Blind calibration for arrays with an aberration layer in ultrasound imaging. van der Meulen , P., Coutino, M., Kruizinga, P., Bosch, J. G., & Leus, G. In *2020 28th European Signal Processing Conference (EUSIPCO)*, pages 1269-1273, Aug, 2020.

Paper doi abstract bibtex

Paper doi abstract bibtex

We consider the scenario of finding the transfer function of an aberrating layer in front of an ultrasound array. We are interested in blindly estimating this transfer function without prior knowledge of the unknown ultrasound sources or ultrasound contrast image. The algorithm gives an exact solution if the matrix representing the aberration layer's transfer function is full rank, up to a scaling and reordering of its columns, which has to be resolved using some prior knowledge of the matrix structure. We provide conditions for the robustness of blind calibration in noise. Numerical simulations show that the method becomes more robust for shorter wavelengths, as the transfer function matrices then tend to be less ill-conditioned. Image reconstruction from simulated data using the k-Wave toolbox show that a well calibrated model removes some of the distortions introduced by an uncalibrated model, and improves the resolution for some of the sources.

@InProceedings{9287755, author = {P. {van der Meulen} and M. Coutino and P. Kruizinga and J. G. Bosch and G. Leus}, booktitle = {2020 28th European Signal Processing Conference (EUSIPCO)}, title = {Blind calibration for arrays with an aberration layer in ultrasound imaging}, year = {2020}, pages = {1269-1273}, abstract = {We consider the scenario of finding the transfer function of an aberrating layer in front of an ultrasound array. We are interested in blindly estimating this transfer function without prior knowledge of the unknown ultrasound sources or ultrasound contrast image. The algorithm gives an exact solution if the matrix representing the aberration layer's transfer function is full rank, up to a scaling and reordering of its columns, which has to be resolved using some prior knowledge of the matrix structure. We provide conditions for the robustness of blind calibration in noise. Numerical simulations show that the method becomes more robust for shorter wavelengths, as the transfer function matrices then tend to be less ill-conditioned. Image reconstruction from simulated data using the k-Wave toolbox show that a well calibrated model removes some of the distortions introduced by an uncalibrated model, and improves the resolution for some of the sources.}, keywords = {aberrations;biomedical ultrasonics;calibration;image reconstruction;matrix algebra;transfer function matrices;aberration layer;matrix structure;blind calibration;transfer function matrices;image reconstruction;calibrated model;ultrasound imaging;aberrating layer;ultrasound array;ultrasound sources;ultrasound contrast image;numerical simulation;k-Wave toolbox;Ultrasonic imaging;Image resolution;Transfer functions;Imaging;Distortion;Calibration;Numerical models}, doi = {10.23919/Eusipco47968.2020.9287755}, issn = {2076-1465}, month = {Aug}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2020/pdfs/0001269.pdf}, }

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