: event occurrence, position, time, energy
  • Overview of analog-to-digital and time-to-digital converters
  • Digital vs. analog signal processing
  • Application examples from particle physics, astrophysics, photon science, and medical imaging


Lecture2: Radiation-hardened Design (Raffaele Giordano)

  • Basics of radiation effects in microelectronics
  • Introduction to radiation hardening by design in digital circuits
  • Techniques for real-time self-repair in reconfigurable ICs
Instructors’ Biographies:

Paul O’Connor is a senior staff scientist and leads the Signal Processing and Electronics group in the Instrumentation Division of Brookhaven Lab. He attended Brown University, earning a master’s degree in electrical engineering in 1977 and a Ph.D. in physics in 1980. He joined AT&T Bell Laboratories as a member of the technical staff in 1980 and arrived at Brookhaven Lab in 1990. His research activities center around developing detector systems for a wide range of physics applications. Dr. O’Connor is an author on 300 publications and has seven patents for microelectronic and detector technologies.

Raffaele Giordano is associate professor in the Department of Physics at the University of Naples Federico II (Unina), Italy. He received the master’s and Ph.D. degrees in physics from the same institution in 2007 and 2010, respectively. As a postdoctoral researcher and assistant professor he has been involved in experiments at CERN and at the Japan High Energy Accelerator Research Organization, KEK. Since 2016, he is a faculty member at Unina where he leads R&D projects on novel instrumentation for High-Energy Physics. He is an author of more than 390 scientific papers and holds two international patents for digital oscillators and radiation hardening techniques.

Course title:

Basics of Radiation Detection

Course organizer:

Douglas McGregor, Kansas State University, Manhattan, KS


Sunday, November 5 – 8:00 am – 6:00 pm  – 110 on Level 1


Christian Enns, University of Heidelberg
Peter Fischer, University of Heidelberg
Alan Owens, European Space Agency
Lothar Strüder, University of Siegen and PNSensor

Course description:

The course is designed to introduce and review the basic principles of radiation detection and measurement, including radiation interactions, counting statistics, quality metrics, various detector types, and measurement methods.

Review of radiation interactions in material (D.S. McGregor)

  • Basics of charged particle interaction in matter, energy loss, ionization, backscattering.
  • Basics of photon interactions in matter, mainly including photoelectric, Compton scattering, pair production.
  • Basics of neutron interactions in matter, including microscopic and macroscopic cross sections, thermal, epithermal, and fast neutron interactions, and various materials commonly used for neutron detection.

Radiation counting Statistics (D.S. McGregor)

  • Review of probability and statistics for radiation measurements. Although binomial on Poisson statistics will be introduced, the session will mainly focus of Gaussian statistics.
  • Experimental and theoretical values for median, mode, mean, trimmed mean, variance, standard deviation, and FWHM will be discussed.
  • Error propagation will be discussed and the 2 test will be discussed with examples.

Charge induction (D.S. McGregor)

  • Basic electrostatic theory as applied to radiation detection. Gauss’ law will be reviewed, along with the Green’s theorem and reciprocal Green’s theory.
  • Applications of Green’s theorems relative to charge induction, weighting fields, and weighting potentials, basically the Ramo and Shockley approaches.
  • Application of the weighting potential with examples will be presented.

Gas-Filled detectors (D.S. McGregor)

  • The gas pulse height curve will be presented discussion on the various regions of operations.
  • Ion chambers will be discussed, including fundamental of operation, operating characteristics, and special detectors.
  • Proportional counters will be discussed, including designs, operational characteristics, avalanche gain, preferred fill gases, and operation.
  • Geiger-Müller counters will be discussed, including designs, the Geiger discharge, operational characteristics, preferred fill gases, and performance.

Scintillation detectors (W.J. McNeil)

  • Introduction to inorganic scintillators, including intrinsic and activated scintillators, Stokes shift, vibrational states, emission spectra, light yield, and decay times.
  • Introduction to organic scintillators, including Jablonski diagrams and the Stokes shift. A description of common crystalline, plastic, and liquid organic scintillators.
  • Photon sensing and response functions, including discussions on operation and characteristics of photomultiplier tubes, microchannel plates, diode sensors, and semiconductor photomultipliers.

Semiconductor detectors (D.S. McGregor)

  • Introduction to solid-state theory, including band formation, effective mass, charge mobility, free carrier populations, intrinsic and extrinsic semiconductors, recombination, and trapping.
  • Semiconductor device theory, including pn junction formation, pin diodes, Schottky diodes, ohmic contacts, charge carrier motion, trapping effects.
  • Performance metrics, including detection efficiency, intrinsic peak efficiency, escape peak efficiency, energy resolution, peak-to-Compton ratio, peak-to-valley ratio,
  • Performance of various semiconductor detectors, including Si devices, Ge devices, and select compound semiconductor devices.

Neutron detection devices and methods (D.S. McGregor)

  • Methods of detection for thermalized neutrons, including devices fabricated as gas-filled, scintillation, and semiconductor detectors.
  • Methods of detection for fast neutrons, including absorption and scattering effects.

