Compressive Sensing Strategy on Sparse Array to Accelerate Ultrasonic TFM Imaging

• Published in: IEEE transactions on ultrasonics, ferroelectrics, and frequency control ferroelectrics, and frequency control, 2023-06, Vol.70 (6), p.1-1
• Main Authors: Piedade, Lucas Pereira, Painchaud-April, Guillaume, Le Duff, Alain, Belanger, Pierre
• Format: Article
• Language: English
• Subjects: Acoustics; Arrays; Compressive sensing; Data recovery; Defects; discrete wavelet transform (DWT); full matrix capture (FMC); Image degradation; Image quality; Image reconstruction; Image resolution; L1-norm minimization; Nondestructive testing; Phased arrays; Probes; Sparse matrices; total focusing method (TFM); Transceivers; Transforms; Ultrasonic imaging; Ultrasonic testing; ultrasound
• ISSN: 0885-3010; 1525-8955
• DOI: 10.1109/TUFFC.2023.3266719

Phased array ultrasonic testing (PAUT) based on full matrix capture (FMC) has recently been gaining in popularity in the scientific and nondestructive testing communities. FMC is a versatile acquisition method that collects all the transmitter-receiver combinations from a given array. Furthermore, when post-processing FMC data using the total focusing method (TFM), high-resolution images are achieved for defect characterization. Today, the combination of FMC and TFM are becoming more widely available in commercial ultrasonic phased array controllers. However, executing the FMC-TFM method is data-intensive, as the amount of data collected and processed is proportional to the square of the number of elements of the probe. This shortcoming may be overcome using a sparsely populated array in transmission followed by an efficient compression using compressive sensing (CS) approaches. The method can therefore lead to a massive reduction of data and hardware requirements and ultimately accelerate TFM imaging. In the present work, a CS methodology was applied to experimental data measured from samples containing artificial flaws. The results demonstrated that the proposed CS method allowed a reduction of up to 80% in the volume of data while achieving adequate FMC data recovery. Such results indicate the possibility of recovering experimental FMC signals using sampling rates under the Nyquist theorem limit. The TFM images obtained from the FMC, CS-FMC, and sparse CS approaches were compared in terms of contrast-to-noise ratio (CNR). It was seen that the CS-FMC combination produced images comparable to those acquitted using the FMC. Implementation of sparse arrays improved CS reconstruction times by up to 11 folds and reduced the firing events by approximately 90%. Moreover, image formation was accelerated by 6.6 times at the cost of only a minor image quality degradation relative to the FMC.