16 results found
Robins TC, Cueto C, Cudeiro J, et al., 2023, Dual-Probe Transcranial Full-Waveform Inversion: A Brain Phantom Feasibility Study, ULTRASOUND IN MEDICINE AND BIOLOGY, Vol: 49, Pages: 2302-2315, ISSN: 0301-5629
Cueto C, Bates O, Strong G, et al., 2023, A flexible software platform for high-performance ultrasound computed tomography Computer Methods and Programs in Biomedicine (vol 221, 106855, 2022), COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, Vol: 240, ISSN: 0169-2607
Cudeiro-blanco J, Cueto C, Bates O, et al., 2022, DESIGN AND CONSTRUCTION OF A LOW-FREQUENCY ULTRASOUND ACQUISITION DEVICE FOR 2-D BRAIN IMAGING USING FULL-WAVEFORM INVERSION, ULTRASOUND IN MEDICINE AND BIOLOGY, Vol: 48, Pages: 1995-2008, ISSN: 0301-5629
Bates O, Guasch L, Strong G, et al., 2022, A probabilistic approach to tomography and adjoint state methods, with an application to full waveform inversion in medical ultrasound, INVERSE PROBLEMS, Vol: 38, ISSN: 0266-5611
Cueto C, Guasch L, Cudeiro J, et al., 2022, Spatial response identification enables robust experimental ultrasound computed tomography, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, Vol: 69, Pages: 27-37, ISSN: 0885-3010
Ultrasound computed tomography techniques have the potential to provide clinicians with 3-D, quantitative and high-resolution information of both soft and hard tissues such as the breast or the adult human brain. Their practical application requires accurate modeling of the acquisition setup: the spatial location, orientation, and impulse response (IR) of each ultrasound transducer. However, the existing calibration methods fail to accurately characterize these transducers unless their size can be considered negligible when compared with the dominant wavelength, which reduces signal-to-noise ratios below usable levels in the presence of high-contrast tissues such as the skull. In this article, we introduce a methodology that can simultaneously estimate the location, orientation, and IR of the ultrasound transducers in a single calibration. We do this by extending spatial response identification (SRI), an algorithm that we have recently proposed to estimate transducer IRs. Our proposed methodology replaces the transducers in the acquisition device with a surrogate model whose effective response matches the experimental data by fitting a numerical model of wave propagation. This results in a flexible and robust calibration procedure that can accurately predict the behavior of the ultrasound acquisition device without ever having to know where the real transducers are or their individual IR. Experimental results using a ring acquisition system show that SRI produces calibrations of significantly higher quality than standard methodologies across all transducers, both in transmission and in reception. Experimental full-waveform inversion (FWI) reconstructions of a tissue-mimicking phantom demonstrate that SRI generates more accurate reconstructions than those produced with standard calibration techniques.
Robins T, Camacho J, Agudo OC, et al., 2021, Deep-learning-driven full-waveform inversion for ultrasound breast imaging, Sensors, Vol: 21, Pages: 1-16, ISSN: 1424-8220
Ultrasound breast imaging is a promising alternative to conventional mammography because it does not expose women to harmful ionising radiation and it can successfully image dense breast tissue. However, conventional ultrasound imaging only provides morphological information with limited diagnostic value. Ultrasound computed tomography (USCT) uses energy in both transmission and reflection when imaging the breast to provide more diagnostically relevant quantitative tissue properties, but it is often based on time-of-flight tomography or similar ray approximations of the wave equation, resulting in reconstructed images with low resolution. Full-waveform inversion (FWI) is based on a more accurate approximation of wave-propagation phenomena and can consequently produce very high resolution images using frequencies below 1 megahertz. These low frequencies, however, are not available in most USCT acquisition systems, as they use transducers with central frequencies well above those required in FWI. To circumvent this problem, we designed, trained, and implemented a two-dimensional convolutional neural network to artificially generate missing low frequencies in USCT data. Our results show that FWI reconstructions using experiment data after the application of the proposed method successfully converged, showing good agreement with X-ray CT and reflection ultrasound-tomography images.
Cueto C, Cudeiro J, Agudo OC, et al., 2021, Spatial Response Identification for Flexible and Accurate Ultrasound Transducer Calibration and its Application to Brain Imaging, IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL, Vol: 68, Pages: 143-153, ISSN: 0885-3010
Guasch L, Calderon Agudo O, Tang M-X, et al., 2020, Full-waveform inversion imaging of the human brain, npj Digital Medicine, Vol: 3, Pages: 1-12, ISSN: 2398-6352
Magnetic resonance imaging and X-ray computed tomography provide the two principal methods available for imaging the brain at high spatial resolution, but these methods are not easily portable and cannot be applied safely to all patients. Ultrasound imaging is portable and universally safe, but existing modalities cannot image usefully inside the adult human skull. We use in silico simulations to demonstrate that full-waveform inversion, a computational technique originally developed in geophysics, is able to generate accurate three-dimensional images of the brain with sub-millimetre resolution. This approach overcomes the familiar problems of conventional ultrasound neuroimaging by using the following: transcranial ultrasound that is not obscured by strong reflections from the skull, low frequencies that are readily transmitted with good signal-to-noise ratio, an accurate wave equation that properly accounts for the physics of wave propagation, and adaptive waveform inversion that is able to create an accurate model of the skull that then compensates properly for wavefront distortion. Laboratory ultrasound data, using ex vivo human skulls and in vivo transcranial signals, demonstrate that our computational experiments mimic the penetration and signal-to-noise ratios expected in clinical applications. This form of non-invasive neuroimaging has the potential for the rapid diagnosis of stroke and head trauma, and for the provision of routine monitoring of a wide range of neurological conditions.
