Tootfinder

Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
Fangyijie Wang, Yuan Liang, Sourav Bhattacharjee, Abey Campbell, Kathleen M. Curran, Gu\'enol\'e Silvestre
https://arxiv.org/abs/2506.20407

Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
Accurate gestational age (GA) estimation, ideally through fetal ultrasound measurement, is a crucial aspect of providing excellent antenatal care. However, deriving GA from manual fetal biometric measurements depends on the operator and is time-consuming. Hence, automatic computer-assisted methods are demanded in clinical practice. In this paper, we present a novel feature fusion framework to estimate GA using fetal ultrasound images without any measurement information. We adopt a deep learning…

Design and fabrication of ultrasound linear array transducer used in ultrasound endoscope
Yuan Zhang, Mingtong Chen, Zhengbao Yang
https://arxiv.org/abs/2507.18628 https://

Design and fabrication of ultrasound linear array transducer used in ultrasound endoscope
This report details the successful construction of an ultrasound imaging platform and the design and fabrication of a novel ultrasound endoscope probe. The projects primary objective was to establish a functional system for acquiring and processing ultrasound signals, specifically targeting minimally invasive endoscopic applications. The ultrasound imaging platform was primarily designed and developed based on Texas Instruments (TI) Evaluation Modules (EVMs). It enables the transmission of 32-c…

Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation
Toufiq Musah, Chinasa Kalaiwo, Maimoona Akram, Ubaida Napari Abdulai, Maruf Adewole, Farouk Dako, Adaobi Chiazor Emegoakor, Udunna C. Anazodo, Prince Ebenezer Adjei, Confidence Raymond
https://arxiv.…

Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation
Automated segmentation of BUS images is important for precise lesion delineation and tumor characterization, but is challenged by inherent artifacts and dataset inconsistencies. In this work, we evaluate the use of a modified Residual Encoder U-Net for breast ultrasound segmentation, with a focus on uncertainty quantification. We identify and correct for data duplication in the BUSI dataset, and use a deduplicated subset for more reliable estimates of generalization performance. Epistemic uncer…

Ultrasound response to time-reversal symmetry breaking below the superconducting phase transition
Chris Halcrow, Paul Leask, Egor Babaev
https://arxiv.org/abs/2509.18922 https:/…

Ultrasound system for precise neuromodulation of human deep brain circuits #tFUS

Ultrasound system for precise neuromodulation of human deep brain circuits - Nature Communications
Modulating deep brain structure can lead to therapies for neurological conditions. Here, the authors show a transcranial ultrasound stimulation (TUS) system featuring a 256-element helmet-shaped transducer array for modulation of the LGN and connected visual cortex regions

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/2]:
- Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
Tristan S. W. Stevens, Ois\'in Nolan, Ruud J. G. van Sloun

BEAM-Net: A Deep Learning Framework with Bone Enhancement Attention Mechanism for High Resolution High Frame Rate Ultrasound Beamforming
Midhila Madhusoodanan, Mahesh Raveendranatha Panicker, Pisharody Harikrishnan Gopalakrishnan, Abhilash Rakkunedeth Hareendranathan
https://arxiv.org/abs/2507.15306

BEAM-Net: A Deep Learning Framework with Bone Enhancement Attention Mechanism for High Resolution High Frame Rate Ultrasound Beamforming
Pocket-sized, low-cost point-of-care ultrasound (POCUS) devices are increasingly used in musculoskeletal (MSK) applications for structural examination of bone tissue. However, the image quality in MSK ultrasound is often limited by speckle noise, low resolution, poor contrast, and anisotropic reflections, making bone images difficult to interpret without additional post-processing. Typically, medical ultrasound systems use delay and sum beamforming (DASB) for image reconstruction, which is not …

HV Metric For Time-Domain Full Waveform Inversion
Matej Neumann, Yunan Yang
https://arxiv.org/abs/2508.17122 https://arxiv.org/pdf/2508.17122

HV Metric For Time-Domain Full Waveform Inversion
Full-waveform inversion (FWI) is a powerful technique for reconstructing high-resolution material parameters from seismic or ultrasound data. The conventional least-squares (\(L^{2}\)) misfit suffers from pronounced non-convexity that leads to \emph{cycle skipping}. Optimal-transport misfits, such as the Wasserstein distance, alleviate this issue; however, their use requires artificially converting the wavefields into probability measures, a preprocessing step that can modify critical amplitude…

Diff-ANO: Towards Fast High-Resolution Ultrasound Computed Tomography via Conditional Consistency Models and Adjoint Neural Operators
Xiang Cao, Qiaoqiao Ding, Xinliang Liu, Lei Zhang, Xiaoqun Zhang
https://arxiv.org/abs/2507.16344

Diff-ANO: Towards Fast High-Resolution Ultrasound Computed Tomography via Conditional Consistency Models and Adjoint Neural Operators
Ultrasound Computed Tomography (USCT) constitutes a nonlinear inverse problem with inherent ill-posedness that can benefit from regularization through diffusion generative priors. However, traditional approaches for solving Helmholtz equation-constrained USCT face three fundamental challenges when integrating these priors: PDE-constrained gradient computation, discretization-induced approximation errors, and computational imbalance between neural networks and numerical PDE solvers. In this work…

🎶 Noninvasive ultrasound technique can spot bad batteries before they malfunction
https://techxplore.com/news/2025-06-noninvasive-ultrasound-technique-bad-batteries.html

Noninvasive ultrasound technique can spot bad batteries before they malfunction
A recent uptick in battery-related fires has drawn attention to the challenge of identifying defects that can cause these catastrophic malfunctions, but are rarely obvious to the naked eye. In hopes of preventing the dangerous glitches that can cause batteries to overheat and catch fire, researchers from Drexel University have developed a standard testing process to give manufacturers a better look at the internal workings of batteries.

Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
Tristan S. W. Stevens, Ois\'in Nolan, Ruud J. G. van Sloun
https://arxiv.org/abs/2508.17326 https://

Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
Echocardiography plays a central role in cardiac imaging, offering dynamic views of the heart that are essential for diagnosis and monitoring. However, image quality can be significantly degraded by haze arising from multipath reverberations, particularly in difficult-to-image patients. In this work, we propose a semantic-guided, diffusion-based dehazing algorithm developed for the MICCAI Dehazing Echocardiography Challenge (DehazingEcho2025). Our method integrates a pixel-wise noise model, der…

4D Computational Ultrasound Imaging of Carotid Artery Flow
Yuyang Hu, Michael Brown, Didem Dogan, Mah\'e Bulot, Maxime Cheppe, Guillaume Ferin, Geert Leus, Antonius F. W. van der Steen, Pieter Kruizinga, Johannes G. Bosch
https://arxiv.org/abs/2509.20963

4D Computational Ultrasound Imaging of Carotid Artery Flow
Computational ultrasound imaging (cUSi) with few elements and spatial field encoding can provide high-resolution volumetric B-mode imaging. In this work, we extend its application to 4D carotid artery (CA) flow imaging using a custom large-aperture 240-element matrix probe. We implemented a frequency band-based matched filtering strategy that balances resolution and contrast. The system's inherent imaging capabilities were evaluated and validated in flow phantom and human CA experiments. In the…

EE Times: How ultrasound UI will shape the future of AR glasses #AR/#XR

ListenToJESD204B: A Lightweight Open-Source JESD204B IP Core for FPGA-Based Ultrasound Acquisition systems
Soumyo Bhattacharjee, Federico Villani, Christian Vogt, Andrea Cossettini, Luca Benini
https://arxiv.org/abs/2508.14798

ListenToJESD204B: A Lightweight Open-Source JESD204B IP Core for FPGA-Based Ultrasound Acquisition systems
The demand for hundreds of tightly synchronized channels operating at tens of MSPS in ultrasound systems exceeds conventional low-voltage differential signaling links' bandwidth, pin count, and latency. Although the JESD204B serial interface mitigates these limitations, commercial FPGA IP cores are proprietary, costly, and resource-intensive. We present ListenToJESD204B, an open-source receiver IP core released under a permissive Solderpad 0.51 license for AMD Xilinx Zynq UltraScale+ devices. W…

EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence
Chaoyin She, Ruifang Lu, Lida Chen, Wei Wang, Qinghua Huang
https://arxiv.org/abs/2509.14977

EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence
Ultrasound imaging has become the preferred imaging modality for early cancer screening due to its advantages of non-ionizing radiation, low cost, and real-time imaging capabilities. However, conventional ultrasound diagnosis heavily relies on physician expertise, presenting challenges of high subjectivity and low diagnostic efficiency. Vision-language models (VLMs) offer promising solutions for this issue, but existing general-purpose models demonstrate limited knowledge in ultrasound medical …

Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data
Mohammad Hosseini, Maryam M. Shanechi
https://arxiv.org/abs/2509.18507 https://…

Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data
High-dimensional imaging of neural activity, such as widefield calcium and functional ultrasound imaging, provide a rich source of information for understanding the relationship between brain activity and behavior. Accurately modeling neural dynamics in these modalities is crucial for understanding this relationship but is hindered by the high-dimensionality, complex spatiotemporal dependencies, and prevalent behaviorally irrelevant dynamics in these modalities. Existing dynamical models often …

A Computer Vision and Depth Sensor-Powered Smart Cane for Real-Time Obstacle Detection and Navigation Assistance for the Visually Impaired
Sunkalp Chandra, Umang Sharma, Devesh Khilnani
https://arxiv.org/abs/2508.16698

A Computer Vision and Depth Sensor-Powered Smart Cane for Real-Time Obstacle Detection and Navigation Assistance for the Visually Impaired
Visual impairment impacts more than 2.2 billion people worldwide, and it greatly restricts independent mobility and access. Conventional mobility aids - white canes and ultrasound-based intelligent canes - are inherently limited in the feedback they can offer and generally will not be able to differentiate among types of obstacles in dense or complex environments. Here, we introduce the IoT Cane, an internet of things assistive navigation tool that integrates real-time computer vision with a tr…

tiDAS: a time invariant approximation of the Delay and Sum algorithm for biomedical ultrasound PSF reconstructions
Chiara Razzetta, Sara Garbarino, Michele Piana, Marco Crocco, Federico Benvenuto
https://arxiv.org/abs/2507.15464

tiDAS: a time invariant approximation of the Delay and Sum algorithm for biomedical ultrasound PSF reconstructions
Ultrasound imaging is a real-time diagnostic modality that reconstructs acoustic signals into visual representations of internal body structures. A key component in this process is beamforming, with the Delay and Sum (DAS) algorithm being a standard due to its balance between simplicity and effectiveness. However, the computational cost of DAS can be a limiting factor, especially in real-time scenarios where fast frame reconstruction is essential. In this work, we introduce a time-invariant app…

UltraHiT: A Hierarchical Transformer Architecture for Generalizable Internal Carotid Artery Robotic Ultrasonography
Teng Wang, Haojun Jiang, Yuxuan Wang, Zhenguo Sun, Xiangjie Yan, Xiang Li, Gao Huang
https://arxiv.org/abs/2509.13832

UltraHiT: A Hierarchical Transformer Architecture for Generalizable Internal Carotid Artery Robotic Ultrasonography
Carotid ultrasound is crucial for the assessment of cerebrovascular health, particularly the internal carotid artery (ICA). While previous research has explored automating carotid ultrasound, none has tackled the challenging ICA. This is primarily due to its deep location, tortuous course, and significant individual variations, which greatly increase scanning complexity. To address this, we propose a Hierarchical Transformer-based decision architecture, namely UltraHiT, that integrates high-lev…

A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories
Haojun Yu, Youcheng Li, Zihan Niu, Nan Zhang, Xuantong Gong, Huan Li, Zhiying Zou, Haifeng Qi, Zhenxiao Cao, Zijie Lan, Xingjian Yuan, Jiating He, Haokai Zhang, Shengtao Zhang, Zicheng Wang, Dong Wang, Ziwei Zhao, Congying Chen, Yong Wang, Wangyan Qin, Qingli Zhu
https…

A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories
Breast ultrasound (BUS) is an essential tool for diagnosing breast lesions, with millions of examinations per year. However, publicly available high-quality BUS benchmarks for AI development are limited in data scale and annotation richness. In this work, we present BUS-CoT, a BUS dataset for chain-of-thought (CoT) reasoning analysis, which contains 11,439 images of 10,019 lesions from 4,838 patients and covers all 99 histopathology types. To facilitate research on incentivizing CoT reasoning, …

Ruggedized Ultrasound Sensing in Harsh Conditions: eRTIS in the wild
Dennis Laurijssen, Wouter Jansen, Arne Aerts, Walter Daems, Jan Steckel
https://arxiv.org/abs/2509.10029 htt…

Ruggedized Ultrasound Sensing in Harsh Conditions: eRTIS in the wild
We present eRTIS, a rugged, embedded ultrasound sensing system for use in harsh industrial environments. The system features a broadband capacitive transducer and a 32-element MEMS microphone array capable of 2D and 3D beamforming. A modular hardware architecture separates sensing and processing tasks: a high-performance microcontroller handles excitation signal generation and data acquisition, while an NVIDIA Jetson module performs GPU-accelerated signal processing. eRTIS supports external syn…

Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Measurement and Potential Field-Based Patient Modeling for Model-Mediated Tele-ultrasound
Ryan S. Yeung, David G. Black, Septimiu E. Salcudean

SCRNet: Spatial-Channel Regulation Network for Medical Ultrasound Image Segmentation
Weixin Xu, Ziliang Wang
https://arxiv.org/abs/2508.13899 https://arxiv…

SCRNet: Spatial-Channel Regulation Network for Medical Ultrasound Image Segmentation
Medical ultrasound image segmentation presents a formidable challenge in the realm of computer vision. Traditional approaches rely on Convolutional Neural Networks (CNNs) and Transformer-based methods to address the intricacies of medical image segmentation. Nevertheless, inherent limitations persist, as CNN-based methods tend to disregard long-range dependencies, while Transformer-based methods may overlook local contextual information. To address these deficiencies, we propose a novel Feature…

Magneto-elastic softening in cold-sprayed polycrystalline nickel studied by resonant ultrasound spectroscopy
Michaela Janovsk\'a (Institute of Thermomechanics, Czech Acad Sci, Prague, Czechia), Petr Sedl\'ak (Institute of Thermomechanics, Czech Acad Sci, Prague, Czechia), Martin \v{S}ev\v{c}\'ik (Institute of Thermomechanics, Czech Acad Sci, Prague, Czechia), Jan Cizek (Institute of Plasma Physics, Czech Acad Sci, Prague, Czechia, Faculty of Mechanical Engineering, Czech Te…

Magneto-elastic softening in cold-sprayed polycrystalline nickel studied by resonant ultrasound spectroscopy
Cold-sprayed metallic deposits are additively manufactured materials containing high levels of compressive residual stress. Here we show that the presence and intensity of this stress can be analyzed using laser-ultrasonics, provided that the sprayed material is ferromagnetic and magnetostrictive, as in the case of pure nickel. Contactless resonant ultrasound spectroscopy is used to monitor the evolution of shear modulus and internal friction parameter of two polycrystalline Ni deposits with te…

Amorphous Silicon-Carbide Photonics for Ultrasound Imaging
R. Tufan Erdogan, Bruno Lopez-Rodriguez, Wouter J. Westerveld, Sophinese Iskander-Rizk, Gerard J. Verbiest, Iman Esmaeil Zadeh, Peter G. Steeneken
https://arxiv.org/abs/2507.04353

Amorphous Silicon-Carbide Photonics for Ultrasound Imaging
Photonic ultrasound sensors offer unparalleled potential for improving spatial and temporal resolution in ultrasound imaging applications due to their size-independent noise figure, high sensitivity, and broad bandwidth characteristics. The exploration of optical materials in ultrasound sensing has attracted significant interest due to their potential to further enhance device performance, fabrication compatibility, and long-term stability. This work introduces amorphous silicon carbide (a-SiC)…

ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound
Pouyan Navard, Yasemin Ozkut, Srikar Adhikari, Elaine Situ-LaCasse, Josie Acu\~na, Adrienne Yarnish, Alper Yilmaz
https://arxiv.org/abs/2508.04735

ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound
Retinal detachment (RD) is a vision-threatening condition that requires timely intervention to preserve vision. Macular involvement -- whether the macula is still intact (macula-intact) or detached (macula-detached) -- is the key determinant of visual outcomes and treatment urgency. Point-of-care ultrasound (POCUS) offers a fast, non-invasive, cost-effective, and accessible imaging modality widely used in diverse clinical settings to detect RD. However, ultrasound image interpretation is limite…

