Ultrasound Planar Array Imaging Metric Analysis

Lou, Cuijuan; Xiao, Feng; Song, Junjie; Ding, Mingyue; Yuchi, Ming

doi:10.1109/tuffc.2021.3065103

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…Mainly, optimization-based sparse array design strategies employ fitness functions (FF) based on the analysis of beampattern characteristics, such as mainlobe width and sidelobe level. Due to the high computational cost associated with the beampattern simulation and the complexity of its analysis [ 14 , 15 ], the use of these FFs turn the bidimensional sparse array design into a complex process [ 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Design of 2D Planar Sparse Binned Arrays Based on the Coarray Analysis

Martínez-Graullera

Souza

Parrilla

et al. 2021

Sensors

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The analysis of the beampattern is the base of sparse arrays design process. However, in the case of bidimensional arrays, this analysis has a high computational cost, turning the design process into a long and complex task. If the imaging system development is considered a holistic process, the aperture is a sampling grid that must be considered in the spatial domain through the coarray structure. Here, we propose to guide the aperture design process using statistical parameters of the distribution of the weights in the coarray. We have studied three designs of sparse matrix binned arrays with different sparseness degrees. Our results prove that there is a relationship between these parameters and the beampattern, which is valuable and improves the array design process. The proposed methodology reduces the computational cost up to 58 times with respect to the conventional fitness function based on the beampattern analysis.

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