Short Courses

While ultrasound is widely used to assess human anatomy and physiology, it plays a very minor role in the field of molecular imaging. Recent advances are beginning to address this limitation thanks to molecular tools that allow ultrasound waves to connect to specific cellular functions.

The first part of this course will cover gas vesicles, a new class of genetically encoded ultrasound contrast agents that serve as the ‘green fluorescent protein for ultrasound’. We will review gas vesicle laboratory production techniques, gas vesicles physical properties from a molecular standpoint, engineering strategies to turn gas vesicles into reporter genes and acoustic biosensors, current and foreseeable biosensing applications, and remaining bioengineering challenges.

The second part of this course will cover imaging strategies dedicated to sensitive, specific and high-resolution gas vesicle detection. We will review gas vesicles physical properties from an acoustics standpoint, specific challenges that arise when imaging gas vesicles, latest trends in gas vesicle detection, foreseeable imaging developments, and remaining imaging challenges.

This course will focus on the theoretical and experimental aspects of quantitative ultrasound (QUS) methods based on the backscattered coefficient, envelope statistics, and ultrasound attenuation. QUS methods permit quantitative soft-tissue microstructure quantitively and in detail and have a long history of success in numerous organ systems. Attendees will become familiar with state-of-the-art methods and will be able to apply them in their own research. The course will also briefly review recent QUS successes from recent literature.

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

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.

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

Since the whole object under investigation will resonate at very specific frequencies, i.e mechanical resonances, air-gaps and other coupling issues do not preclude obtaining vibrational information, which can be directly related to mechanical properties. Three different techniques will be described, along with specific applications for each: Resonant Ultrasound Spectroscopy (small solids of regular shape), Acoustic Resonance Spectroscopy (large, complex, multi-component objects) and Swept-Frequency Acoustic Interferometry (liquids).

The course compares classical signal processing techniques with emerging artificial intelligence (AI) and deep learning methodologies for flaw detection, material characterization, imaging, and data compression. Through practical case studies, we present established approaches, including chirplet-based signal estimation and order-statistics-based flaw detection for ultrasonic inspection of materials with high microstructural scattering. These physics-based methods are contrasted with modern deep neural network architectures for automated material characterization, defect classification, and decision-making. We also present recent advances in ultrasonic data compression, through-solid data communication, and real-time System-on-Chip (SoC) hardware/software co-design for embedded NDE systems. By integrating signal theory with the ultrasonic physical properties of materials, such as wave propagation, attenuation, and scattering mechanisms, the course provides a framework for selecting and deploying optimal solutions for ultrasonic NDE applications. Experimental results demonstrate how the interpretability and reliability of classical methods can be combined with the powerful pattern-recognition capabilities of AI to advance next-generation automated ultrasonic NDE systems.

This tutorial offers a concise yet rigorous overview of acoustic tweezer technologies, emphasizing the governing physical principles. We first introduce acoustic wave propagation in fluids and solids and explain how momentum transfer from acoustic fields produces time-averaged acoustic radiation forces and torques. Using intuitive energy-landscape descriptions (e.g., Gor’kov potential), we describe stable trapping in standing- and traveling-wave fields, the formation of pressure/velocity nodes and antinodes, and how particle size, density, and compressibility determine selectivity and trapping strength.

Building on these fundamentals, we summarize key design strategies, including transducer configurations, material choices, and system architectures for generating controllable trapping patterns. We then review practical fabrication and integration workflows for ultrasound transducers, along with alignment and co-registration with optical microscopy for visualization and quantification. Finally, we survey representative applications ranging from single-cell manipulation and micro-assembly to biomechanical characterization, illustrating the technology’s impact on biomedical research and emerging diagnostic workflows. Overall, this tutorial links first-principles physics to device realization and application-driven implementation.

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 course offers a comprehensive overview of the design and operation of these resonators. Core topics include the fundamentals of piezoelectric theory including linear and nonlinear stress-strain, dielectric, and piezoelectric relationships in thin films, acoustic wave propagation, dispersion analysis, and resonator design strategies emphasizing energy confinement and suppression of lateral modes.

The course will include:

Resonator modeling approaches, ranging from the phenomenological Butterworth–Van Dyke model, the physical one-dimensional Mason model, and two- and three-dimensional electro-acoustic multi-physics finite element models.

Nonlinear resonator models for harmonic emissions and intermodulation distortion.

Key metrological performance indicators for thin films and resonators, including quality factor and electro-mechanical coupling efficiency. We will compare Berlincourt and dynamic electro-mechanical coupling formulations across different material systems and layer thicknesses and discuss their import.

This short course reviews thin film piezoelectric platforms, dispersion and waveguiding engineering, and microfabrication that extend operation from MHz ultrasonics into the millimeter wave regime. We will highlights recent advances in piezoelectric acoustic devices for applications in millimeter-wave (mmWave) signal processing, ultrasound transducers, power resonators, sensors, and emerging technologies. We begin by introducing transferred thin-film lithium niobate (LN) and sputtered scandium aluminum nitride (ScAlN) as platforms for mmWave acoustic resonators and filters, demonstrating record-high electromechanical coupling and quality factors at frequencies up to 60 GHz. We then examine high-power operation and reliability, focusing on acoustomigration, self-heating, spurious mode growth, and material and interface degradation, and we present design and test methods that increase power handling without sacrificing quality factor, bandwidth, and frequency stability. For AI design-measurement, we introduce data-driven models that map structure to key figures of merit and to the full admittance spectrum, enabling screening and optimization. We highlight sparse to dense reconstruction that recovers a full spectrum from as few as 16 evenly spaced sweep points, and inverse synthesis using a physics-guided surrogate library to propose new, spurious suppressed resonators. Finally, we discuss physics-guided simulation to real learning that corrects simulator bias under limited measurements. 

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.

Successfully translating ultrasound innovations from research to industry requires a comprehensive understanding of product development, regulatory frameworks, intellectual property strategies, and commercialization pathways. This short course is designed to equip researchers, engineers, and entrepreneurs with the critical knowledge and practical skills necessary to navigate this complex landscape and drive innovation from concept to market.

 This course will first provide a structured framework for identifying market opportunities, constructing a compelling business case, and formulating effective go-to-market strategies. Participants will gain a deep understanding of the regulatory environment, including FDA and MDR approval pathways, medical device classifications, and compliance requirements for both medical and commercial ultrasound applications. Intellectual property protection is a key consideration in ultrasound commercialization, and this course will provide best practices for patent filing, freedom-to-operate analysis, and IP portfolio management to secure competitive advantages. The entrepreneurship segment will address the essential components of launching a successful startup, from securing funding and developing a minimum viable product to navigating partnerships and scaling operations.

 A detailed exploration of the product development lifecycle will cover system design, prototyping methodologies, and the critical transition from research prototypes to scalable manufacturing. Additionally, process development and quality control strategies will be examined, focusing on regulatory compliance, risk mitigation, and the establishment of robust manufacturing pipelines. Supply chain management will also be addressed, with insights into strategic sourcing, vendor relationships, and navigating global supply chain disruptions.

 Structured into targeted sessions, this course integrates expert-led presentations, real-world case studies, and interactive discussions to provide attendees with actionable insights and industry best practices. Whether you are a researcher seeking commercialization pathways, an entrepreneur refining a business strategy, or an industry professional looking to deepen your expertise, this course offers invaluable guidance on bridging the gap between research and industry to successfully bring ultrasound technologies to market.