Additional detectors of interest (D.S. McGregor)

  • A review of some interesting radiation detection devices, including cryogenic detectors, Wavelength dispersive detectors, neutrino and Cerenkov detectors, Luminescent detectors (TLD, OSLD), and track detectors.


Book (course based on):
Radiation Detection: Concepts, Methods, and Devices, By D.S. McGregor and J. Kenneth Shultis
ISBN: 978-1439819395


 Prerequisites of this course: Basic understanding of radiation physics and mathematics.

Instructors’ Biographies:

Douglas McGregor earned BA (1985) and MS (1989) degrees, both in electrical engineering, at Texas A&M University. He then earned MS (1992) and PhD (1993) degrees, both in nuclear engineering, from the University of Michigan. He joined the nuclear engineering faculty at Kansas State University (KSU) in 2002 where he now is a University Distinguished Professor and holds the Boyd D. Brainard Chair in Mechanical and Nuclear Engineering. Prof. McGregor’s research is focused on the design, development, and deployment of novel radiation detectors and detector systems. He develops detectors for measuring various ionizing and non-ionizing radiations based on semiconductor, scintillator, and gas-filled detectors. He specializes in semiconductor device physics, detector physics, semiconductor device designs, and fabrication of various semiconductor devices. For his numerous inventions, he and his students have received 23 allowed radiation detector patents. Prof. McGregor has received several awards for his research, including five R&D 100 Awards for various radiation detectors. His book, Radiation Detection: Concepts, Methods, and Devices, co-authored by J. Kenneth Shultis, is a 1300-page comprehensive description of radiation detection and measurement. Additionally, he has published 9 book chapters on radiation detectors in various books, more than 140 peer reviewed journal articles and more than 100 conference proceedings, which, as of 2023, have resulted in over 6250 citations and an H-index of 41 and i10-index of 151 according to Google Scholar. He and his students have presented more than 140 talks at national/international conferences, universities, and workshops.

Walter McNeil earned a B.S. in Mechanical Engineering (2005) within the Nuclear Option and a PhD in Nuclear Engineering (2010) at Kansas State University, including a thesis topic on the design and fabrication of the micro-structured silicon neutron detector (MSND).  After acquiring industrial experience with radiation detector design and deployment, he joined the nuclear engineering faculty at Kansas State University (KSU) in 2015 where he now serves as an associate professor and is the Steve Hsu Keystone Research Scholar. Prof. McNeil has over 60 publications on novel radiation sensors and holds patents on semiconductor and gas neutron sensor designs as well as neutron emitting devices.  He earned three R&D 100 awards for Li-foil and MSND neutron sensors and the insulated Frisch-Collar semiconductor gamma-ray spectrometer and has served as Associate Editor for the Journal of Radiation Physics and Chemistry.  His experience spans from sensor materials and device fabrication to mobile radiation detection systems and algorithm integration.  He led several engineering efforts resulting in fielded equipment for the U.S. Dept. of Defense, which including vehicle-mounted and hand-held detection systems and radiological isotope identifiers ranging from gas-type counters to scintillator and HPGe gamma-ray spectrometers.

Course title:

Medical Image Reconstruction: from Foundations to AI

Course organizer:

Andrew Reader (King’s College London, UK)


Monday, November 6 – 8:00 am – 6:00 pm  – 109 on Level 1


Normal 0 21 false false false EN-US EN-US AR-SA /* Style Definitions */ table.MsoNormalTable {mso-style-name:”Normale Tabelle”; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:””; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:”Calibri”,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} Christian Enns, University of Heidelberg
Peter Fischer, University of Heidelberg
Alan Owens, European Space Agency
Lothar Strüder, University of Siegen and PNSensor

Course description:

Using the primary example of positron emission tomography (PET), core iterative image reconstruction methods will be explained, from unregularised to regularised methods. The principles of deep learning will then be covered in the context of image reconstruction, from practical implementation of core methods and then finishing with some of the more recent advances in AI for image reconstruction.


Image reconstruction foundations:                           

  • Reconstruction basics: object representation and forward models
  • Maximum likelihood expectation maximisation (MLEM)
  • Kernel EM (KEM)
  • Maximum a posteriori EM (MAPEM)
    • Priors                                                                        
    • Algorithms

AI for image reconstruction:

  • Basic principles of deep learning
  • Direct methods (CNNs and CEDs)
  • Unrolled iterative methods (e.g. FBSEM-Net)
  • Methods without training data
    • Deep image prior                                                      
    • Deep kernel methods      

Implementation of AI for image reconstruction:

  • Deep learned FBP (DL FBP)
  • Deep image prior (DIP)
  • Unrolled iterative reconstruction
  • Implementing methods practically for fully 3D reconstruction

Latest advances in AI for image reconstruction:

  • DIP with kernel and kinetic layers
  • Diffusion models for PET


Prerequisites for this course: Basic understanding of linear algebra, Python basics


Instructors’ Biographies:

ANDREW READER is a Professor of Imaging Sciences at King’s College London, United Kingdom. He received his Ph.D. in medical physics from the University of London in 1999 on the subject of PET image reconstruction. Prior to joining the School of Biomedical Engineering and Imaging Sciences at King’s College London in 2014, he was a Canada Research Chair at McGill University and the Montreal Neurological institute for 6 years. He is an Associate Editor of IEEE TRPMS and has co-authored over 200 scientific outputs. His main research interests include PET-MR, multi-modal image reconstruction and medical image analysis, all now with a primary emphasis on exploiting deep learning.