Agudo OC, da Silva NV, Stronge G, et al., 2020, Mitigating elastic effects in marine 3-D full-waveform inversion, GEOPHYSICAL JOURNAL INTERNATIONAL, Vol: 220, Pages: 2089-2104, ISSN: 0956-540X
Guasch L, Agudo OC, Tang MX, et al., 2020, Full-waveform inversion of transmitted ultrasound to image the human brain, Pages: 3517-3521, ISSN: 1052-3812
We demonstrate that acoustic full-waveform inversion (FWI), using transmitted ultrasound, is able to reconstruct accurate high-resolution images of the human brain in three dimensions in a way that would be entirely familiar to most geophysicists. Imaging the brain through the bones of the skull has close analogies to imaging sedimentary sequences beneath complex salt bodies. Here we use adaptive waveform inversion to build the skull, and use conventional FWI to recover the brain, a similar approach to that used for sub-salt imaging. This new form of non-invasive neuroimaging has the potential for rapid diagnosis of stroke and head trauma, and for the routine monitoring of a wide range of neurological conditions.
da Silva NV, Yao G, Agudo ÒC, et al., 2019, 3D elastic semi-global waveform inversion – estimation of Vp to Vs ratio, Pages: 1440-1444, ISSN: 1052-3812
Elastic FWI depends upon an accurate estimate of a constraining Vp to Vs ratio. Such a relation can be obtained empirically from rock-physics relations or from the analysis of the seismic data. The first is case-dependent and the second requires intense human intervention. Herein, we report a new method for a semi-automatic estimation of Vp to Vs ratios from seismic data requiring only a waveform inversion algorithm and minimal data intervention. We show synthetic examples and a real-data case study.
Agudo ÓC, Vieira Da Silva N, Warner M, et al., 2018, Addressing viscous effects in acoustic full-waveform inversion, Geophysics, Vol: 83, Pages: R611-R628, ISSN: 0016-8033
In conventional full-waveform inversion (FWI), viscous effects are typically neglected, and this is likely to adversely affect the recovery of P-wave velocity. We have developed a strategy to mitigate viscous effects based on the use of matching filters with the aim of improving the performance of acoustic FWI. The approach requires an approximate estimate of the intrinsic attenuation model, and it is one to three times more expensive than conventional acoustic FWI. First, we perform 2D synthetic tests to study the impact of viscoacoustic effects on the recorded wavefield and analyze how that affects the recovered velocity models after acoustic FWI. Then, we apply the current method on the generated data and determine that it mitigates viscous effects successfully even in the presence of noise. We find that having an approximate estimate for intrinsic attenuation, even when these effects are strong, leads to improvements in resolution and a more accurate recovery of the P-wave velocity. Then, we implement and develop our method on a 2D field data set using Gabor transforms to obtain an approximate intrinsic attenuation model and inversion frequencies of up to 24 Hz. The analysis of the results indicates that there is an improvement in terms of resolution and continuity of the layers on the recovered P-wave velocity model, leading to an improved flattening of gathers and a closer match of the inverted velocity model with the migrated seismic data.
Calderon Agudo O, Vieira Da Silva N, Warner M, et al., 2018, Acoustic full-waveform inversion in an elastic world, Geophysics, Vol: 83, Pages: R257-R271, ISSN: 1942-2156
Full-waveform inversion (FWI) is a technique used to obtain high-quality velocity models of the subsurface. Despite the elastic nature of the earth, the anisotropic acoustic wave equation is typically used to model wave propagation in FWI. In part, this simplification is essential for being efficient when inverting large 3D data sets, but it has the adverse effect of reducing the accuracy and resolution of the recovered P-wave velocity models, as well as a loss in potential to constrain other physical properties, such as the S-wave velocity given that amplitude information in the observed data set is not fully used. Here, we first apply conventional acoustic FWI to acoustic and elastic data generated using the same velocity model to investigate the effect of neglecting the elastic component in field data and we find that it leads to a loss in resolution and accuracy in the recovered velocity model. Then, we develop a method to mitigate elastic effects in acoustic FWI using matching filters that transform elastic data into acoustic data and find that it is applicable to marine and land data sets. Tests show that our approach is successful: The imprint of elastic effects on the recovered P-wave models is mitigated, leading to better-resolved models than those obtained after conventional acoustic FWI. Our method requires a guess of VP/VS and is marginally more computationally demanding than acoustic FWI, but much less so than elastic FWI.Read More: https://library.seg.org/doi/10.1190/geo2017-0063.1
Agudo OC, da Silva NV, Warner M, et al., 2017, Addressing viscous effects in acoustic full-waveform inversion, Publisher: SOC EXPLORATION GEOPHYSICISTS, Pages: R611-R628, ISSN: 0016-8033
Agudo OC, Caprioli P, van Manen D-J, 2016, A spatially compact source designature filter, GEOPHYSICS, Vol: 81, Pages: V125-V139, ISSN: 0016-8033
Agudo OC, da Silva NV, Warner M, et al., 2016, Acoustic full-waveform inversion in an elastic world, Pages: 1058-1062, ISSN: 1052-3812
Despite the elastic nature of the earth, wave propagation in the subsurface is normally modeled using the acoustic anisotropic wave equation, in part due to the requirement to be efficient when dealing with large 3D datasets. This simplification has a negative effect on the quality of recovered P-wave models, as it means that amplitude information in the observed data cannot be fully utilized when applying full-waveform inversion (FWI) (Warner et al., 2013). We examine the consequences of using an acoustic wave propagator in two synthetic examples, and we propose a method to mitigate elastic effects in acoustic FWI based on matching filters. We find that our proposed approach is successful: the recovered P-wave models are better resolved than those obtained using conventional acoustic FWI.
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