Cramér-Rao Bounds for Flaw Localization in Subsampled Multistatic Multichannel Ultrasound Ndt Data

Perez, Eduardo; Kirchhof, Jan N.; Semper, Sebastian; Krieg, Fabian; Römer, Florian

doi:10.1109/icassp40776.2020.9053523

Cited by 3 publications

(17 citation statements)

References 17 publications

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“…Additionally, the same set of Rx elements is active during each measurement cycle. An FMC data set subsampled in this fashion is given by [13,23]…”

Section: Subsampled Datamentioning

confidence: 99%

“…Now that the model and reconstruction procedures have been established, the choice of Φ = S s S f remains. The approach explored in this work is the same as in [23], where the design procedure is defined as a minmax problem of the form…”

Section: Sparse Array Designmentioning

confidence: 99%

“…Motivated by the success of compressed sensing in ultrasound imaging and the ubiquity of sparse array design and optimal sensor positioning problems, the present study expands upon the results in [13,23]. By enforcing specific conditions on the transmitting and receiving elements in a Uniform Linear Array (ULA), structured compression matrices are designed based on the CRB of a single scatterer whose location is estimated from a subsampled FMC measurement.…”

Section: Introductionmentioning

confidence: 97%

“…In [ 22 ], the CRB and Monte Carlo simulations were employed to study the resolution limit of ultrasound localization microscopy, showing a direct relationship to the aperture size and therefore the spacing between the active sensors. This behavior was quantified in [ 23 ], where approximations and simplifications allowed the CRB of FMC data containing echoes from a single scatterer to be written in a closed form that clearly portrayed the impact of the sensor positioning on the variance of the estimated scatterer location. This insight was then used to design a channel selection technique with the CRB as a cost function.…”

Section: Introductionmentioning

confidence: 99%

“…Since the presented novel approaches generalize the previous work [ 13 ], they entail a higher computational complexity. Therefore, this work also adapts the compression matrix design approach from [ 23 ] into a two stage greedy algorithm. The resulting imaging quality is then compared to that in [ 13 ] by applying the new compression matrices to the same measurement data.…”

Section: Introductionmentioning

confidence: 99%

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Subsampling Approaches for Compressed Sensing with Ultrasound Arrays in Non-Destructive Testing

Perez

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et al. 2020

Sensors

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Full Matrix Capture is a multi-channel data acquisition method which enables flexible, high resolution imaging using ultrasound arrays. However, the measurement time and data volume are increased considerably. Both of these costs can be circumvented via compressed sensing, which exploits prior knowledge of the underlying model and its sparsity to reduce the amount of data needed to produce a high resolution image. In order to design compression matrices that are physically realizable without sophisticated hardware constraints, structured subsampling patterns are designed and evaluated in this work. The design is based on the analysis of the Cramér–Rao Bound of a single scatterer in a homogeneous, isotropic medium. A numerical comparison of the point spread functions obtained with different compression matrices and the Fast Iterative Shrinkage/Thresholding Algorithm shows that the best performance is achieved when each transmit event can use a different subset of receiving elements and each receiving element uses a different section of the echo signal spectrum. Such a design has the advantage of outperforming other structured patterns to the extent that suboptimal selection matrices provide a good performance and can be efficiently computed with greedy approaches.

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“…Additionally, the same set of Rx elements is active during each measurement cycle. An FMC data set subsampled in this fashion is given by [13,23]…”

Section: Subsampled Datamentioning

confidence: 99%

Section: Sparse Array Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%