Design and Construction of a Low-Frequency Ultrasound Acquisition Device for 2-D Brain Imaging Using Full-Waveform Inversion

Cudeiro-Blanco, Javier; Cueto, Carlos; Bates, Oscar; Strong, George; Robins, Thomas; Toulemonde, Matthieu; Warner, Mike; Tang, Meng‐Xing; Agudo, Òscar Calderón; Guasch, Lluís

doi:10.1016/j.ultrasmedbio.2022.05.023

Cited by 11 publications

(7 citation statements)

References 38 publications

(38 reference statements)

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“…Two single-element or clinical array transducers could be purchased and mounted to a rotation stage to sample a virtual array [16,17], but this configuration has a large data acquisition time and so multi-element transducer arrays that fully surround the object are typically used instead. Either bowl [18] or rotating planar [19] configurations are used in 3D, but the most common design is a vertically translated 2D ring array [20,17,21], since these allow data to be collected and reconstructed in 2D slices, which is computationally efficient. The standard open-UST configuration is a 2D ring array, but its modularity also allows reconfiguration into 3D geometries.…”

Section: Array Designmentioning

confidence: 99%

See 1 more Smart Citation

open-UST: An Open-Source Ultrasound Tomography Transducer Array System

Roberts¹,

Martin²,

Brown³

et al. 2023

Preprint

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Fast imaging methods are needed to promote widespread clinical adoption of Ultrasound Tomography (UST), and more widely available UST hardware could support the experimental validation of new measurement configurations. In this work, an open-source 256-element transducer ring array was developed (morganjroberts.github.io/open-UST) and manufactured using rapid prototyping, for only £2k. Novel manufacturing techniques were used, resulting in a 1.17 • mean beam axis skew angle, a 104 µm mean element position error, and a ±13.6 µm deviation in matching layer thickness. The nominal acoustic performance was measured using hydrophone scans and watershot data, and the 61.2 dB SNR, 55.4 • opening angle, 16.3 mm beamwidth and 54% transmit-receive bandwidth (-12 dB), were found to be similar to existing systems, and compatible with full waveform inversion reconstruction methods. The inter-element variation in acoustic performance was typically <10% without using normalisation, meaning that the elements can be modelled identically during image reconstruction, removing the need for individual source definitions based on hydrophone measurements. Finally, data from a phantom experiment was successfully reconstructed. These results demonstrate that the open-UST system is accessible for users, and suitable for UST imaging research.

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Section: Array Designmentioning

confidence: 99%

“…Current UST systems have between 40 [21] and 2304 [18] transducer elements. Systems with many elements have denser sampling and higher image quality, but are complex to manufacture and require a data acquisition system (DAQ) with an equivalent channel count, or a multiplexer.…”

Section: Array Designmentioning

confidence: 99%

open-UST: An Open-Source Ultrasound Tomography Transducer Array System

Roberts¹,

Martin²,

Brown³

et al. 2023

Preprint

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show abstract

“…Following the pioneering work by Guasch et al (2020), it was shown that modeling all aspects of the acoustic wavefield enables high-resolution imaging of brain structures and anomalies. Since then, many works Taskin et al (2020); Marty et al (2021); Tong et al (2022); Cudeiro-Blanco et al (2022); are demonstrating increasing evidence from both in silico and controlled laboratory experiments that these full wavefield methods are capable of producing reliable brain images bringing this novel approach closer to clinical viability. These full wavefield methods are denoted full-waveform inversion (FWI) and are adapted from sophisticated seismic imaging methods (Virieux and Operto, 2009;Tarantola and Valette, 1982).…”

Section: Introductionmentioning

confidence: 99%

Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification

Orozco

Siahkoohi

Rizzuti

et al. 2023

Medical Imaging 2023: Image Processing

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Machine learning algorithms are powerful tools in Bayesian uncertainty quantification (UQ) of inverse problems. Unfortunately, when using these algorithms medical imaging practitioners are faced with the challenging task of manually defining neural networks that can handle complicated inputs such as acoustic data. This task needs to be replicated for different receiver types or configurations since these change the dimensionality of the input. We propose to first transform the data using the adjoint operator -ex: time reversal in photoacoustic imaging (PAI) or back-projection in computer tomography (CT) imaging -then continue posterior inference using the adjoint data as an input now that it has been standardized to the size of the unknown model. This adjoint preprocessing technique has been used in previous works but with minimal discussion on if it is biased. In this work, we prove that conditioning on adjoint data is unbiased for a certain class of inverse problems. We then demonstrate with two medical imaging examples (PAI and CT) that adjoints enable two things: Firstly, adjoints partially undo the physics of the forward operator resulting in faster convergence of a learned Bayesian UQ technique. Secondly, the algorithm is now robust to changes in the observed data caused by different transducer subsampling in PAI and number of angles in CT. Our adjoint-based Bayesian inference method results in point estimates that are faster to compute than traditional baselines and show higher SSIM metrics, while also providing validated UQ.

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“…The strong material contrast between the skull and the surrounding soft tissues lead to both significant reflections and reverberations off of the skull boundaries. 1,2 Image reconstruction techniques such as full-waveform inversion (FWI) have been explored in recent years within the context of transcranial ultrasound [3][4][5] given the success with which FWI has been applied to imaging soft tissue systems. 6,7 However, a significant hindrance with applying many of the proposed transcranial FWI approaches in practice is that the geometry of the skull must be known a priori.…”

Section: Introductionmentioning

confidence: 99%

Shape optimization for transcranial ultrasound computed tomography

Marty

Boehm

Fichtner

2023

Medical Imaging 2023: Ultrasonic Imaging and Tomography

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Using waveform-based inversion methods within transcranial ultrasound computed tomography is an attractive emerging reconstruction technique for imaging the human brain. However, such imaging approaches generally rely on possessing an accurate model of the skull in order to account for the complex interactions which occur when the ultrasound waves propagate between soft tissue and bone. In order to recover the shape of the skull within the context of full-waveform inversion, adjoint-based shape optimization is performed within this study. The gradients with respect to the acoustic properties of the tissues which are used in conventional full-waveform inversion act as a proxy for estimating the sensitivities to the shape of the skull. These shape derivatives can be utilized to update the interface between the interior brain tissue and the skull. This technique employs the spectral-element method for solving the wave equation and, thus, allows for the use of a convenient framework for representing the skull interfaces throughout the inversion. Adaptations of the Shepp-Logan phantom are used as a proof of concept to demonstrate this inversion strategy where both the shape of the skull as well as the interior brain tissue are imaged sequentially.

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Design and Construction of a Low-Frequency Ultrasound Acquisition Device for 2-D Brain Imaging Using Full-Waveform Inversion

Cited by 11 publications

References 38 publications

open-UST: An Open-Source Ultrasound Tomography Transducer Array System

open-UST: An Open-Source Ultrasound Tomography Transducer Array System

Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification

Shape optimization for transcranial ultrasound computed tomography

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