Prostate Capsule Segmentation from Micro-Ultrasound Images using Adaptive Focal Loss
Kaniz Fatema, Vaibhav Thakur, Emad A. Mohammed
https://arxiv.org/abs/2509.15595 https://

Prostate Capsule Segmentation from Micro-Ultrasound Images using Adaptive Focal Loss
Micro-ultrasound (micro-US) is a promising imaging technique for cancer detection and computer-assisted visualization. This study investigates prostate capsule segmentation using deep learning techniques from micro-US images, addressing the challenges posed by the ambiguous boundaries of the prostate capsule. Existing methods often struggle in such cases, motivating the development of a tailored approach. This study introduces an adaptive focal loss function that dynamically emphasizes both har…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonan...
Mathilde Faanes, Ragnhild Holden Helland, Ole Solheim, S\'ebastien Muller, Ingerid Reinertsen

Label Uncertainty for Ultrasound Segmentation
Malini Shivaram, Gautam Rajendrakumar Gare, Laura Hutchins, Jacob Duplantis, Thomas Deiss, Thales Nogueira Gomes, Thong Tran, Keyur H. Patel, Thomas H Fox, Amita Krishnan, Deva Ramanan, Bennett DeBoisblanc, Ricardo Rodriguez, John Galeotti
https://arxiv.org/abs/2508.15635

Label Uncertainty for Ultrasound Segmentation
In medical imaging, inter-observer variability among radiologists often introduces label uncertainty, particularly in modalities where visual interpretation is subjective. Lung ultrasound (LUS) is a prime example-it frequently presents a mixture of highly ambiguous regions and clearly discernible structures, making consistent annotation challenging even for experienced clinicians. In this work, we introduce a novel approach to both labeling and training AI models using expert-supplied, per-pixe…

COVID-BLUeS -- A Prospective Study on the Value of AI in Lung Ultrasound Analysis
Nina Wiedemann, Dianne de Korte-de Boer, Matthias Richter, Sjors van de Weijer, Charlotte Buhre, Franz A. M. Eggert, Sophie Aarnoudse, Lotte Grevendonk, Steffen R\"ober, Carlijn M. E. Remie, Wolfgang Buhre, Ronald Henry, Jannis Born
https://arxiv.org/abs…

COVID-BLUeS -- A Prospective Study on the Value of AI in Lung Ultrasound Analysis
As a lightweight and non-invasive imaging technique, lung ultrasound (LUS) has gained importance for assessing lung pathologies. The use of Artificial intelligence (AI) in medical decision support systems is promising due to the time- and expertise-intensive interpretation, however, due to the poor quality of existing data used for training AI models, their usability for real-world applications remains unclear. In a prospective study, we analyze data from 63 COVID-19 suspects (33 positive) coll…

SUAD: Solid-Channel Ultrasound Injection Attack and Defense to Voice Assistants
Chao Liu, Zhezheng Zhu, Hao Chen, Zhe Chen, Kaiwen Guo, Penghao Wang, Jun Luo
https://arxiv.org/abs/2508.02116

SUAD: Solid-Channel Ultrasound Injection Attack and Defense to Voice Assistants
As a versatile AI application, voice assistants (VAs) have become increasingly popular, but are vulnerable to security threats. Attackers have proposed various inaudible attacks, but are limited by cost, distance, or LoS. Therefore, we propose \name~Attack, a long-range, cross-barrier, and interference-free inaudible voice attack via solid channels. We begin by thoroughly analyzing the dispersion effect in solid channels, revealing its unique impact on signal propagation. To avoid distortions i…

From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound
Max Kr\"ahenmann, Sergio Tascon-Morales, Fabian Laumer, Julia E. Vogt, Ece Ozkan
https://arxiv.org/abs/2508.14552

From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound
Volumetric ultrasound has the potential to significantly improve diagnostic accuracy and clinical decision-making, yet its widespread adoption remains limited by dependence on specialized hardware and restrictive acquisition protocols. In this work, we present a novel unsupervised framework for reconstructing 3D anatomical structures from freehand 2D transvaginal ultrasound (TVS) sweeps, without requiring external tracking or learned pose estimators. Our method adapts the principles of Gaussian…

Developing a Framework to Simulate Quantitative Ultrasound Flow and Tissue Motion for Ultrafast Doppler Ultrasound
Qiang Fu, Changhui Li
https://arxiv.org/abs/2509.05464 https:/…

Developing a Framework to Simulate Quantitative Ultrasound Flow and Tissue Motion for Ultrafast Doppler Ultrasound
Ultrafast power Doppler imaging (uPDI) has made significant progress and become an important imaging method for both research and clinical implementations. While, it lacks simulation tools that can perform three-dimensional (3D) quantitative flow with tissue motion close to realistic conditions. In this study, we explore to construct an open-source framework, named 3D-Fully Quantitative Flow (3D-FQFlow), to provide quantitative modeling of 3D vascular flow with tissue motion and uPDI imaging. T…

Force-Based Viscosity and Elasticity Measurements for Material Biomechanical Characterisation with a Collaborative Robotic Arm
Luca Beber, Edoardo Lamon, Giacomo Moretti, Matteo Saveriano, Luca Fambri, Luigi Palopoli, Daniele Fontanelli
https://arxiv.org/abs/2507.11133

Force-Based Viscosity and Elasticity Measurements for Material Biomechanical Characterisation with a Collaborative Robotic Arm
Diagnostic activities, such as ultrasound scans and palpation, are relatively low-cost. They play a crucial role in the early detection of health problems and in assessing their progression. However, they are also error-prone activities, which require highly skilled medical staff. The use of robotic solutions can be key to decreasing the inherent subjectivity of the results and reducing the waiting list. For a robot to perform palpation or ultrasound scans, it must effectively manage physical i…

Transcranial focused ultrasound for identifying the neural substrate of conscious perception https://arxiv.org/abs/2507.08517 Indeed I see long-term potential for phased-array tFUS as a visual prosthesis for the late-blind. The company Nudge is developing a phased-array tFUS helmet.

Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception
Identifying what aspects of brain activity are responsible for conscious perception remains one of the most challenging problems in science. While progress has been made through psychophysical studies employing EEG and fMRI, research would greatly benefit from improved methods for stimulating the brain in healthy human subjects. Traditional techniques for neural stimulation through the skull, including electrical or magnetic stimulation, suffer from coarse spatial resolution and have limited ab…

Miniaturized optically-generated Bessel beam ultrasound for volumetric transcranial brain stimulation
Yueming Li, Guo Chen, Tiago R. Oliveira, Nick Todd, Yong-Zhi Zhang, Carolyn Marar, Nan Zheng, Lu Lan, Nathan McDannold, Ji-Xin Cheng, Chen Yang
https://arxiv.org/abs/2507.06108

Miniaturized optically-generated Bessel beam ultrasound for volumetric transcranial brain stimulation
Non-invasive stimulation of small, variably shaped brain sub-regions is crucial for advancing our understanding of brain functions. Current ultrasound neuromodulation faces two significant trade-offs when targeting brain sub-regions: miniaturization versus volumetric control and spatial resolution versus transcranial capability. Here, we present an optically-generated Bessel beam ultrasound (OBUS) device designed to overcome these limitations. This 2.33 mm-diameter miniaturized device delivers …

Full-Wave Modeling of Transcranial Ultrasound using Volume-Surface Integral Equations and CT-Derived Heterogeneous Skull Data
Alberto Almuna-Morales, Danilo Aballay, Pierre G\'elat, Reza Haqshenas, Elwin van 't Wout
https://arxiv.org/abs/2508.11100

Full-Wave Modeling of Transcranial Ultrasound using Volume-Surface Integral Equations and CT-Derived Heterogeneous Skull Data
Transcranial ultrasound therapy uses focused acoustic energy to induce therapeutic bioeffects in the brain. Ultrasound is transmitted through the skull, which is highly attenuating and heterogeneous, causing beam distortion, reducing focal pressure, and shifting the target location. Computational models are frequently used for predicting beam aberration, assessing cranial heating, and correcting the phase of ultrasound transducers. These models often rely on computed tomography (CT) images to b…

DualTrack: Sensorless 3D Ultrasound needs Local and Global Context
Paul F. R. Wilson, Matteo Ronchetti, R\"udiger G\"obl, Viktoria Markova, Sebastian Rosenzweig, Raphael Prevost, Parvin Mousavi, Oliver Zettinig
https://arxiv.org/abs/2509.09530

