Ultrasound Imaging of the Brain using Full-Waveform Inversion

Taskin, Ulas; Eikrem, Kjersti Solberg; Nævdal, Gunnar; Jakobsen, Morten; Verschuur, D.J.; Dongen, Koen W. A. van

doi:10.1109/ius46767.2020.9251665

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…Suitable data pre-processing typically allows us to model the propagation as if it occurs in an unbounded domain, i.e., no explicit boundary conditions are required (reflections from experimental equipment can be time-gated out and waves propagating deeper into the body are absorbed). See [7,80,82] for more detailed discussions on the mathematical modeling and [10,43,79] for recent extensions of FWI to UST involving hard tissues such as bone.…”

Section: Modelling Ultrasound Propagation In Soft Biological Tissuesmentioning

confidence: 99%

High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion

et al. 2021

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Ultrasound tomography (UST) scanners allow quantitative images of the human breast's acoustic properties to be derived with potential applications in screening, diagnosis and therapy planning. Time domain full waveform inversion (TD-FWI) is a promising UST image formation technique that fits the parameter fields of a wave physics model by gradient-based optimization. For high resolution 3D UST, it holds three key challenges: Firstly, its central building block, the computation of the gradient for a single US measurement, has a restrictively large memory footprint. Secondly, this building block needs to be computed for each of the 103-104 measurements, resulting in a massive parallel computation usually performed on large computational clusters for days. Lastly, the structure of the underlying optimization problem may result in slow progression of the solver and convergence to a local minimum. In this work, we design and evaluate a comprehensive computational strategy to overcome these challenges: Firstly, we exploit a gradient computation based on time reversal that dramatically reduces the memory footprint at the expense of one additional wave simulation per source. Secondly, we break the dependence on the number of measurements by using source encoding (SE) to compute stochastic gradient estimates. Also we describe a more accurate, TD-specific SE technique with a finer variance control and use a state-of-the-art stochastic LBFGS method. Lastly, we design an efficient TD multi-grid scheme together with preconditioning to speed up the convergence while avoiding local minima. All components are evaluated in extensive numerical proof-of-concept studies simulating a bowl-shaped 3D UST breast scanner prototype. Finally, we demonstrate that their combination allows us to obtain an accurate 442x442x222 voxel image with a resolution of 0.5mm using Matlab on a single GPU within 24 hours.

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Section: Modelling Ultrasound Propagation In Soft Biological Tissuesmentioning

confidence: 99%

High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion

et al. 2021

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“…Suitable data pre-processing typically allows us to model the propagation as if it occurs in an unbounded domain, i.e., no explicit boundary conditions are required (reflections from experimental equipment can be time-gated out and waves propagating deeper into the body are absorbed). See [75,77,7] for more detailed discussions on the mathematical modeling and [9,41,74] for recent extensions of FWI to UST involving hard tissues such as bone.…”

Section: Modelling Ultrasound Propagation In Soft Biological Tissuesmentioning

confidence: 99%

High Resolution 3D Ultrasonic Breast Imaging by Time-Domain Full Waveform Inversion

Lucka,

Pérez-Liva,

Treeby

et al. 2021

Preprint

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Ultrasound tomography (UST) scanners allow quantitative images of the human breast's acoustic properties to be derived with potential applications in screening, diagnosis and therapy planning. Time domain full waveform inversion (TD-FWI) is a promising UST image formation technique that fits the parameter fields of a wave physics model by gradient-based optimization. For high resolution 3D UST, it holds three key challenges: Firstly, its central building block, the computation of the gradient for a single US measurement, has a restrictively large memory footprint. Secondly, this building block needs to be computed for each of the 10 3 − 10 4 measurements, resulting in a massive parallel computation usually performed on large computational clusters for days. Lastly, the structure of the underlying optimization problem may result in slow progression of the solver and convergence to a local minimum. In this work, we design and evaluate a comprehensive computational strategy to overcome these challenges: Firstly, we introduce a novel gradient computation based on time reversal that dramatically reduces the memory footprint at the expense of one additional wave simulation per source. Secondly, we break the dependence on the number of measurements by using source encoding (SE) to compute stochastic gradient estimates. Also we describe a more accurate, TD-specific SE technique with a finer variance control and use a state-of-the-art stochastic LBFGS method. Lastly, we design an efficient TD multi-grid scheme together with preconditioning to speed up the convergence while avoiding local minima. All components are evaluated in extensive numerical proof-of-concept studies simulating a bowl-shaped 3D UST breast scanner prototype. Finally, we demonstrate that their combination allows us to obtain an accurate 442 × 442 × 222 voxel image with a resolution of 0.5 mm using Matlab on a single GPU within 24 hours.

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“…In this paper, we show that the conditions for elastodynamic Green's function retrieval are significantly better when an elastic volume can be accessed from two sides, as is the case in particular laboratory experiments [12], non-destructive testing [13,14], brain imaging [15,16], transcranial ultrasound focusing [17,18], transcranial photoacoustics [19,20], or by using auxiliary downhole receivers in seismic data acquisition [21,22]. Although the underlying representations of our work could be extended to account for lateral variations [11], the presence of a free surface [23,24] and intrinsic attenuation [25,26], we restrict ourselves to a layered lossless medium for simplicity.…”

Section: Introductionmentioning

confidence: 96%

Marchenko Green's Function Retrieval in Layered Elastic Media from Two-Sided Reflection and Transmission Data

Neut¹,

Brackenhoff²,

Meles³

et al. 2022

Preprint

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By solving a Marchenko equation, Green's functions at an arbitrary (inner) depth level inside an unknown elastic layered medium can be retrieved from single-sided reflection data, which are to be collected at the top of the medium. So far, an exact solution could only be obtained if the medium obeys stringent monotonicity conditions and if all forward-scattered (non-converted and converted) transmissions between the acquisition level and the inner depth level are known a-priori. We introduce an alternative Marchenko equation by revising the window operators that are applied in its derivation. We also introduce an auxiliary equation for transmission data, which are to be collected at the bottom of the medium, and a coupled equation, which is based on both reflection and transmission data. We show that the joint system of the Marchenko equation, the auxiliary equation and the coupled equation can be succesfully inverted when broadband reflection and transmission data are available. This results in a novel methodology for elastodynamic Green's function retrieval from two-sided data. Apart from these data, our approach requires P- and S-wave transmission times between the inner depth level and the top of the medium, as well as two angle-dependent amplitude scaling factors, which can be estimated from the data by enforcing energy conservation.

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Ultrasound Imaging of the Brain using Full-Waveform Inversion

Cited by 6 publications

References 12 publications

High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion

High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion

High Resolution 3D Ultrasonic Breast Imaging by Time-Domain Full Waveform Inversion

Marchenko Green's Function Retrieval in Layered Elastic Media from Two-Sided Reflection and Transmission Data

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