GEORG SCHRAMM is a visiting instructor at the Radiological Sciences Laboratory (RSL) at Stanford University, US. He received his M.S. degree in nuclear physics and Ph.D degree in medical imaging at TU Dresden, Germany in 2011 and 2015, respectively. Prior to joining RSL, he spent 7 years as a PostDoc in the lab of Prof. Johan Nuyts at KU Leuven, Belgium.  His main research interests include (PET and MR) image reconstruction as inverse problems and open-source high performance computing.

KUANG GONG is an Assistant Professor of Radiology at Massachusetts General Hospital and Harvard Medical School. He received his M.S. degree in Statistics and Ph.D. degree in Biomedical Engineering from University of California, Davis in 2015 and 2018, respectively. His current research focuses on deep learning-based image reconstruction and analysis, clinical task-driven deep learning, and multi-modality information integration for precision medicine. He received the Bruce H. Hasegawa Young Investigator Medical Imaging Science Award from IEEE NPSS in 2021 for contributions to machine learning-based PET image reconstruction, denoising and attenuation correction.

Course title:

PET kinetic modeling and parametric imaging

Course organizer:

Guobao Wang, UC Davis


Monday, November 6 – 8:00 am – 6:00 pm  – 110 on Level 1


Christian Enns, University of Heidelberg
Peter Fischer, University of Heidelberg
Alan Owens, European Space Agency
Lothar Strüder, University of Siegen and PNSensor

Course description:

Dynamic PET imaging with tracer kinetic modeling can provide images of physiologically important parameters that have the advantages of creating higher lesion contrast, being quantitative, and allowing single tracer multiparametric imaging as compared to standard static images. Conventionally, dynamic PET parametric imaging was hampered by limited scanner sensitivity and axial field-of-view. State-of-the-art commercial PET scanners now have achieved unprecedented sensitivity and also enabled simultaneous dynamic imaging of the entire body. It is becoming increasingly feasible to exploit kinetic modeling and parametric imaging for various clinical applications. This course will provide an overview of the basics of PET tracer kinetic modeling and parametric imaging and clinical applications.  It will also cover recent advances in total-body PET kinetic modeling. The intended audience is anyone who would like to gain a better understanding of PET kinetic modeling and parametric imaging.

Course outline:

  • Basics of dynamic PET quantification
  • Compartment modeling
  • Graphical and linearized models
  • Reference-tissue modeling methods
  • Direct estimation of kinetic parameters
  • Brain applications
  • Oncological and cardiac applications
  • Total-body PET kinetic modeling
  • Applications of total-body PET kinetic modeling

Prerequisites for this course: Basic understanding of PET physics and mathematics.

Instructors’ Biographies:

Richard E. Carson is a Professor of Radiology and Biomedical Imaging and of Biomedical Engineering at Yale University. His research focuses on the application of mathematical techniques to the study of humans and primates with Positron Emission Tomography and development of next-generation brain PET systems.  Dr. Carson was awarded the Kuhl-Lassen award from Society of Nuclear Medicine in 2007 and named the winner of the Ed Hoffman Memorial Award from the SNM in 2009. In 2016, Carson was awarded the Distinguished Investigator Award from the Academy of Radiology Research. In 2017, he received the IEEE Edward J. Hoffman Medical Imaging Scientist Award for “contributions to quantification in Positron Emission Tomography including image reconstruction, tracer kinetic modeling techniques, and development and application of mathematical and statistical methods for novel radiopharmaceuticals.” In 2018, Dr. Carson gave the Henry N. Wagner Jr. Lectureship at the SNMMI annual meeting in Philadelphia. In 2019, Dr. Carson was named as a Fellow of the IEEE.

Roger Gunn is CSO at Invicro Konica Minolta and Emeritus Professor of Molecular Neuroimaging at Imperial College London. In his role at Invicro, he is heading the companies scientific strategy including the R&D of new biomarkers, analytics and leading the design, analysis and delivery of clinical imaging trials for pharmaceutical companies. He has held executive management positions in industry for the last 10 years with responsibility for a wide range of scientific imaging portfolios. He is also the founder and a director of MIAKAT Ltd which develops image analysis software for PET imaging data. He has published over 200 peer reviewed papers in the field of imaging with an h-index of 70 and has delivered over 80 invited lectures. His career has involved positions on research councils, consultancy to pharmaceutical companies and the training and mentoring of PhD students and clinical research fellows.