DualTrack: Sensorless 3D Ultrasound needs Local and Global Context
Three-dimensional ultrasound (US) offers many clinical advantages over conventional 2D imaging, yet its widespread adoption is limited by the cost and complexity of traditional 3D systems. Sensorless 3D US, which uses deep learning to estimate a 3D probe trajectory from a sequence of 2D US images, is a promising alternative. Local features, such as speckle patterns, can help predict frame-to-frame motion, while global features, such as coarse shapes and anatomical structures, can situate the sc…

Crosslisted article(s) found for physics.comp-ph. https://arxiv.org/list/physics.comp-ph/new
[1/1]:
- Full-Wave Modeling of Transcranial Ultrasound using Volume-Surface Integral Equations and CT-Deri...
Alberto Almuna-Morales, Danilo Aballay, Pierre G\'elat, Reza Haqsh…

AURA-CVC: Autonomous Ultrasound-guided Robotic Assistance for Central Venous Catheterization
Deepak Raina, Lidia Al-Zogbi, Brian Teixeira, Vivek Singh, Ankur Kapoor, Thorsten Fleiter, Muyinatu A. Lediju Bell, Vinciya Pandian, Axel Krieger
https://arxiv.org/abs/2507.05979

AURA-CVC: Autonomous Ultrasound-guided Robotic Assistance for Central Venous Catheterization
Purpose: Central venous catheterization (CVC) is a critical medical procedure for vascular access, hemodynamic monitoring, and life-saving interventions. Its success remains challenging due to the need for continuous ultrasound-guided visualization of a target vessel and approaching needle, which is further complicated by anatomical variability and operator dependency. Errors in needle placement can lead to life-threatening complications. While robotic systems offer a potential solution, achiev…

Impact of early-age exposure to power ultrasound on the micromechanical properties of hardened cement paste
Martin Chaigne, S\'ebastien Manneville, Michael Haist, Roland J. -M. Pellenq, Thibaut Divoux
https://arxiv.org/abs/2509.02183

Impact of early-age exposure to power ultrasound on the micromechanical properties of hardened cement paste
Cement paste serves as the universal binder in concrete, formed by mixing water with Ordinary Portland Cement (OPC). In its fresh state, cement paste is a suspension in which the initial particle inventory continuously dissolves, while simultaneously, a growing population of colloidal calcium silica-hydrate (C-S-H) particles forms and progressively builds a percolated structure that densifies and hardens through hydration. Like most colloidal gels, cement paste is sensitive to external stimuli …

Research on Ultrasonic Imaging Systems Based on Scattering Field Theory
Kai Yabumoto, Takayoshi Yumii, Kenjiro Kimura
https://arxiv.org/abs/2508.14469 https://

Research on Ultrasonic Imaging Systems Based on Scattering Field Theory
In an ultrasonic array system, increasing the aperture size to achieve a high resolution requires more transmit and receive channels, thus making it essential to have an analysis technique that can reconstruct the shape and physical properties of scatterers in a finite time based on measurement data obtained from numerous ultrasonic elements. Herein, we developed an ultrasound imaging technique using a two-dimensional ultrasonic array system comprising N^2 transmitter/receiver pairs and a techn…

Intelligent Virtual Sonographer (IVS): Enhancing Physician-Robot-Patient Communication
Tianyu Song, Feng Li, Yuan Bi, Angelos Karlas, Amir Yousefi, Daniela Branzan, Zhongliang Jiang, Ulrich Eck, Nassir Navab
https://arxiv.org/abs/2507.13052

Intelligent Virtual Sonographer (IVS): Enhancing Physician-Robot-Patient Communication
The advancement and maturity of large language models (LLMs) and robotics have unlocked vast potential for human-computer interaction, particularly in the field of robotic ultrasound. While existing research primarily focuses on either patient-robot or physician-robot interaction, the role of an intelligent virtual sonographer (IVS) bridging physician-robot-patient communication remains underexplored. This work introduces a conversational virtual agent in Extended Reality (XR) that facilitates …

CapsBeam: Accelerating Capsule Network based Beamformer for Ultrasound Non-Steered Plane Wave Imaging on Field Programmable Gate Array
Abdul Rahoof, Vivek Chaturvedi, Mahesh Raveendranatha Panicker, Muhammad Shafique
https://arxiv.org/abs/2509.03201

CapsBeam: Accelerating Capsule Network based Beamformer for Ultrasound Non-Steered Plane Wave Imaging on Field Programmable Gate Array
In recent years, there has been a growing trend in accelerating computationally complex non-real-time beamforming algorithms in ultrasound imaging using deep learning models. However, due to the large size and complexity these state-of-the-art deep learning techniques poses significant challenges when deploying on resource-constrained edge devices. In this work, we propose a novel capsule network based beamformer called CapsBeam, designed to operate on raw radio-frequency data and provide an en…

Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study
Mohammad Abbadi, Yassine Himeur, Shadi Atalla, Wathiq Mansoor
https://arxiv.org/abs/2509.05004

Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study
Breast cancer remains a leading cause of cancer-related mortality among women worldwide. Ultrasound imaging, widely used due to its safety and cost-effectiveness, plays a key role in early detection, especially in patients with dense breast tissue. This paper presents a comprehensive study on the application of machine learning and deep learning techniques for breast cancer classification using ultrasound images. Using datasets such as BUSI, BUS-BRA, and BrEaST-Lesions USG, we evaluate classica…

ConamArray: A 32-Element Broadband MEMS Ultrasound Transducer Array
Dennis Laurijssen, Rens Baeyens, Walter Daems, Jan Steckel
https://arxiv.org/abs/2509.01372 https://

ConamArray: A 32-Element Broadband MEMS Ultrasound Transducer Array
This paper presents the ConamArray, a compact broadband ultrasound transducer array composed of 32 MEMS loudspeakers. Unlike conventional broadband transducers, which are typically large and require high driving voltages, the proposed array combines small form factor MEMS devices in a staggered two-row configuration to enable beam steering across a wide ultrasonic band. A dual-microcontroller back-end with synchronized multi-DAC outputs provides flexible waveform generation and runtime steering…

PlanTUS: A heuristic tool for prospective planning of transcranial ultrasound transducer placements
Maximilian Lueckel, Suhas Vijayakumar, Til Ole Bergmann
https://arxiv.org/abs/2506.22563

PlanTUS: A heuristic tool for prospective planning of transcranial ultrasound transducer placements
Low-intensity focused transcranial ultrasonic stimulation (TUS) offers unique depth and precision in non-invasive brain stimulation. Effective administration of TUS, however, requires precise placement of transducers to ensure selective neural target exposure and engagement. This process is constrained by individual skull anatomy, hardware limitations, as well as computational and time costs of running exhaustive acoustic simulations to optimize transducer placement. To address these challenges…

Dynamic film thickness measurement in a rolling bearing using numerical elastohydrodynamic-acoustic modelling to interpret reflected ultrasound data
Pan Dou, Yayu Li, Suhaib Ardah, Tonghai Wu, Min Yu, Tom Reddyhoff, Yaguo Lei, Daniele Dini
https://arxiv.org/abs/2506.22618

Dynamic film thickness measurement in a rolling bearing using numerical elastohydrodynamic-acoustic modelling to interpret reflected ultrasound data
The thickness of the lubricating film plays a vital role in the operational efficiency and reliability of rolling bearings. Ultrasonic reflection techniques offer a promising non-invasive approach for in situ evaluation of lubricant films.However, accurately identifying the central film thickness remains challenging due to several complex factors, including dynamic fluctuations, localized elastic deformation, cavitation effects, and variations in oil supply. This study presents a comprehensive …

UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction
Zhi Chen
https://arxiv.org/abs/2509.11108 https://

UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction
Ultrasound imaging is widely used in clinical practice due to its cost-effectiveness, mobility, and safety. However, current AI research often treats disease prediction and tissue segmentation as two separate tasks and their model requires substantial computational overhead. In such a situation, we introduce UltraUPConvNet, a computationally efficient universal framework designed for both ultrasound image classification and segmentation. Trained on a large-scale dataset containing more than 9,7…

Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception
Daniel K. Freeman, Brian Odegaard, Seung-Schik Yoo, Matthias Michel
https://arxiv.org/abs/2507.08517

Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception
Identifying what aspects of brain activity are responsible for conscious perception remains one of the most challenging problems in science. While progress has been made through psychophysical studies employing EEG and fMRI, research would greatly benefit from improved methods for stimulating the brain in healthy human subjects. Traditional techniques for neural stimulation through the skull, including electrical or magnetic stimulation, suffer from coarse spatial resolution and have limited ab…

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise
David Choi, Milos Vukadinovic, Bryan He, Christina Binder, Yuki Sahashi, David Ouyang

Improving the Elevational Focusing of Fast Orthogonal Row-Column Electronic Scanning (FORCES) Ultrasound Imaging using Retrospective Transmit Beamforming (RTB)
Michael Caulfield, Randy Palamar, Darren Dahunsi, Mohammad Rahim Sobhani, Negar Majidi, Roger Zemp
https://arxiv.org/abs/2509.09056

Improving the Elevational Focusing of Fast Orthogonal Row-Column Electronic Scanning (FORCES) Ultrasound Imaging using Retrospective Transmit Beamforming (RTB)
Recent developments in Row Column Arrays (RCAs) have presented promising options for volumetric imaging without the need for the excessive channel counts of fully wired 2D-arrays. Bias programmable RCAs, also known as Top Orthogonal to Bottom Electrode (TOBE) Arrays, show further promise in that imaging schemes, such as Fast Orthogonal Row-Column Electronic Scanning (FORCES) allow for full transmit and receive focusing everywhere in the image plane. However, due to its fixed elevational focus a…

Tactile-Guided Robotic Ultrasound: Mapping Preplanned Scan Paths for Intercostal Imaging
Yifan Zhang, Dianye Huang, Nassir Navab, Zhongliang Jiang
https://arxiv.org/abs/2507.20282

Tactile-Guided Robotic Ultrasound: Mapping Preplanned Scan Paths for Intercostal Imaging
Medical ultrasound (US) imaging is widely used in clinical examinations due to its portability, real-time capability, and radiation-free nature. To address inter- and intra-operator variability, robotic ultrasound systems have gained increasing attention. However, their application in challenging intercostal imaging remains limited due to the lack of an effective scan path generation method within the constrained acoustic window. To overcome this challenge, we explore the potential of tactile c…

USEANet: Ultrasound-Specific Edge-Aware Multi-Branch Network for Lightweight Medical Image Segmentation
Jingyi Gao, Di Wu, Baha lhnaini
https://arxiv.org/abs/2509.08860 https://…

USEANet: Ultrasound-Specific Edge-Aware Multi-Branch Network for Lightweight Medical Image Segmentation
Ultrasound image segmentation faces unique challenges including speckle noise, low contrast, and ambiguous boundaries, while clinical deployment demands computationally efficient models. We propose USEANet, an ultrasound-specific edge-aware multi-branch network that achieves optimal performance-efficiency balance through four key innovations: (1) ultrasound-specific multi-branch processing with specialized modules for noise reduction, edge enhancement, and contrast improvement; (2) edge-aware a…

🔊 New technique uses focused sound waves and holograms to control brain circuits
https://medicalxpress.com/news/2025-08-technique-focused-holograms-brain-circuits.html

New technique uses focused sound waves and holograms to control brain circuits
A new study provides the first visual evidence showing that brain circuits in living animals can be activated by ultrasound waves projected into specific patterns (holograms).

A 240 Elements Matrix Probe with Aberration Mask for 4D Carotid Artery Computational Ultrasound Imaging
Yuyang Hu, Michael Brown, Didem Dogan, Mah\'e Bulot, Maxime Cheppe, Guillaume Ferin, Geert Leus, Antonius F. W. van der Steen, Pieter Kruizinga, Johannes G. Bosch
https://arxiv.org/abs/2508.10527…

A 240 Elements Matrix Probe with Aberration Mask for 4D Carotid Artery Computational Ultrasound Imaging
Three-dimensional (3D) ultrasound provides enhanced visualization of the carotid artery (CA) anatomy and volumetric flow, offering improved accuracy for cardiovascular diagnosis and monitoring. However, fully populated matrix transducers with large apertures are complex and costly to implement. Computational ultrasound imaging (cUSi) offers a promising alternative by enabling simplified hardware design through model-based reconstruction and spatial field encoding. In this work, we present a 3D …

A Fully Transformer Based Multimodal Framework for Explainable Cancer Image Segmentation Using Radiology Reports
Enobong Adahada, Isabel Sassoon, Kate Hone, Yongmin Li
https://arxiv.org/abs/2508.13796 …

A Fully Transformer Based Multimodal Framework for Explainable Cancer Image Segmentation Using Radiology Reports
We introduce Med-CTX, a fully transformer based multimodal framework for explainable breast cancer ultrasound segmentation. We integrate clinical radiology reports to boost both performance and interpretability. Med-CTX achieves exact lesion delineation by using a dual-branch visual encoder that combines ViT and Swin transformers, as well as uncertainty aware fusion. Clinical language structured with BI-RADS semantics is encoded by BioClinicalBERT and combined with visual features utilising cro…

Replaced article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- Functional Ultrasound Imaging Combined with Machine Learning for Whole-Brain Analysis of Drug-Ind...
Deighton, Zhong, Agyeman, Choi, Liu, Lee, Maroulas, Christopoulos

Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings
Dongli He, Hu Wang, Mohammad Yaqub
https://arxiv.org/abs/2507.22802 https://ar…

Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings
Accurate fetal biometric measurements, such as abdominal circumference, play a vital role in prenatal care. However, obtaining high-quality ultrasound images for these measurements heavily depends on the expertise of sonographers, posing a significant challenge in low-income countries due to the scarcity of trained personnel. To address this issue, we leverage FetalCLIP, a vision-language model pretrained on a curated dataset of over 210,000 fetal ultrasound image-caption pairs, to perform auto…

Momentum Equation-Based Regularization and Image Registration for Two-Dimensional Ultrasound Elasticity Imaging
Olalekan A. Babaniyi, Rebecca Rodrigues, Michael S. Richards
https://arxiv.org/abs/2508.19086

Momentum Equation-Based Regularization and Image Registration for Two-Dimensional Ultrasound Elasticity Imaging
Objective: Evaluate and compare multiple mechanics-based and traditional regularization strategies within a variational image registration framework for quasi-static ultrasound elastography. Methods:We reformulate a previously proposed momentum-equation-based post-processing method (SPREME) as a regularization term directly integrated into an image registration energy functional. Four regularization types are implemented and compared: a strain magnitude ($\R_ε$), a strain magnitude with incomp…

Direct evidence for the absence of coupling between shear strain and superconductivity in Sr2RuO4
Giordano Mattoni, Thomas Johnson, Atsutoshi Ikeda, Shubhankar Paul, Jake Bobowski, Manfred Sigrist, Yoshiteru Maeno
https://arxiv.org/abs/2509.10215

Direct evidence for the absence of coupling between shear strain and superconductivity in Sr2RuO4
The superconducting symmetry of Sr2RuO4 has been intensely debated for many years. A crucial controversy recently emerged between shear-mode ultrasound experiments, which suggest a two-component order parameter, and some uniaxial pressure experiments that suggest a one-component order parameter. To resolve this controversy, we use a new approach to directly apply three different kinds of shear strain to single crystals of Sr2RuO4 and investigate the coupling to superconductivity. After characte…

EdgeSRIE: A hybrid deep learning framework for real-time speckle reduction and image enhancement on portable ultrasound systems
Hyunwoo Cho, Jongsoo Lee, Jinbum Kang, Yangmo Yoo
https://arxiv.org/abs/2507.03937

EdgeSRIE: A hybrid deep learning framework for real-time speckle reduction and image enhancement on portable ultrasound systems
Speckle patterns in ultrasound images often obscure anatomical details, leading to diagnostic uncertainty. Recently, various deep learning (DL)-based techniques have been introduced to effectively suppress speckle; however, their high computational costs pose challenges for low-resource devices, such as portable ultrasound systems. To address this issue, EdgeSRIE, which is a lightweight hybrid DL framework for real-time speckle reduction and image enhancement in portable ultrasound imaging, is …

Volumetric ultrasound imaging with a sparse matrix array and integrated fiber-optic sensing for robust needle tracking in interventional procedures
Weidong Liang, Javad Rostami, Christian Baker, Simeon West, Athanasios Diamantopoulos, Sunish Mathews, Adrien E. Desjardins, Sebastien Ourselin, Laura Peralta, Wenfeng Xia
https://arxiv.org/abs…

