Total Focusing Method with Subsampling in Space and Frequency Domain for Ultrasound NDT

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

doi:10.1109/ultsym.2019.8926040

Cited by 7 publications

(19 citation statements)

References 9 publications

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“…In particular, each active transmitter is only required to have the same number of associated receivers, while each transmitter-receiver pair must have the same number of frequency samples; however, the actual elements and frequency samples can be chosen freely. 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.…”

Section: Introductionmentioning

confidence: 79%

“…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%

“…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: 96%

“…Reconstructions from such subsampled 3D array data are studied in [ 11 , 12 ], where it is shown that complete data scans can be omitted with only a small increase in the error. Indeed, it was shown in [ 13 ] that high quality reconstructions can be obtained even when the FMC data has been heavily subsampled both spatially (i.e., by omitting the data from several sensors) and in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

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

Perez

Kirchhof

Krieg

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.

show abstract

Section: Introductionmentioning

confidence: 79%

“…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: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Perez

Kirchhof

Krieg

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The reconstruction of this data with the total focusing method (TFM) is shown in the middle plot of Figure 4. We then applied compression of the data in time domain (factor 7.4) as well as the spatial domain (factor 12.8) leading to a total compression factor of 94.8 (see [43,44] for details). The parametric reconstruction of the compressed data using the Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) is shown on the righthand side of Figure 4.…”

Section: From Big Data To Relevant Data: the Power Of Sparse Signal Pmentioning

confidence: 99%