Marc D. Normandin is an Associate Professor of Radiology at Harvard Medical School. Dr. Normandin’s work spans a variety of laboratory and medical imaging techniques toward the development and application of noninvasive physiological measurement technologies and assessment of therapeutic interventions. To that end, he utilizes pharmacokinetic analysis techniques, radiosynthetic/analytic procedures, and molecular biology assays to characterize biological processes in cell culture, tissue samples, and in vivo imaging in animals and human subjects. He is a recognized worldwide as a leader in molecular imaging, especially quantitative methodology for PET and MRI, and serves the scientific community through teaching locally at Harvard and MIT and internationally in the acclaimed PET Pharmacokinetics Course held annually as a satellite to the NeuroReceptor Mapping and BrainPET conferences.

Guobao Wang is an Associate Professor in the Department of Radiology, University of California Davis Health. He is a recipient of NIH/NIBIB Trailblazer Award and NIH/NCI Paul Calabresi Clinical Oncology K12 Research Scholar. His primary research interest is in the theory and practice of PET parametric imaging. The research in his laboratory commonly integrates multidimensional (e.g., dynamic) PET data acquisition with the design of advanced computational imaging algorithms to derive quantitative parametric imaging biomarkers for assessing human diseases. In close collaboration with clinicians, his group is actively pursuing novel clinical translation of PET/CT parametric imaging in various diseases, including metastatic cancer, fatty liver disease, and heart disease. Dr. Wang is an Associate Editor for the journal IEEE Transactions on Radiation in Plasma and Medical Sciences.

Course title:

GATE, a Monte Carlo simulation platform for imaging and therapy

Course organizer:

Lydia Maigne, associate professor, Laboratoire de Physique de Clermont, UMR 6533 CNRS – UCA


Tuesday, November 7 – 8:00 am – 18:00 pm  – 109 on Level 1


Normal 0 21 false false false EN-US EN-US AR-SA /* Style Definitions */ table.MsoNormalTable {mso-style-name:”Normale Tabelle”; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:””; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:”Calibri”,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} Christian Enns, University of Heidelberg
Peter Fischer, University of Heidelberg
Alan Owens, European Space Agency
Lothar Strüder, University of Siegen and PNSensor

Course description:

The GATE toolkit has been in the research landscape for almost 20 years. This open-source software is designed to help researchers and engineers to perform a large range of Monte Carlo simulations in the medical physics field: PET, SPECT, Compton Camera, CT, CBCT (Cone-Beam CT) and radiation therapies. The first publication on PET and SPECT developments has been published in 2004, the evolution towards radiation therapy in 2011, some extensions to other dosimetry applications in 2014 and, more recently in 2021, a specific topic for emission tomography imaging. A third long-term project has started, aiming to completely rethink the way the simulations are described by the user. It has been decided to investigate whether simulations can be directly described in Python instead of macro files. A first beta version is currently showing the feasibility of such an approach. The mechanism is based on the Geant4 python binding thanks to pybind11 that exposes to Python a fraction of the Geant4 API. The first public version of this approach, GATE 10-beta, released in 2023 will be used during this one-day training.

Installation, configuration, and environment of GATE 10-beta

  • Description of GATE 10-beta environment and prerequisites for installation
  • Description of the architecture and installation

Configuring a simulation step-by-step
The following items will be developed through an example describing a particle beam interacting with a multi-slice phantom:

  • How to build a geometry
  • How to parameter the physics settings
  • How to configure source of particles
  • How to set up an actor
  • How to analyse your data

Clinical dosimetry for internal radiotherapy treatment
The following items will be developed through an example describing a clinical dosimetry on a patient CT-scan for a 177-Lu internal radiation therapy treatment.

  • How to import a patient CT-scan
  • How to configure 177-Lu source based on SPECT images
  • How to calculate dose to tumour and organs of interests

SPECT application
Simulation of a clinical SPECT system

  • How to define the detector geometry: crystal and collimator
  • How to define the phantom and the source
  • How to fix the digitizer settings: energy resolution, thresholds, spatial blurring


Prerequisites for this course: Participants should attend the training with their laptop, they will connect to a dedicated server through a remote connection.
Information for connection will be provided one week before the training. Sessions will be registered. A basic understanding of Python programming is recommended.


Instructors’ Biographies:

LYDIA MAIGNE is associate professor at University Clermont Auvergne (UCA, since 2007. She is a team leader in modeling and simulations in medical and particle physics at Health, Environment and Energy Department of the Physics Laboratory of Clermont (CNRS-IN2P3). She obtained a PhD in particle physics in 2005, she has been qualified as medical physicist in 2007. She has been elected as the new spokesperson of the collaboration in 2018.

Through GATE, she seeks improving dosimetry simulations associated to innovative radiopharmaceuticals in internal radiation therapy. She leaded the developments of programs to tackle cell and DNA damage combining GATE and Geant4-DNA studies. Since 2017, she focused on the deployment and validation of biophysical models into the GATE platform for the simulation of the biological dose in ion beam therapy. More recently, she has been involved in FLASH therapy; investigating the simulation of water radiolysis for very high dose rate beams, especially proton beams available at ARRONAX. 