Volumetric ultrasound imaging with a sparse matrix array and integrated fiber-optic sensing for robust needle tracking in interventional procedures
Accurate visualization of interventional devices, such as medical needles, is essential for the safe and effective guidance of minimally invasive procedures. Ultrasound (US) imaging is widely used for needle guidance, but the two-dimensional nature of most clinical probes limits accurate three-dimensional (3D) localization, particularly of the needle tip. We present a novel system that integrates volumetric US imaging with 3D needle tracking by combining a fiber-optic hydrophone embedded in the…

UltraTac: Integrated Ultrasound-Augmented Visuotactile Sensor for Enhanced Robotic Perception
Junhao Gong, Kit-Wa Sou, Shoujie Li, Changqing Guo, Yan Huang, Chuqiao Lyu, Ziwu Song, Wenbo Ding
https://arxiv.org/abs/2508.20982

UltraTac: Integrated Ultrasound-Augmented Visuotactile Sensor for Enhanced Robotic Perception
Visuotactile sensors provide high-resolution tactile information but are incapable of perceiving the material features of objects. We present UltraTac, an integrated sensor that combines visuotactile imaging with ultrasound sensing through a coaxial optoacoustic architecture. The design shares structural components and achieves consistent sensing regions for both modalities. Additionally, we incorporate acoustic matching into the traditional visuotactile sensor structure, enabling integration o…

Systematic Evaluation of Wavelet-Based Denoising for MRI Brain Images: Optimal Configurations and Performance Benchmarks
Asadullah Bin Rahman, Masud Ibn Afjal, Md. Abdulla Al Mamun
https://arxiv.org/abs/2508.15011

Systematic Evaluation of Wavelet-Based Denoising for MRI Brain Images: Optimal Configurations and Performance Benchmarks
Medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound are essential for accurate diagnosis and treatment planning in modern healthcare. However, noise contamination during image acquisition and processing frequently degrades image quality, obscuring critical diagnostic details and compromising clinical decision-making. Additionally, enhancement techniques such as histogram equalization may inadvertently amplify existing noise artifacts, …

Leveraging Information Divergence for Robust Semi-Supervised Fetal Ultrasound Image Segmentation
Fangyijie Wang, Gu\'enol\'e Silvestre, Kathleen M. Curran
https://arxiv.org/abs/2509.06495

Leveraging Information Divergence for Robust Semi-Supervised Fetal Ultrasound Image Segmentation
Maternal-fetal Ultrasound is the primary modality for monitoring fetal development, yet automated segmentation remains challenging due to the scarcity of high-quality annotations. To address this limitation, we propose a semi-supervised learning framework that leverages information divergence for robust fetal ultrasound segmentation. Our method employs a lightweight convolutional network (1.47M parameters) and a Transformer-based network, trained jointly with labelled data through standard supe…

Time-Domain Reconstruction of the Speed of Sound in Ring-Array Ultrasound Computed Tomography with Randomized Super-Shots
Luca A. Forte
https://arxiv.org/abs/2508.09675 https://…

Time-Domain Reconstruction of the Speed of Sound in Ring-Array Ultrasound Computed Tomography with Randomized Super-Shots
Ring-array ultrasound computed tomography has recently achieved sufficient maturity for clinical applications like breast imaging. Image reconstruction is achieved with state of art iterative algorithms (full waveform inversion in the frequency domain). In this Letter, we consider a stochastic reconstruction in the time-domain. We introduce the notion of multiple super-shots and stochastic ensembles and test our inversion algorithm on publicly available experimental data. Our results show that …

Do We Need Pre-Processing for Deep Learning Based Ultrasound Shear Wave Elastography?
Sarah Grube, S\"oren Gr\"unhagen, Sarah Latus, Michael Meyling, Alexander Schlaefer
https://arxiv.org/abs/2508.03744

Do We Need Pre-Processing for Deep Learning Based Ultrasound Shear Wave Elastography?
Estimating the elasticity of soft tissue can provide useful information for various diagnostic applications. Ultrasound shear wave elastography offers a non-invasive approach. However, its generalizability and standardization across different systems and processing pipelines remain limited. Considering the influence of image processing on ultrasound based diagnostics, recent literature has discussed the impact of different image processing steps on reliable and reproducible elasticity analysis.…

Recursive Aperture Decoded Ultrasound Imaging (READI) With Estimated Motion-Compensated Compounding (EMC2)
Tyler Keith Henry, Darren Dahunsi, Randy Palamar, Negar Majidi, Mohammad Rahim Sobhani, Roger Zemp
https://arxiv.org/abs/2509.08781

Recursive Aperture Decoded Ultrasound Imaging (READI) With Estimated Motion-Compensated Compounding (EMC2)
Fast Orthogonal Row-Column Electronic Scanning (FORCES) is a Hadamard-encoded Synthetic Transmit Aperture (STA) imaging sequence using bias-sensitive Top-Orthogonal to Bottom Electrode (TOBE) arrays. It produces images with a higher Signal-to-Noise Ratio (SNR) and improved penetration depth compared to traditional STA techniques, but suffers from motion sensitivity due to ensemble size and aperture encoding. This work presents Recursive Aperture Decoded Ultrasound Imaging (READI), a novel decod…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
- Robotic Ultrasound-Guided Femoral Artery Reconstruction of Anatomically-Representative Phantoms
Lidia Al-Zogbi, Deepak Raina, Vinciya Pandian, Thorsten Fleiter, Axel Krieger

Stochastic Reconstruction of the Speed of Sound in Breast Ultrasound Computed Tomography with Phase Encoding in the Frequency Domain
Luca A. Forte
https://arxiv.org/abs/2508.08434

Stochastic Reconstruction of the Speed of Sound in Breast Ultrasound Computed Tomography with Phase Encoding in the Frequency Domain
The framework of ultrasound computed tomography (USCT) has recently re-emerged as a powerful, safe and operator-independent way to image the breast. State of the art image reconstruction methods are performed with iterative techniques based on deterministic optimization algorithms in the frequency domain in the 300 kHz - 1 MHz bandwidth. Alternative algorithms with deterministic and stochastic optimization have been considered in the time-domain. In this paper, we present the equivalent stochas…

Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data
Xuesong Li, Nassir Navab, Zhongliang Jiang
https://arxiv.org/abs/2507.06828 h…

Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data
Image denoising is a fundamental task in computer vision, particularly in medical ultrasound (US) imaging, where speckle noise significantly degrades image quality. Although recent advancements in deep neural networks have led to substantial improvements in denoising for natural images, these methods cannot be directly applied to US speckle noise, as it is not purely random. Instead, US speckle arises from complex wave interference within the body microstructure, making it tissue-dependent. Thi…

Refraction corrected specular beamforming applied to cortical bone enhances interface visibility of bone-soft tissues interfaces
Amadou S. Dia, Quentin Grimal, Guillaume Renaud
https://arxiv.org/abs/2507.08497

Refraction corrected specular beamforming applied to cortical bone enhances interface visibility of bone-soft tissues interfaces
Ultrasound imaging of the cortex of long bones may enable the measurement of the cortical thickness and the ultrasound wave speed in cortical bone tissue. However, with bone loss, the cortical porosity and the size of the pores increase, resulting in strong ultrasound diffuse scattering whose magnitude can exceed that of the specular reflection from the bone-marrow (endosteal) interface. In this study we adapt to bone a specular beamforming technique proposed to better image a needle in soft ti…

Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging
Lijie Huang, Jingyi Yin, Jingke Zhang, U-Wai Lok, Ryan M. DeRuiter, Jieyang Jin, Kate M. Knoll, Kendra E. Petersen, James D. Krier, Xiang-yang Zhu, Gina K. Hesley, Kathryn A. Robinson, Andrew J. Bentall, Thomas D. Atwell, Andrew D. Rule, Lilach O. Lerman, Shigao Chen, Chengwu Huang

Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging
Ultrasound microvascular imaging (UMI) is often hindered by low signal-to-noise ratio (SNR), especially in contrast-free or deep tissue scenarios, which impairs subsequent vascular quantification and reliable disease diagnosis. To address this challenge, we propose Half-Angle-to-Half-Angle (HA2HA), a self-supervised denoising framework specifically designed for UMI. HA2HA constructs training pairs from complementary angular subsets of beamformed radio-frequency (RF) blood flow data, across whic…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
- UltraRay: Introducing Full-Path Ray Tracing in Physics-Based Ultrasound Simulation
Felix Duelmer, Mohammad Farid Azampour, Magdalena Wysocki, Nassir Navab

Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning
Akhil John Thomas, Christiaan Boerkamp
https://arxiv.org/abs/2507.13394 https://

Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning
Nerve segmentation is crucial in medical imaging for precise identification of nerve structures. This study presents an optimized DeepLabV3-based segmentation pipeline that incorporates automated threshold fine-tuning to improve segmentation accuracy. By refining preprocessing steps and implementing parameter optimization, we achieved a Dice Score of 0.78, an IoU of 0.70, and a Pixel Accuracy of 0.95 on ultrasound nerve imaging. The results demonstrate significant improvements over baseline mod…

Temporal Representation Learning for Real-Time Ultrasound Analysis
Yves Stebler, Thomas M. Sutter, Ece Ozkan, Julia E. Vogt
https://arxiv.org/abs/2509.01433 https://

Temporal Representation Learning for Real-Time Ultrasound Analysis
Ultrasound (US) imaging is a critical tool in medical diagnostics, offering real-time visualization of physiological processes. One of its major advantages is its ability to capture temporal dynamics, which is essential for assessing motion patterns in applications such as cardiac monitoring, fetal development, and vascular imaging. Despite its importance, current deep learning models often overlook the temporal continuity of ultrasound sequences, analyzing frames independently and missing key …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[6/9]:
- UltraAD: Fine-Grained Ultrasound Anomaly Classification via Few-Shot CLIP Adaptation
Yue Zhou, Yuan Bi, Wenjuan Tong, Wei Wang, Nassir Navab, Zhongliang Jiang

Ultrasound-based detection and malignancy prediction of breast lesions eligible for biopsy: A multi-center clinical-scenario study using nomograms, large language models, and radiologist evaluation
Ali Abbasian Ardakani, Afshin Mohammadi, Taha Yusuf Kuzan, Beyza Nur Kuzan, Hamid Khorshidi, Ashkan Ghorbani, Alisa Mohebbi, Fariborz Faeghi, Sepideh Hatamikia, U Rajendra Acharya

Ultrasound-based detection and malignancy prediction of breast lesions eligible for biopsy: A multi-center clinical-scenario study using nomograms, large language models, and radiologist evaluation
To develop and externally validate integrated ultrasound nomograms combining BIRADS features and quantitative morphometric characteristics, and to compare their performance with expert radiologists and state of the art large language models in biopsy recommendation and malignancy prediction for breast lesions. In this retrospective multicenter, multinational study, 1747 women with pathologically confirmed breast lesions underwent ultrasound across three centers in Iran and Turkey. A total of 10…

Generation of realistic cardiac ultrasound sequences with ground truth motion and speckle decorrelation
Thierry Judge, Nicolas Duchateau, Khuram Faraz, Pierre-Marc Jodoin, Olivier Bernard
https://arxiv.org/abs/2509.05261

Generation of realistic cardiac ultrasound sequences with ground truth motion and speckle decorrelation
Simulated ultrasound image sequences are key for training and validating machine learning algorithms for left ventricular strain estimation. Several simulation pipelines have been proposed to generate sequences with corresponding ground truth motion, but they suffer from limited realism as they do not consider speckle decorrelation. In this work, we address this limitation by proposing an improved simulation framework that explicitly accounts for speckle decorrelation. Our method builds on an e…

Pointwise Encoding Time reduction with Radial Acquisition (PETRA) to pseudo-CT mapping for precision Focused Transcranial Ultrasound Applications
Maria Miscouridou, Alisa A. Krokhmal, Aaron T. Hess, Jon Campbell, Charlotte J. Stagg, Eleanor Martin, Bradley E. Treeby
https://arxiv.org/abs/2508.01050…

Pointwise Encoding Time reduction with Radial Acquisition (PETRA) to pseudo-CT mapping for precision Focused Transcranial Ultrasound Applications
Ultrasound simulations used for treatment planning in transcranial ultrasound stimulation(TUS) currently depend on converting computed tomography(CT) images to acoustic properties. Aiming to reduce subject exposure to ionising radiation, recent literature focused on generating pseudo-CT images from different magnetic resonance(MR) imaging sequences. Here, the PETRA (pointwise encoding time reduction with radial acquisition) sequence was used because of its stronger ability to image bones. A dat…

Coarse-to-Fine Joint Registration of MR and Ultrasound Images via Imaging Style Transfer
Junyi Wang, Xi Zhu, Yikun Guo, Zixi Wang, Haichuan Gao, Le Zhang, Fan Zhang
https://arxiv.org/abs/2508.05240

Coarse-to-Fine Joint Registration of MR and Ultrasound Images via Imaging Style Transfer
We developed a pipeline for registering pre-surgery Magnetic Resonance (MR) images and post-resection Ultrasound (US) images. Our approach leverages unpaired style transfer using 3D CycleGAN to generate synthetic T1 images, thereby enhancing registration performance. Additionally, our registration process employs both affine and local deformable transformations for a coarse-to-fine registration. The results demonstrate that our approach improves the consistency between MR and US image pairs in …

UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis
Zhuo Li, Xuhang Chen, Shuqiang Wang
https://arxiv.org/abs/2507.03341

UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis
Functional ultrasound (fUS) is a neuroimaging technique known for its high spatiotemporal resolution, enabling non-invasive observation of brain activity through neurovascular coupling. Despite its potential in clinical applications such as neonatal monitoring and intraoperative guidance, the development of fUS faces challenges related to data scarcity and limitations in generating realistic fUS images. This paper explores the use of a generative adversarial network (GAN) framework tailored for…

Influence of bone microstructure on ultrasound loss through skull-mimicking digital phantoms
Samuel Clinard, Taylor Webb, Henrik Od\'een, Dennis L. Parker, Douglas A. Christensen
https://arxiv.org/abs/2508.04838

Influence of bone microstructure on ultrasound loss through skull-mimicking digital phantoms
Transcranial focused ultrasound applications often use simulations that require accurate acoustic properties, which can be related to computed tomography (CT) Hounsfield Units (HU). However, clinical CT is insensitive to microstructure. This study examines how bone/marrow microstructure introduces variations in acoustic property relationships to CT HUs. The insertion loss was found through skull-mimicking digital phantoms with two materials (bone/marrow) from 0 to 75% porosity. The phantoms had…

TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
Qi Li, Shaheer U. Saeed, Yuliang Huang, Mingyuan Luo, Zhongnuo Yan, Jiongquan Chen, Xin Yang, Dong Ni, Nektarios Winter, Phuc Nguyen, Lucas Steinberger, Caelan Haney, Yuan Zhao, Mingjie Jiang, Bowen Ren, SiYeoul Lee, Seonho Kim, MinKyung Seo, MinWoo Kim, Yimeng Dou, Zhiwei Zhang, Yin Li, Tomy Varghese, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, Yipeng Hu

TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
Trackerless freehand ultrasound reconstruction aims to reconstruct 3D volumes from sequences of 2D ultrasound images without relying on external tracking systems, offering a low-cost, portable, and widely deployable alternative for volumetric imaging. However, it presents significant challenges, including accurate inter-frame motion estimation, minimisation of drift accumulation over long sequences, and generalisability across scanning protocols. The TUS-REC2024 Challenge was established to ben…

VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videos
Julia Wolleb, Florentin Bieder, Paul Friedrich, Hemant D. Tagare, Xenophon Papademetris
https://arxiv.org/abs/2507.21863

VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videos
Ultrasound is widely used in clinical care, yet standard deep learning methods often struggle with full video analysis due to non-standardized acquisition and operator bias. We offer a new perspective on ultrasound video analysis through implicit neural representations (INRs). We build on Functa, an INR framework in which each image is represented by a modulation vector that conditions a shared neural network. However, its extension to the temporal domain of medical videos remains unexplored. T…

Contrast-Free Ultrasound Microvascular Imaging via Radiality and Similarity Weighting
Jingyi Yin, Jingke Zhang, Lijie Huang, U-Wai Lok, Ryan M DeRuiter, Kaipeng Ji, Yanzhe Zhao, Kate M. Knoll, Kendra E. Petersen, Tao Wu, Xiang-yang Zhu, James D Krier, Kathryn A. Robinson, Lilach O Lerman, Andrew J. Bentall, Shigao Chen, Chengwu Huang
https://

