Time-domain spectral-element ultrasound waveform tomography using a stochastic quasi-Newton method

Medical Imaging 2020: Ultrasonic Imaging and Tomography

Fichtner

2020

Self Cite

We present a novel approach to obtain time-of-flight measurements between transducer pairs in an Ultrasound computed tomography (USCT) scanner by applying the interferometry principle, which has been used successfully in seismic imaging to recover the subsurface velocity structure from ambient noise recordings. To apply this approach to a USCT aperture, random wavefields are generated by activating the emitting transducers in a random sequence. By correlating the random signals recorded by the receiving transducers, we obtain an approximation of the Green's functions between all receiver pairs, where one is acting as a virtual source. This eliminates specific source imprints, and thus avoids the need for reference measurements and calibration. The retrieved Green's functions between any two measurement locations can then be used as new data to invert the sound speed map. On the basis of the cross-correlation travel times a ray-based time-of-flight tomography is developed and solved with an iterative least-squares method. As a proof of concept, the algorithm is tested on numerical breast phantoms in a synthetic 2D study.

Section: Discussionmentioning

confidence: 99%

Random field interferometry for medical ultrasound

Ulrich

Medical Imaging 2020: Ultrasonic Imaging and Tomography

Fichtner

2020

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“…To reduce the computational cost for large-scale datasets, we can adaptively operate on partial data and restrict the forward operator to a subset of emitting and/or receiving transducers. 19,28 This results in the sample average approximation of the misfit, and a dimension reduction of F and d obs , namely…”

Section: Stochastic Split Bregman Methods With Dynamic Batchesmentioning

confidence: 99%

Sparsifying regularizations for stochastic sample average minimization in ultrasound computed tomography

Ulrich

Zunino

Medical Imaging 2021: Ultrasonic Imaging and Tomography

et al. 2021

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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.

“…The combination of (9) and (15), or (16) for 2D approximations, describes the tomographic method presented in this study. Using a compact notation, the linearized forward problem is written as…”

Section: New Parameterization Of the Forward Operatormentioning

confidence: 99%

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

Martiartu

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

Fichtner

2020

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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.