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eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Speed-of-Sound Imaging using Diverging Waves
Richard Rau, Dieter Schweizer, Valery Vishnevskiy, Orcun Goksel · 2019-10-14 · via eess.SP updates on arXiv.org

Recent ultrasound imaging modalities based on ultrasound computed tomography indicate a huge potential to detect pathologies is tissue due to altered biomechanical properties. Especially the imaging of speed-of-sound (SoS) distribution in tissue has shown clinical promise and thus gained increasing attention in the field -- with several methods proposed based on transmission mode tomography. SoS imaging using conventional ultrasound (US) systems would be convenient and easy for clinical translation, but this requires using conventional US probes with single-sides tissue access and thus pulse-echo imaging sequences. Recent pulse-echo SoS imaging methods rely on plane wave (PW) insonifications, which is prone to strong aberration effects for non-homogeneous tissue composition. In this paper we propose to use diverging waves (DW) for SoS imaging and thus substantially improve the reconstruction of SoS distributions. We study this proposition by first plane wavefront aberrations compared to DW. We then present the sensitivity of both approaches to major parameterization choices on a set of simulated phantoms. Using the optimum parameter combination for each method for a given transducer model and imaging sequence, we analyze the SoS imaging performance comparatively between the two approaches. Results indicate that using DW instead of PW, the reconstruction accuracy improves substantially, by over 22% in reconstruction error (RMSE) and by 55% in contrast (CNR). We also demonstrate improvements in SoS reconstructions from an actual US acquisition of a breast phantom with tumor- and cyst-representative inclusions, with high and low SoS contrast, respectively.