Multi-modality or multi-physics imaging is gaining interest beacuase it overcomes the limitations of a single imaging modality, as each modality typically suffers from its own application specific limitations. Different imaging techniques are developed to combine the outcome of both modalities; varying from image fusion up to the usage of prior information obtained from one imaging modality and used as input for the other one. In this work an alternative approach is presented. The method employs a multi-physics Born inversion algorithm where structural similarity is used as regularization parameter to align the acoustic and electromagnetic contrast interfaces with each other. To align the interfaces the gradients of the acoustic and electromagnetic contrast functions are used. Two approaches are tested successfully on a synthetic profile; one where the cross-product of the two gradients and one where the gradient differences are considered. Both approaches work but the gradient-difference approach outperforms the cross-gradient one. Overall, it is shown that multi-physics Born inversion both approaches reveals details in the electromagnetic contrast function that would have been missed by only employing electromagnetic inversion. This improvement is obtained at the cost of an increase in computational complexity compared to single modality inversion.

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With full waveform inversion (FWI) all available information enclosed in the recorded wavefield - including multiple scattering, dispersion, and diffraction - is used to obtain accurate images showing quantitative information of the tissue parameters. These non-linear inversion methods are implemented either in the time or in the frequency domain. Unfortunately, selecting which implementation should be used for a specific problem is not trivial. To ease the selection process, we compare the performance of one time-domain inversion (TDI) and one frequency-domain inversion (FDI) - also known as Contrast Source Inversion - to provide insights into the strengths and weaknesses of each FWI method. In this contribution, we investigate the effect of the (i) bandwidth, (ii) problem complexity, (iii) number of sources and receivers, and (iv) initial speed-of-sound model on the performance of each FWI method by comparing the resulting reconstructions. Both methods are tested for the same configuration: a 2-D tomographic scan of a cancerous breast model. To avoid an inverse crime, TDI is tested on synthetic data obtained using a frequency-domain forward solver and CSI on data from a time-domain forward solver.

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Ultrasound imaging is used for detecting and characterizing breast lesions. A state of the art imaging method is the contrast source inversion (CSI), which solves the full wave nonlinear inverse problem. However, when the measurements are acquired in noisy environments, CSI can diverge from the correct solution after several iterations. Problems associated with noisy data were originally solved by including total variation (TV) regularization. Unfortunately, for very noisy data, TV regularization alone is not sufficient. In this work, compressed sensing ideas are used to regularize the inversion process by restricting the solution of the CSI method to be sparse in a transformation domain. The proposed method estimates the contrast source and contrast function by minimizing the mean squared error between the measured and modeled data. An extra penalty term is added to measure sparsity in the transformation domain. A second method that combines sparsity of the contrast source and minimal TV in the contrast function is also presented. The proposed methods are tested on noise-free and noisy synthetic data sets representing a scan of a cancerous breast. Numerical experiments show that, for measurements contaminated with 1% noise, the sparsity constrained CSI improves the normalized mean squared error of the reconstructed speed-of-sound profiles up to 36% in comparison with traditional CSI. Also, for measurements contaminated with 5% noise, the proposed methods improve the quality of the reconstruction up to 70% in comparison with the traditional CSI method. Experimental results also show that the methods remain convergent to the correct speed-of-sound profile as the number of iterations increases.

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The design of ultrasound scanning systems for applications such as breast cancer detection is a challenging task, especially when the number of sources and receivers increases and they become spread over a large surface. In order to determine the number of transducers and above all their position, several approaches could be followed. A simple and straightforward approach is to compute the energy distribution in the region of interest for several configurations, and treat each receiver as if it is a source. Here the underlying assumption is that the number of A-scans, i.e. number of sources times receivers, determines the image quality. This assumption is mainly based on reciprocity; the observation that the response RAB measured by a receiver located at B and a source at A is identical to the response RBA obtained after interchanging the source and receiver locations. This is valid for linear imaging methods. However, the question arises whether this is also the case for non-linear inversion methods. In this work, we evaluate the SAFT, CG and CSI imaging and inversion methods for different configurations of sources and receivers located uniformly distributed over a circular array, surrounding an heterogeneous medium. The obtained results show that with the linear SAFT and CG methods, the sources and receivers can be interchanged, and the resulting images are the same. However, for the non-linear CSI method, the results show that it is preferred to have more receivers than sources, despite the fact the measured signals are reciprocal.@en