HAN GYU KANG is a researcher at National Institutes for Quantum and Radiological Science and Technology (QST) in Japan since 2018. He developed high-resolution small animal PET scanners using DOI detectors. He modeled and developed various light sharing DOI detectors such as staggered 3-layer DOI detector, 4-layer DOI detector with special reflector patterns, and dual-ended readout DOI detector using trapezoidal crystal geometry. The GATE optical modeling results of those DOI detectors were validated with experimental studies leading to the development of several prototype preclinical PET scanners as presented in 2018, 2019, 2020, 2021, and 2022 IEEE NSS/MIC. Dr. Han Gyu has expanded his research topics to the optical imaging of heavy ion beam in collaboration with Prof. Seiichi Yamamoto at Nagoya University. Their collaborative work was highlighted by “Physics World” in 2019 showing the potential of optical imaging for the cost-effective quality control of radioactive ion beams in HIMAC. Since 2020, Dr. Han Gyu has joined the steering committee of the OpenGATE collaboration.

CARLOTTA TRIGILA has a background in particle physics and biomedical imaging, with specific expertise in designing and optimizing radiation detectors for nuclear medicine through experimental measurements and Monte Carlo simulations with the toolkit Geant4 and GATE. As a postdoctoral scholar at the University of California Davis, she carried out extensive studies to optimize optical models for Monte Carlo simulation of PET detectors, with the final goal of developing ultra-fast radiation detectors. She developed the first version of a standalone application to allow users to use our optical models and modified the GATE source code to integrate these developments.  During her Ph.D. at the university Paris Sud, she developed and optimized a small gamma detector to optimize the dose delivered to patients during radionuclide therapy, specifically focusing on thyroid diseases.

Course title:

Artificial intelligence in nuclear medicine image analysis and processing 

Course organizer:

Arman Rahmim, Professor of Radiology, Physics and Biomedical Engineering at the University of British Columbia (UBC)


Tuesday, November 7 – 8:00 am – 18:00 pm  – 110 on Level 1


Christian Enns, University of Heidelberg
Peter Fischer, University of Heidelberg
Alan Owens, European Space Agency
Lothar Strüder, University of Siegen and PNSensor

Course description:

1) Introduction to Modern AI

  • Principles of AI: providing an overview of the core principles of artificial intelligence, including Convolutional Neural Networks (CNN), Encoder-Decoder Networks, Recurrent Neural Networks (RNN), and Transformer Networks, and their respective architectures and applications.
  • Prior/ongoing work in nuclear medicine: Understanding the applications of AI in Nuclear Medicine, including data analysis and image processing. Applying AI techniques for image and data analysis. Evaluating the potential applications of AI in Nuclear Medicine and identifying the current challenges and opportunities.

2) Pitfalls and Best Practices

  • Common pitfalls to AI studies: poor reproducibility, overly optimistic performance statement, lack of generalizability, and insufficient transparency.
  • SNMMI AI Task Force papers: working closely with domain experts, collecting representative datasets, developing models using cross-validation, ablation studies, following published reporting guidelines, making models and codes available, and being fully transparent about dataset characteristics and algorithm failure modes. (More: using multiple annotators for training and evaluating segmentation and diagnostic algorithms, making sure that clinical task algorithms are interpretable, and removing redundant features from radiomics analysis.)
  • Providing detailed AI examples, developed with best practices taken into consideration

3) How to develop a model (deep dive / practical considerations)  

  • How to prepare data: Preparing data for deep learning models, including addressing class imbalance and data augmentation.
  • How to tune a model: choosing architectures, selecting hyperparameters, comparator models, memory considerations.
  • How to use cross-validation to evaluate a model.
  • Using coding aids: Applying GitHub Co-Pilot to generate code and assist in the development of deep learning models.
  • Analyzing and addressing practical challenges that arise in the development and deployment of models in the field of nuclear medicine.

4) Advanced Future applications and hot topics

  • Diffusion models, Federated learning, Continuous learning, Active learning, contrastive learning, semi-supervised, Graph Neural Networks, large language models (e.g., GPT), Fairness, Neurosymbolic AI, Ethical and social considerations in the deployment of advanced deep learning models in nuclear medicine.


Prerequisites for this course: Familiarity with basic principles of deep learning is recommended.

Instructors’ Biographies:

Dr. Arman Rahmim is Professor of Radiology, Physics and Biomedical Engineering at the University of British Columbia and a Distinguished Scientist and Provincial Medical Imaging Physicist at BC Cancer. He received his MSc and Ph.D. in physics at UBC and worked as a researcher at Johns Hopkins University before returning to Vancouver in 2018. He conducts research in molecular imaging & therapy and has published a book and over 200 journal articles. Dr. Rahmim has been awarded multiple prestigious awards, including the John S. Laughlin Young Scientist Award by the American Association of Physicists in Medicine in 2016 and the Presidential Distinguished Service Award by SNMMI in 2022. He has held various leadership positions in the Society of Nuclear Medicine & Molecular Imaging and currently chairs the SNMMI Artificial Intelligence Task Force and the SNMMI Dosimetry-AI working group.

