Methods for Forward and Inverse Scattering in Ultrasound Tomography

Lavarello, Roberto; Hesford, Andrew J.

doi:10.1007/978-94-007-6952-6_14

Cited by 14 publications

(7 citation statements)

References 159 publications

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“…One widely used member in the family of 1 regularized optimizations is total variation regularization (TV). 25 The algorithm penalizes the 1 norm of the spatial gradient of m in the optimization problem (3). For an equidistantly spaced grid with ∆ x = ∆ y , we have ∇m = Dm, where D is the finite differencing matrix thus Dm has entries […”

Section: Total Variation Regularizationmentioning

confidence: 99%

“…Linearizing the problem with the straight ray approximation is a simple choice and, although efficient, it simplifies the propagation of acoustic waves in an inhomogeneous medium significantly. 3 This leads to imprecisions in the forward modelled time of flights that propagate into the inverse problem and possibly results in artefacts in the reconstruction. These shortcomings and the ever-increasing computational resources have motivated the use of more complex forward models that consider the non-linearity of acoustic wave propagation in varying complexity [4][5][6][7][8][9][10][11][12] and provide quantitative images.…”

Section: Introductionmentioning

confidence: 99%

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Sparsifying regularizations for stochastic sample average minimization in ultrasound computed tomography

Ulrich

Zunino

Boehm

et al. 2021

Medical Imaging 2021: Ultrasonic Imaging and Tomography

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Ultrasound computed tomography (USCT) is a promising imaging modality for breast cancer screening. Three challenges commonly arising in time-of-flight USCT are (1) to choose a physical forward model that describes acoustic wave propagation in an inhomogeneous medium appropriately (2) to effectively mitigate the ill-posedness for an adequate reconstruction of the model and (3) to efficiently deal with large data sets. In this contribution, we investigate methods that address these three challenges by developing an optimization strategy based on a stochastic descent method that adaptively subsamples the data and analyze its performance in combination with different sparsity-enforcing regularization techniques.

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Section: Total Variation Regularizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparsifying regularizations for stochastic sample average minimization in ultrasound computed tomography

Ulrich

Zunino

Boehm

et al. 2021

Medical Imaging 2021: Ultrasonic Imaging and Tomography

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“…∂ t p obs (x r , t + ∆T )G(x, t ; x r , t)dt (11) propagating backward in time from the receivers x r . Equation (9) establishes the relationship between crosscorrelation traveltime shifts and velocity perturbations in terms of the finite-frequency sensitivity kernels K(x) [24], [26], [27].…”

Section: Finite-frequency Traveltime Tomographymentioning

confidence: 99%

“…To improve the spatial resolution of the reconstructions, significant efforts have been made to introduce imaging techniques that rely on more accurate descriptions of wave propagation [11], [12]. This has led researchers to recognize similarities with seismology, and as a result, seismic waveform tomography has been transferred to USCT [13]- [16].…”

Section: Introductionmentioning

confidence: 99%

3-D Wave-Equation-Based Finite-Frequency Tomography for Ultrasound Computed Tomography

Martiartu

Boehm

Fichtner

2020

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Ultrasound Computed Tomography (USCT) has great potential for 3D quantitative imaging of acoustic breast tissue properties. Typical devices include high-frequency transducers, which makes tomography techniques based on numerical wave propagation simulations computationally challenging, especially in 3D. Therefore, despite the finite-frequency nature of ultrasonic waves, ray-theoretical approaches to transmission tomography are still widely used.This work introduces finite-frequency traveltime tomography to medical ultrasound. In addition to being computationally tractable for 3D imaging at high frequencies, the method has two main advantages: (1) It correctly accounts for the frequency dependence and volumetric sensitivity of traveltime measurements, which are related to off-ray-path scattering and diffraction. (2) It naturally enables out-of-plane imaging and the construction of 3D images from 2D slice-by-slice acquisition systems.Our method rests on the availability of calibration data in water, used to linearize the forward problem and to provide analytical expressions of cross-correlation traveltime sensitivity. As a consequence of the finite frequency content, sensitivity is distributed in multiple Fresnel volumes, thereby providing out-ofplane sensitivity. To improve computational efficiency, we develop a memory-efficient implementation by encoding the Jacobian operator with a 1D parameterization, which allows us to extend the method to large-scale domains. We validate our tomographic approach using lab measurements collected with a 2D setup of transducers and using a cylindrically symmetric phantom. We then demonstrate its applicability for 3D reconstructions by simulating a slice-by-slice acquisition systems using the same dataset.

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“…While there are many possible image reconstruction methods for both PACT and USCT, this paper will focus on use of optimization-based image reconstruction methods [36, 37]. These methods provide a flexible framework for image reconstruction that can accurately model the underlying physics of the imaging system.…”

Section: Introductionmentioning

confidence: 99%

Joint reconstruction of the initial pressure and speed of sound distributions from combined photoacoustic and ultrasound tomography measurements

Matthews

Anastasio

2017

Inverse Problems

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The initial pressure and speed of sound (SOS) distributions cannot both be stably recovered from photoacoustic computed tomography (PACT) measurements alone. Adjunct ultrasound computed tomography (USCT) measurements can be employed to estimate the SOS distribution. Under the conventional image reconstruction approach for combined PACT/USCT systems, the SOS is estimated from the USCT measurements alone and the initial pressure is estimated from the PACT measurements by use of the previously estimated SOS. This approach ignores the acoustic information in the PACT measurements and may require many USCT measurements to accurately reconstruct the SOS. In this work, a joint reconstruction method where the SOS and initial pressure distributions are simultaneously estimated from combined PACT/USCT measurements is proposed. This approach allows accurate estimation of both the initial pressure distribution and the SOS distribution while requiring few USCT measurements.

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Methods for Forward and Inverse Scattering in Ultrasound Tomography

Cited by 14 publications

References 159 publications

Sparsifying regularizations for stochastic sample average minimization in ultrasound computed tomography

Sparsifying regularizations for stochastic sample average minimization in ultrasound computed tomography

3-D Wave-Equation-Based Finite-Frequency Tomography for Ultrasound Computed Tomography

Joint reconstruction of the initial pressure and speed of sound distributions from combined photoacoustic and ultrasound tomography measurements

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