Short Courses

Super-resolution ultrasound imaging has the capacity to distinguish and map structures that are smaller than the classical limit, typically a fraction of the wavelength. For ultrasound imaging, this means exploring features, such as blood vessels, in the micrometric range deep inside tissue. At the end of this course, students should be able to understand and reproduce super-resolution ultrasound imaging experiments, from data acquisition to image reconstruction, and apply such knowledge in their specific fields. 

We first explore the fundamental aspects of imaging resolution in ultrasound. Various approaches to bypass the diffraction-limit with microbubbles and other agents are presented. Particularly, we discuss ultrasound localization microscopy, which has recently improved the resolution for vascular imaging by more than 10-fold. We present its various steps, including separation, localization and tracking, and compare different approaches. Specific elements such as temporal resolution, motion correction or volumetric imaging are considered. We then detail the applications of super-resolution ultrasound for brain, cancerous tumor, kidney, liver, lymph node and peripheral vessel imaging, along with future perspectives in the clinical and preclinical context. 

We will share tips and tricks for setting up and conducting in vivo super-resolution imaging experiments on chicken embryos, mice, rats, rabbits, pigs, and humans. Hands-on advice will be shared and discussed (e.g., tail vein and jugular vein catheterization, chicken embryo microbubble injections, microbubble bolus versus steady-state infusion, craniotomy and transcranial imaging preparations, respiratory gating considerations, microbubble concentration/sparsity checking and manipulations before data acquisition, data size and processing requirements/considerations, etc.).

The last part of the course will include hands-on image processing of open data (in-silico and in- vivo) with provided ultrasound localization microscopy algorithms. Deep learning-based microbubble localization and tracking methods will also be introduced in this course. We will provide practical experience for microbubble and flow model simulations to generate data for training and testing the neural network.

A.I. and deep learning are increasingly impacting ultrasound research. At the 2019 IUS we started a short course series on A.I. in ultrasound imaging, introducing the basic concepts and applications to our community.

The A.I. landscape has dramatically changed over the past years. A.I. in 2019 was mostly computer vision and convolutional neural networks. A.I. in 2024 is characterized by revolutionary breakthroughs in deep generative modelling (generative AI) and cognitive agents (active inference and reinforcement learning). We aim to bring these A.I. breakthroughs and their opportunities to the ultrasound community.

The course focuses on:

• Deep generative AI (with a strong focus on diffusion models): from theory to implemntation

• Active inference, cognitive agents and perception-action loops: from theory to implementation

• Applications in ultrasound: from cardia image dehazing to adaptive scanning

• Real-time demonstrators and software on the US4US open ultrasound platform

This course aims to familiarize participants with the complete development workflow of end-to-end, software ultrasound systems running on GPUs. Participants will be guided through several realistic use-cases that demonstrate full data flow-from raw channel data to reconstructed outputs-and the corresponding GPU implementations.

This course will explore advanced aspects of GPU programming, including:

• Optimal utilization of GPU cores and memory hierarchies

• Dynamic parallelism and kernel orchestration

• Use of CUDA-optimized libraries, such as

  • cuBLAS/cuSPARSE for sparse matric operations

  • cuFFT for frequency-domain processing

  • TensorRT/cuDNN for machine-learning-based ultrasound pipelines

• Performance modeling, occupancy analysis, and memory-bandwidth optimization

• Mixed-precision computing and considerations for signal-processing accuracy

Today's precision piezoelectric acoustic wave devices are designed with several essential features, including high quality factor (Q), low power consumption, compact size, and strict requirements for frequency and temperature stability, as well as force sensitivity. Since these devices serve critical roles in frequency standards and detection, their frequency performance must be upheld through precise design, manufacturing, and operational practices. Therefore, the analysis and design of these piezoelectric devices require accurate two-dimensional (2-D) or three-dimensional (3-D) models that reflect the resonator geometry, mountings, and material properties.

Additionally, models for nonlinear analysis must consider effects such as (1) temperature sensitivity, (2) applied forces from environmental vibrations, (3) harmonic generation, and (4) intermodulation. These models are essential for the design and analysis of acoustic resonators, as we have successfully extracted their electrical circuit parameters and identified major factors influencing their precise frequency performance.

The course will begin by addressing the fundamentals of accurate linear finite element modeling, focusing on frequency spectra and quality factor (Q) as functions of resonator geometry and mountings. We will present comparisons between model results and relevant experimental data. Following that, we will discuss the nonlinear finite element modeling of these devices, taking both linear and nonlinear material properties and deformations into account.

We will cover the linear and nonlinear material constants for common piezoelectric materials. The nonlinear behavior of quartz resonators will also be explored, including their frequency-temperature behavior, force-frequency effects, and nonlinear resonance, such as the Duffing effect. If time allows, we will present nonlinear frequency response modeling for force-frequency effects, harmonic generation, and intermodulation of bulk acoustic wave (BAW) and surface acoustic wave (SAW) resonators, comparing the results with experimental findings.

This short course explores the interaction of Analog Front End (AFE) electronics with passive ultrasound transducers, advances to the integration of the AFE with in-probe electronics, and finally considers the implications on ultrasound system design.

The course starts by considering the electronics within a typical AFE. A basic electronics primer is provided including Characteristic Impedance, Impedance Matching, Cable Selection then Analog and Switched Mode Transmit Circuits, Transmit/Receive Switches and Multiplexers, Receiver AFE, Amplification including Noise Factor and Noise Figure, Filtering and Analog to Digital Convertors (ADC).

With an understanding of the discrete building blocks, active probes with in-probe electronics and the associated system partitioning will be studied, with use cases in SNR enhancement and the realization of 3D probes and wearables. The use of Application-Specific Integrated Circuits (ASICs) and the integration of such ASICs with ultrasound transducer arrays will be discussed. Approaches to realizing in-probe transmit and receive circuitry will be introduced. Multiplexing, sub-array beamforming and in-probe digitization will be discussed as approaches to realizing the channel-count reduction that is crucial for high-element-count 3D probes. 

The course concludes by exploring ultrasound system design in context of passive probes with separate AFEs and active probes with in-probe electronics. The challenges and techniques for data acquisition (both multiplexed and full-channel), storage and transfer will be discussed. Beamformer implementations will be introduced, with emphasis on delay-and-sum. Techniques for data post-processing for B-mode and Doppler modes will be covered. Finally, communication with active probes and the handling of pre-beamformed data will be discussed.

Medical ultrasound stands as a cornerstone in modern diagnostic imaging. The Medical Ultrasound Transducer short course at the International Ultrasonics Symposium introduces principles, design, and applications of ultrasound transducers for clinical imaging. It covers fundamental theory, recent advances, and challenges for both beginners and experienced professionals.  

Key Topics 

• Physics & Operation: Basics of ultrasound physics—wave propagation, piezoelectricity, and acoustic principles. 

• Design & Fabrication: Array configurations, material choices (ceramic, polymer, composite, and single crystal), fabrication methods, and trade-offs between sensitivity, bandwidth, and resolution.  

• Simulation & Characterization: simulation approaches, prototype characterization, and FDA requirements. 

• Clinical Applications: How transducer engineering meets diverse imaging needs in cardiology, obstetrics, musculoskeletal, and vascular diagnostics. 

• Emerging Technologies: CMUTs, integrated electronics, and AI.