Dr. Tyler Bradshaw is Assistant Professor in the Department of Radiology in the Imaging Sciences Section, who received his Ph.D. in Medical Physics from the University of Wisconsin in 2016. His research is focused on using machine learning to automate the analysis and interpretation of medical images in PET and nuclear medicine imaging. He also explores the combination of medical image analysis and natural language processing of medical text. Dr. Bradshaw has served on the Board of Directors for the Physics, Instrumentation, and Data Science Council (PIDSC) of the Society of Nuclear Medicine and Molecular Imaging (SNMMI) and as a member of the SNMMI Artificial Intelligence Task Force. Apart from research, Dr. Bradshaw oversees the quality assurance program and physics support for PET and Nuclear Medicine at UW Hospitals and Clinics and the Wisconsin Institute of Medical Research (WIMR).

Dr. Quanzheng Li is Associate Professor of Radiology at the Massachusetts General Hospital, Harvard Medical School. He is also the Director of the Center for Advanced Medical Computing and Analysis, a Core faculty of the Gordon Center for Medical Imaging, and the Scientific Director of the MGH/BWH Center for Clinical Data Science, Massachusetts General Hospital, Harvard Medical School. He did his postdoctoral training at USC, from 2006 to 2007, and was a Research Assistant Professor, from 2008 to 2010. In 2011, he joined the Radiology Department, at Massachusetts General Hospital. He is a recipient of the 2015 IEEE Nuclear and Plasma Sciences Society (NPSS) Early Achievement Award. His research interests include image reconstruction and analysis in PET, SPECT, CT, and MRI, and data science in health and medicine.

Dr. Fereshteh Yousefirizi is Research Programmer at the Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada. She received her PhD from the School of Electrical and Computer Engineering, University of Tehran, Iran. Her main research interests involve the automated segmentation and quantification of tumors on FDG PET/CT imaging in lymphoma, head and neck and cervical cancer. In this regard, she works on the development and implementation of artificial intelligence (AI) techniques for automated quantification of tumor burden on PET scans as well as the investigation of new quantitative features for prognostication in different cancer types.

Our course will include contributions from: (i) Isaac Shiri, from the Division of Nuclear Medicine and Molecular Imaging at Geneva University Hospital in Switzerland, whose main research interests include developing advanced machine and deep learning frameworks to enhance radiology frameworks from image acquisition to decision support systems, and (ii) Shadab Ahamed, from the Department of Physics & Astronomy, University of British Columbia, Canada, whose research interests include AI-based detection and segmentation in clinical PET/CT imaging.


The Digital SiPM Revolution: Opportunities, New Detector Concepts and Networking (SPAD)

Date: Sunday November 5
Time: 08:30 am – 06:00 pm
Room: 114 / 115 on Level 1


For the last two decades, several collaborations in the field of radiation instrumentation have adopted analog SiPMs. That being said, these devices suffer from major limitations that can be addressed if the basic unit cell, the single-photon avalanche diode (SPAD), is read out individually, coupled one-to-one with its processing electronics. This way, we obtain an all-digital sensor, with its integrated readout and digital signal processing.

Even though “digital SiPMs” (a.k.a. photon-to-digital converter—PDC) have been around for a while, they only recently grabbed the attention of our radiation instrumentation community with their ability to measure both photons and charged particles. With SPADs, we can reach timing resolution in the tens of picoseconds and accessing specifications like single-photon counting per SPAD, SPAD hit maps, and many more previously unreachable features. As the sensor provides digital information, the digital signal processing opportunities are endless.

This workshop seeks to convene SPAD-based system experts, experimentalists seeking to upgrade their detectors, and researchers who want to learn more about PDCs or wish to discuss their PDC-based concepts further. Of utmost importance, graduate students, postdocs and early career researchers are most welcome to join us and discover a new field in need of talented people to address the instrumentation challenges targeted by our community.

Unlike typical conference sessions characterized by a series of talks, this workshop strongly builds on discussions. Short talks (5–10 minutes) will introduce various topics, followed by group discussions. Also, time will be allotted for experts and potential users to meet and network, hopefully leading to the initiation of new research programs and collaborations to explore and harness this technology. More comprehensive details regarding the format of the workshop will be confirmed closer to the date of the conference.

Scientists, both SPAD experts and future potential users, interested in presenting their technology as well as their applications and needs at the workshop can contact the organizers at their earliest convenience. Presentations are by invitation only.

Note: We won’t address typical analog SiPM topics at this workshop. “Digital SiPMs”, where each SPAD is read out individually, will constitute the focus.

