Wavefield focusing with reduced cranial invasiveness

Meles, Giovanni Angelo; Neut, Joost van der; Dongen, Koen W. A. van; Wapenaar, Kees

doi:10.1109/ultsym.2019.8925565

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…the skull and the various air cavities. In addition to imaging and inversion, FWI methods are needed to model the acoustic wavefield when transcranial ultrasound is used for tumor ablation [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Imaging of the Brain using Full-Waveform Inversion

Taskin

Eikrem

Nævdal

et al. 2020

2020 IEEE International Ultrasonics Symposium (IUS)

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Transcranial ultrasound has been used to image the brain since 1942. Currently, it is regaining interest and full-waveform inversion (FWI) methods are now employed to reconstruct speed-of-sound profiles of the brain. Many of these methods require a good starting model. Here, we test the applicability of contrast source inversion (CSI) as a FWI method to reconstruct two-dimensional speed-of-sound profiles of the soft brain tissue enclosed by the skull. The advantage of CSI is that it can handle large acoustic contrasts without the need for a good starting model. To test the performance of CSI, we first compute synthetic data. The resulting pressure field clearly shows a significant amount of multiple scattering caused by the skull that acts as a hard acoustic contrast. Next we invert the resulting synthetic data within the Born approximation as well as by applying CSI as a FWI method. The results clearly show that Born inversion can only image the soft brain tissue in the absence of the skull whereas it generates erroneous results when the skull is present. On the other hand, with CSI it is feasible to reconstruct both the skull and the soft brain tissue accurately. Importantly, as compared to other methods CSI does not require any a priori information about the contrast, a mask or a heterogeneous starting model to reconstruct the soft tissue enclosed by the skull.

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Section: Introductionmentioning

confidence: 99%

Ultrasound Imaging of the Brain using Full-Waveform Inversion

Taskin

Eikrem

Nævdal

et al. 2020

2020 IEEE International Ultrasonics Symposium (IUS)

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“…Similar conclusions were presented by Wapenaar et al (Wapenaar et al, 2021), using a different derivation that is overall closer to previous strategies. Studies for closed boundary Marchenko-like schemes (Meles et al, 2019b) also made the observation that up-/down-decomposition inside the medium is not a necessity (Kiraz et al, 2021b). In this paper we further extend, discuss and illustrate the novel approach to focusing functions, see Chapter 6 and Diekmann and Vasconcelos (2021b).…”

Section: Introductionmentioning

confidence: 81%

“…The former technique is related to separating wavefields according to their direction of propagation, the latter to the concept of a medium that is reflection-free underneath the location of the virtual source of the Green's function. Furthermore, recent research points towards Marchenko-type integrals for other acquisition surfaces, not just a horizontal surface like that in Figure 1.2 (Meles et al, 2019b;Kiraz et al, 2020). And the relation between conventional seismic interferometry and the Marchenko integral still has to be fully understood.…”

Section: Motivationmentioning

confidence: 99%

Marchenko-type focusing functions: Generalisation, modelling and imaging

Diekmann¹

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Imaging is a field of mathematics and physics that aims to retrieve information about the internal structure of an object that can only be accessed on its boundary. Many imaging methods are based on the following principle: a source outside of the object emits a wave. The wave propagates through the object. Wherever the physical structure of the object changes, scattered waves are induced. These scattered waves are measured by receivers outside of the object, and these scattered data are used to invert for the interior composition of the medium under investigation. The Marchenko integral was originally introduced for one-dimensional inverse scattering problems in the context of quantum mechanics. It can be related to Green's functions and so-called focusing functions - fields that produce a focus when injected into a medium from a single side. About ten years ago, the Marchenko integral was extended to two and three dimensions. This paved the way for, e.g., the elimination of imaging artefacts due to multiple scattering and Green's function retrieval for virtual source locations. However, many questions about the full potential as well as the accuracy of the Marchenko equation in two and three dimensions remain unanswered. In this thesis we present a new derivation for the multidimensional Marchenko integral. Our derivation is based on a generalised framework for wavefield focusing and circumvents the limiting assumptions of the previous extension. As we use partial differential equations rather than integral equations to define focusing functions, it allows for new physical insights. For instance, our approach indicates that it is possible to model Marchenko-type focusing functions with a conventional wave equation. Ultimately, this enables us to study Marchenko-type focusing in different 2D and 3D media and learn about the accuracy of the concept. We present a straightforward modelling approach for 1D as well as a least-squares modelling approach for 2D and 3D. The latter suggests that the Marchenko integral might be inherently approximative in multiple dimensions. We also discuss Green's function retrieval with our newly derived Marchenko integral, i.e. without wavefield decomposition. This method allows for estimating Green's functions for virtual sources inside of the medium. While it requires single-sided scattering data and an estimate of the first arrival of the desired Green's function there is no need to have an actual source or receiver inside of the medium. Our results demonstrate that we can retrieve good estimates of the full-spectrum Green's functions, involving evanescent and refracted waves, which were believed to not be retrievable with the previously derived Marchenko integral. Ultimately, we discuss imaging with these Marchenko-based Green's functions. Being able to include measurements for virtual sources inside of the medium allows for a natural linearisation of the imaging problem. Thus, we use the Marchenko integral to linearise state-of-the-art imaging approaches, similar to full waveform inversion or least-squares reverse time migration, and estimate the scattering potential. Our Marchenko-based linearisation accounts for all orders of scattering and performs slightly better than a single-scattering approximation.

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Wavefield focusing with reduced cranial invasiveness

Cited by 2 publications

References 7 publications

Ultrasound Imaging of the Brain using Full-Waveform Inversion

Ultrasound Imaging of the Brain using Full-Waveform Inversion

Marchenko-type focusing functions: Generalisation, modelling and imaging

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