Contrast-Free Ultrasound Microvascular Imaging via Radiality and Similarity Weighting
Microvascular imaging has advanced significantly with ultrafast data acquisition and improved clutter filtering, enhancing the sensitivity of power Doppler imaging to small vessels. However, the image quality remains limited by spatial resolution and elevated background noise, both of which impede visualization and accurate quantification. To address these limitations, this study proposes a high-resolution cross-correlation Power Doppler (HR-XPD) method that integrates spatial radiality weighti…

MTCNet: Motion and Topology Consistency Guided Learning for Mitral Valve Segmentationin 4D Ultrasound
Rusi Chen, Yuanting Yang, Jiezhi Yao, Hongning Song, Ji Zhang, Yongsong Zhou, Yuhao Huang, Ronghao Yang, Dan Jia, Yuhan Zhang, Xing Tao, Haoran Dou, Qing Zhou, Xin Yang, Dong Ni
https://arxiv.org/abs/2507.00660

MTCNet: Motion and Topology Consistency Guided Learning for Mitral Valve Segmentationin 4D Ultrasound
Mitral regurgitation is one of the most prevalent cardiac disorders. Four-dimensional (4D) ultrasound has emerged as the primary imaging modality for assessing dynamic valvular morphology. However, 4D mitral valve (MV) analysis remains challenging due to limited phase annotations, severe motion artifacts, and poor imaging quality. Yet, the absence of inter-phase dependency in existing methods hinders 4D MV analysis. To bridge this gap, we propose a Motion-Topology guided consistency network (MT…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
Pegios, Lin, Weng, Svendsen, Bashir, Bigdeli, Christensen, Tolsgaard, Feragen

Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound
Jian Wang, Qiongying Ni, Hongkui Yu, Ruixuan Yao, Jinqiao Ying, Bin Zhang, Xingyi Yang, Jin Peng, Jiongquan Chen, Junxuan Yu, Wenlong Shi, Chaoyu Chen, Zhongnuo Yan, Mingyuan Luo, Gaocheng Cai, Dong Ni, Jing Lu, Xin Yang
https://arxiv.org/abs/2507.00398

Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound
Accurate fetal birth weight (FBW) estimation is essential for optimizing delivery decisions and reducing perinatal mortality. However, clinical methods for FBW estimation are inefficient, operator-dependent, and challenging to apply in cases of complex fetal anatomy. Existing deep learning methods are based on 2D standard ultrasound (US) images or videos that lack spatial information, limiting their prediction accuracy. In this study, we propose the first method for directly estimating FBW from…

UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound
Junxuan Yu, Yaofei Duan, Yuhao Huang, Yu Wang, Rongbo Ling, Weihao Luo, Ang Zhang, Jingxian Xu, Qiongying Ni, Yongsong Zhou, Binghan Li, Haoran Dou, Liping Liu, Yanfen Chu, Feng Geng, Zhe Sheng, Zhifeng Ding, Dingxin Zhang, Rui Huang, Yuhang Zhang, Xiaowei Xu, Tao Tan, Dong Ni, Zhongshan Gou, Xin Yang

UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound
Echocardiography is routine for cardiac examination. However, 2D ultrasound (US) struggles with accurate metric calculation and direct observation of 3D cardiac structures. Moreover, 3D US is limited by low resolution, small field of view and scarce availability in practice. Constructing the cardiac anatomical twin from 2D images is promising to provide precise treatment planning and clinical quantification. However, it remains challenging due to the rare paired data, complex structures, and US…

Noninvasive Focused Ultrasound Spinal Cord Stimulation in Humans: A Computational Feasibility Study
Koharu Isomura, Wenwei Yu, Jose Gomez-Tames
https://arxiv.org/abs/2507.05702

Noninvasive Focused Ultrasound Spinal Cord Stimulation in Humans: A Computational Feasibility Study
Background: Trans-spinal FUS (tsFUS) has recently been shown promise in modulating spinal reflexes in rodents, opening new avenues for spinal cord interventions in motor control and pain management. However, anatomical differences between rodents and human spinal cords require careful targeting strategies and transducer design adaptations for human applications. Aim: This study aims to computationally explore the feasibility of tsFUS in the human spinal cord by leveraging the intervertebral aco…

Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising
Hojat Asgariandehkordi, Mostafa Sharifzadeh, Hassan Rivaz
https://arxiv.org/abs/2506.21499

Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising
Ultrasound Coherent Plane Wave Compounding (CPWC) enhances image contrast by combining echoes from multiple steered transmissions. While increasing the number of angles generally improves image quality, it drastically reduces the frame rate and can introduce blurring artifacts in fast-moving targets. Moreover, compounded images remain susceptible to noise, particularly when acquired with a limited number of transmissions. We propose a zero-shot denoising framework tailored for low-angle CPWC ac…

A Novel Framework for Integrating 3D Ultrasound into Percutaneous Liver Tumour Ablation
Shuwei Xing, Derek W. Cool, David Tessier, Elvis C. S. Chen, Terry M. Peters, Aaron Fenster
https://arxiv.org/abs/2506.21162

A Novel Framework for Integrating 3D Ultrasound into Percutaneous Liver Tumour Ablation
3D ultrasound (US) imaging has shown significant benefits in enhancing the outcomes of percutaneous liver tumour ablation. Its clinical integration is crucial for transitioning 3D US into the therapeutic domain. However, challenges of tumour identification in US images continue to hinder its broader adoption. In this work, we propose a novel framework for integrating 3D US into the standard ablation workflow. We present a key component, a clinically viable 2D US-CT/MRI registration approach, le…

3D Heart Reconstruction from Sparse Pose-agnostic 2D Echocardiographic Slices
Zhurong Chen, Jinhua Chen, Wei Zhuo, Wufeng Xue, Dong Ni
https://arxiv.org/abs/2507.02411

3D Heart Reconstruction from Sparse Pose-agnostic 2D Echocardiographic Slices
Echocardiography (echo) plays an indispensable role in the clinical practice of heart diseases. However, ultrasound imaging typically provides only two-dimensional (2D) cross-sectional images from a few specific views, making it challenging to interpret and inaccurate for estimation of clinical parameters like the volume of left ventricle (LV). 3D ultrasound imaging provides an alternative for 3D quantification, but is still limited by the low spatial and temporal resolution and the highly dema…

Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation
Hongyi Pan, Ziliang Hong, Gorkem Durak, Ziyue Xu, Ulas Bagci
https://arxiv.org/abs/2506.23334

Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation
Federated learning (FL) has emerged as a promising paradigm for collaboratively training deep learning models across institutions without exchanging sensitive medical data. However, its effectiveness is often hindered by limited data availability and non-independent, identically distributed data across participating clients, which can degrade model performance and generalization. To address these challenges, we propose a generative AI based data augmentation framework that integrates synthetic …

Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
Fangyijie Wang, Kevin Whelan, F\'elix Balado, Gu\'enol\'e Silvestre, Kathleen M. Curran
https://arxiv.org/abs/2506.23664

Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
Medical image data is less accessible than in other domains due to privacy and regulatory constraints. In addition, labeling requires costly, time-intensive manual image annotation by clinical experts. To overcome these challenges, synthetic medical data generation offers a promising solution. Generative AI (GenAI), employing generative deep learning models, has proven effective at producing realistic synthetic images. This study proposes a novel mask-guided GenAI approach using diffusion model…

2D Ultrasound Elasticity Imaging of Abdominal Aortic Aneurysms Using Deep Neural Networks
Utsav Ratna Tuladhar, Richard Simon, Doran Mix, Michael Richards
https://arxiv.org/abs/2508.19303

2D Ultrasound Elasticity Imaging of Abdominal Aortic Aneurysms Using Deep Neural Networks
Abdominal aortic aneurysms (AAA) pose a significant clinical risk due to their potential for rupture, which is often asymptomatic but can be fatal. Although maximum diameter is commonly used for risk assessment, diameter alone is insufficient as it does not capture the properties of the underlying material of the vessel wall, which play a critical role in determining the risk of rupture. To overcome this limitation, we propose a deep learning-based framework for elasticity imaging of AAAs with …

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
Fangyijie Wang, Kevin Whelan, F\'elix Balado, Kathleen M. Curran, Gu\'enol\'e Silvestre

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[2/2]:
- UltraBoneUDF: Self-supervised Bone Surface Reconstruction from Ultrasound Based on Neural Unsigne...
Wu, Seibold, Cavalcanti, Loggia, Reissner, Sigrist, Hein, Calvet, Vieh\"ofer, F\"urn…