Experts :

Claudio Bruschini, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland

Edoardo Charbon, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland

Serge Charlebois, Université de Sherbrooke, Canada

Lorenzo Fabris, Oak Ridge National Laboratory, United States of America

Stefan Gundacker, RWTH Aachen University, Germany

Maria Liubarska, University of Alberta, Canada

Rok Pestotnik, Jožef Stefan Institute, Slovenia

Lodovico Ratti, Università di Pavia and Istituto Nazionale di Fisica Nucleare (INFN), Italy

Fabrice Retiere, TRIUMF, Canada

Denis Schaart, Delft University of Technology, Netherlands

Craig Woody, Brookhaven National Laboratory, United States of America


Serge Charlebois, Université de Sherbrooke
Catherine M. Pepin, Université de Sherbrooke
Jean-François Pratte, Université de Sherbrooke

Open Kinetic Modeling Initiative

Date: Tuesday November 7
Time: 1:00 pm – 4:15 pm
Room: 109 / 110 on Level 1


The workshop is a forum on the IEEE NPSS-supported Open Kinetic Modeling Initiative ( and its future directions. This effort is timely in response to the recent boost of high-performance PET scanners (e.g., UIH uEXPLORER, PennExploer, NeuroExplorer, Siemens Quadra, etc). The workshop will also host point-of-view exchanges on open-source code and data effort for tracer kinetic modeling research in the field.

• Introduction of the initiative and related activities
• Tracer kinetic modeling research experience from academia
• Development experience from industry
• Discussions on open-source code and data sharing

Young Investigators’ Workshop (YIWS)

Date: Saturday November 11
Time: 12:00 pm – 6:00 pm
Room: 118 / 119 / 120 on Level 1


The workshop will be a combination of lectures from renowned scientists and industry representatives with plenary discussions including all participants and will provide networking and career development opportunities as well as insights into writing successful grants.


This highly interactive and engaging event is tailored for young researchers from student to postdoc level. Join us for an enriching and memorable experience that is sure to benefit your professional and academic growth, as you immerse yourself in hands-on activities and sessions that foster learning, collaboration, and creativity.

Young Investigators’ Workshop will cover a diverse range of topics to provide a valuable and enjoyable experience:

  1. Networking: Develop international and long-lasting connections with fellow researchers, creating a solid foundation for future collaborations and career advancements.
  2. Career Development: Learn from experienced professionals and experts as they share valuable advice on navigating a successful career path in your chosen field.
  3. Meeting Representatives: Engage with influential figures from industry and academia, opening doors to new opportunities and collaborations.
  4. Peer Discussions: Participate in thought-provoking conversations with your contemporaries, exchanging ideas and discussing the latest research and innovations in your field.
  5. Scientific Speed-Dating: Quickly connect with a variety of professionals in a dynamic and enjoyable environment, expanding your network and learning from others’ experiences.
  6. Grant Writing: Gain valuable insights from experienced scientists on how to write a successful grant application, boosting your chances of securing funding for your research projects.

The Young Investigators’ Workshop is a unique opportunity to extend your stay in Vancouver and make the most of your conference experience. By offering a blend of knowledge-sharing, skill-building, and networking, this event will undoubtedly enrich your professional journey.

So, don’t miss your chance to be part of this exceptional event. Mark your calendars and plan to stay an extra day in Vancouver. We look forward to welcoming you to the Young Investigators’ Workshop!

And finally, dear group leaders and principal investigators, please support your young researchers in attending this exceptional event. Investing in their participation will not only benefit their individual career development but also contribute to the overall growth and success of your research group.


Reimund Bayerlein, PhD, University of California Davis, US;
Steven Seeger, MS, University of Luebeck, Germany;
Florian Mueller, PhD, University of Aachen, Germany

Click here to download the flyer.

Ultra-low-dose PET imaging

Date: Saturday November 11
Time: 2:00 pm – 6:00 pm
Room: 109 / 110 on Level 1


The workshop consists of two parts. The first part will review the recent progress in ultra-low-dose PET imaging. The second part will be an open challenge, which engage the public to join the development of ultra-low-dose PET imaging technologies.


Kuangyu Shi, Dept. Nuclear Medicine, University of Bern, Switzerland;
Rui Guo, Dept. Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University, China;
Song Xue, Dept. Nuclear Medicine, University of Bern, Switzerland;
Hanzhong Wang, Dept. Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University, China;
Axel Rominger, Dept. Nuclear Medicine, University of Bern, Switzerland;
Biao Li, Nuclear Medicine, Ruijin Hospital, Shanghai Jiaotong University, China

Special Events

Women in Engineering (WIE) Luncheon

Date: Thursday November 9
Time: 12:15 pm – 1:45 pm
Room: 121 / 122 on Level 1

Dear WIE supporters,

We are delighted to announce the IEEE NPSS WIE Luncheon at the NSS MIC RTSD in Vancouver the 9th of November 2023!

This year we have an exceptional guest, the renowned science reporter and author Dava Sobel, who will discuss with us her last book on the life of Marie Curie.

“Lessons from Mme. Curie for Women Engineers”

 Marie Curie has been hailed for over a century as the winner of two Nobel Prizes, but very few people know any details of her life story. Even fewer realize how much she did to advance other women scientists, especially her daughter Irène, who helped construct France’s first atomic pile and also its first synchro-cyclotron, at the nuclear physics research center in Orsay. 

