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Special Issue Editors

Dr. Richard Nauber

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Guest Editor

1. Faculty of Electrical and Computer Engineering, Technical University of Dresden, 01069 Dresden, Germany 2. Leibniz Institute for Solid State and Materials Research, 01069 Dresden, Germany
Interests: ultrasound imaging; photoacoustics; signal processing; aberration correction; beamforming; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Lars Buettner

E-Mail Website
Guest Editor

Faculty of Electrical and Computer Engineering, Technical University of Dresden, 01069 Dresden, Germany
Interests: ultrasound imaging; photoacoustics; signal processing; aberration correction; beamforming; laser- and ultrasound-based measurement techniques; adaptive wavefront shaping and aberration correcting systems; advanced measurement systems in biology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jürgen W. Czarske

E-Mail Website
Guest Editor

1. Faculty of Electrical and Computer Engineering, TU Dresden, 01069 Dresden, Germany
2. Institute of Applied Physics, Faculty of Natural Sciences, TU Dresden, 01062 Dresden, Germany
Interests: ultrasound imaging; photoacoustics; signal processing; aberration correction; beamforming; laser- and ultrasound-based measurement techniques; adaptive wavefront shaping and aberration correcting systems; advanced measurement systems in biology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The availability of enormous computational resources has spurred the recent transition from fixed-purpose devices to software-defined ultrasound platforms. This paradigm shift enables new signal processing approaches that can vastly improve the performance of an ultrasound imaging system; transitioning the image formation from conventional scanning to computational beamforming allows recording at very high framerates. The localization and tracking of nonlinear scatterers can improve the spatial resolution below the diffraction limit. Adaptive imaging and aberration correction allows one to image through scattering media. Machine-learning-based approaches can solve inverse problems in real time and make novel measurement modalities accessible. These advancements have the potential to open up a broad variety of new applications: medical imaging and diagnostics, such as functional ultrasound, experimental research of complex, turbulent flows and in situ imaging of industrial processes in harsh environments.

This Special Issue addresses the recent trend toward computational ultrasound imaging. It welcomes contributions (original research articles or reviews) from a broad spectrum of fields, which focus on the methods, implementation and applications of computational ultrasound imaging.

Dr. Richard Nauber
Dr. Lars Buettner
Prof. Dr. Jürgen W. Czarske
Guest Editors

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Keywords

methods:

super resolution
machine learning
beamforming
photoacoustics
elastography
tomography and solution to the inverse problem
compressed sensing; coded excitation
3D imaging/volumetric reconstruction

applications:

medical imaging and diagnostics
experimental research in liquids or gases; rheology
monitoring of technical, industrial and biotechnical processes; hydrology
nondestructive evaluation or testing (NDE/NDT)

Related Special Issue

Published Papers (1 paper)

Research

0 pages, 3121 KiB

Open AccessArticle

A Deep Neural Networks-Based Sound Speed Reconstruction with Enhanced Generalization by Training on a Natural Image Dataset

by Yoshiki Watanabe, Takashi Azuma and Shu Takagi

Appl. Sci. 2024, 14(1), 37; https://doi.org/10.3390/app14010037 - 20 Dec 2023

Viewed by 721

Abstract

Sound speed reconstruction has been investigated for quantitative evaluation of tissue properties in breast examination. Full waveform inversion (FWI), a mainstream method for conventional sound speed reconstruction, is an iterative method that includes numerical simulation of wave propagation, resulting in high computational cost. In contrast, high-speed reconstruction of sound speed using a deep neural network (DNN) has been proposed in recent years. Although the generalization performance is highly dependent on the training data, how to generate data for sufficient generalization performance is still unclear. In this study, the quality and generalization performance of DNN-based sound speed reconstruction with a ring array transducer were evaluated on a natural image-derived dataset and a breast phantom dataset. The DNN trained on breast phantom data (BP-DNN) could not reconstruct the structures on natural image data with diverse structures. On the other hand, the DNN trained on natural image data (NI-DNN) successfully reconstructed the structures on both natural image and breast phantom test data. Furthermore, the NI-DNN successfully reconstructed tumour structures in the breast, while the BP-DNN overlooked them. From these results, it was demonstrated that natural image data enables DNNs to learn sound speed reconstruction with high generalization performance and high resolution. Full article

(This article belongs to the Special Issue Computational Ultrasound Imaging and Applications, 2nd Edition)

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