Marie Curie originally coined the term “radioactivity” to describe the behavior of the elements she discovered with her husband, Pierre. Circumstances led her to take over his laboratory and step into his teaching position, thus becoming the first female faculty member at the University of Paris. She frequently found herself the only woman in the room when the leading physicists of her day—Max Planck, Ernest Rutherford, Albert Einstein, Nils Bohr, Enrico Fermi, Werner Heisenberg—gathered to discuss the structure of the atom that radioactivity laid bare. Dava Sobel, author of a forthcoming book about the women who worked “At Mme. Curie’s Lab,” will explore their strategies and successes.

Please book your ticket during the registration to guarantee your entrance to this unmissable event. While the entrance will be free, the number of participants will be limited and only granted by your early registration.

We are looking forward to welcoming you!!


Cinzia DaVia, NPSS WIE Liaison

Dava Sobel, a former New York Times science reporter, is the author of Longitude (Walker 1995 and 2005, Penguin 1996), Galileo’s Daughter (Walker 1999 and 2011, Penguin 2000), The Planets (Viking 2005, Penguin 2006), A More Perfect Heaven (Walker / Bloomsbury 2011 and 2012), And the Sun Stood Still (Bloomsbury, 2016) and The Glass Universe (Viking, 2016). She has also co-authored six books, including Is Anyone Out There? with astronomer Frank Drake.

A longtime science contributor to Harvard Magazine, Audubon, Discover, Life, Omni, and The New Yorker, she wrote about leap seconds and the transit of Venus for the on-line Aeon.

Ms. Sobel received the 2001 Individual Public Service Award from the National Science Board “for fostering awareness of science and technology among broad segments of the general public.” Also in 2001, the Boston Museum of Science gave her its prestigious Bradford Washburn Award for her “outstanding contribution toward public understanding of science, appreciation of its fascination, and the vital roles it plays in all our lives.” In October 2004, in London, Ms. Sobel accepted the Harrison Medal from the Worshipful Company of Clockmakers, in recognition of her contribution to increasing awareness of the science of horology by the general public, through her writing and lecturing. In 2008 the Astronomical Society of the Pacific honored her with its Klumpke-Roberts Award for “increasing the public understanding and appreciation of astronomy.” Her 2014 Cultural Award from the Eduard Rhein Foundation in Germany commends her “for using her profound scientific knowledge and literary talent to combine facts with fiction by merging scientific adventures and human stories in order to give the history of science a human face.”

Dava Sobel

GATE User Meeting

Date: Thursday November 9
Time: 12:15 pm – 1:45 pm
Room: 220 on Level 2


GATE is an advanced opensource software developed by the international OpenGATE collaboration and dedicated to numerical simulations in medical imaging and radiotherapy. GATE is based on the Geant4 toolkit.

It currently supports simulations of Emission Tomography (Positron Emission Tomography – PET and Single Photon Emission Computed Tomography – SPECT), Computed Tomography (CT), Optical Imaging (Bioluminescence and Fluorescence) and Radiotherapy experiments. Using an easy-to-learn macro mechanism to configurate simple or highly sophisticated experimental settings, GATE now plays a key role in the design of new medical imaging devices, in the optimization of acquisition protocols and in the development and assessment of image reconstruction algorithms and correction techniques. It can also be used for dose calculation in radiation therapy, brachytherapy or any other application.

We encourage our community of users to participate in our annual workshop, the objectives will be to better understand the latest innovative applications in the field of imaging, to identify certain limits and to propose solutions adapted to the improvement of simulation practices.


Lydia Maigne, University of Clermont Auvergne

STIR Users and Developer's workshop

Date: Thursday November 9
Time: 6.30 pm – 8.30 pm
Room: 220 on Level 2
Submission Deadline: 20. October 2022


STIR is an open-source software for use in tomographic image reconstruction ( Its aim is to provide a multi-platform object-oriented framework for all data manipulations in emission tomography. Currently, STIR supports data from various clinical and preclinical scanner manufacturers and simulation toolkits (e.g. GATE, SimSET). More recently, STIR has been updated to reconstruct images for long axial field of view scanners but also for dedicated organ specific systems demonstrating its adaptive potential. The main supported modalities are PET and SPECT with sinogram and list-mode reconstruction and processing functionalities.

The annual meetings during the IEEE NSS/MIC conference provide the opportunity for experienced users and developers to present their recent work with STIR with a technical emphasis on software and algorithmic development. In addition, new users and developers can benefit by presenting their results to the rest of STIR’s community and receive direct feedback. The meeting can be attended by people remotely as well.


Kris Thielemans, University College London
Daniel Deidda, National Physical Laboratory
Nikos Efthimiou, Massachusetts General Hospital
Charalampos Tsoumpas, University of Groningen

Young Professionals

Date: Wednesday, November 8
Time: 12.30 pm – 1.30 pm
Room: MR 121/2


A post-doctoral position is a natural transition for most graduate students as they seek to further their career. There are several options and preparation is essential for successfully transitioning from being a student or postdoctoral researcher.

Join an experienced and diverse panel to hear them and discuss career options and how to approach them.



Vesna Sossi, University of Britisch Columbia
Sara Pozzi, University of Michigan
Dennis Schaart, Delft University of Technology
Youngho Seo, University of California