Online evolution of a phased array for ultrasonic imaging by a novel adaptive data acquisition method

Abstract

Ultrasonic imaging, using ultrasonic phased arrays, has an enormous impact in science, medicine and society and is a widely used modality in many application fields. The maximum amount of information which can be captured by an array is provided by the data acquisition method capturing the complete data set of signals from all possible combinations of ultrasonic generation and detection elements of a dense array. However, capturing this complete data set requires long data acquisition time, large number of array elements and transmit channels and produces a large volume of data. All these reasons make such data acquisition unfeasible due to the existing phased array technology or non-applicable to cases requiring fast measurement time. This paper introduces the concept of an adaptive data acquisition process, the Selective Matrix Capture (SMC), which can adapt, dynamically, to specific imaging requirements for efficient ultrasonic imaging. SMC is realised experimentally using Laser Induced Phased Arrays (LIPAs), that use lasers to generate and detect ultrasound. The flexibility and reconfigurability of LIPAs enable the evolution of the array configuration, on-the-fly. The SMC methodology consists of two stages: a stage for detecting and localising regions of interest, by means of iteratively synthesising a sparse array, and a second stage for array optimisation to the region of interest. The delay-and-sum is used as the imaging algorithm and the experimental results are compared to images produced using the complete generation-detection data set. It is shown that SMC, without a priori knowledge of the test sample, is able to achieve comparable results, while preforming $\sim$ 10 times faster data acquisition and achieving $\sim$ 10 times reduction in data size.

Figure 1

(A,C,E) Graphical demonstration of the amount of information contained in each signal, with respect to the location of the ROI for three array directivities: (A) Transducer array; (C) LIPA; (E) EMAT array. Red circle depicts ROI. Green and grey circles depict array elements with high or low directivity towards the ROI respectively. (B,D,F) directivity patterns for array elements from: (B) a transducer array; (D) a LIPA; (F) an EMAT array. Green areas highlight angles where directvity is higher than half relative to its maximum.

Figure 2

Matrix showing combined sensitivity of each generation and detection element combination for a point scatterer located at the centre of an array at a depth of 15 mm. The array consists of 90 elements and has an aperture of 30 mm.

Figure 3

Ultrasonic delay-and-sum images, using experimental data from an array with 161 element and 0.155 mm pitch, of (A) a scatterer free region and (B,C) a region with a scatterer. White and black arrows show the location of the scatterer. Normalisation was performed on (A,C) the highest intensity noise pixel and (B) highest intensity scatterer pixel. (D) Graph shows the pixel intensity distribution of each ultrasonic image. Additional (E) graph highlights the 15 dB shift observed between the pixel intensity distributions with and without a scatterer.

Figure 4

(A) Surface projected directivity (red curve) and sensitivity (green curve) patterns calculated for a scatterer located at 15 mm deep at 0 mm from the centre of the scan area. (B,C) Corresponding arrays produced using (B) SPT-SMC and (C) SED-SMC for the above shown patterns. Red circles and green dots are the generation and detection element positions, respectively. Figures are plotted for one half of the scan area (-15 to 0 mm) due to symmetry.

Figure 5

Diagram of (A) experimental setup and (B) experimental, aluminium sample. Cases 1 and 2 with their respective scan area of 30 mm and 22mm are indicated on the sample surface.

Figure 6

Array sensitivities for when the scatterer shown by a red circle is (A) inside and (B) outside the high sensitivity region between 0 and − 6 dB (red dashed region).

Figure 7

Experimental results from stage 1 of SMC for (A–C) Case 1 and (D–F) Case 2. In each case three iterations were performed: (A,D) iteration 1, (B,E) iteration 2 and (C,F) iteration 3. All images were plotted against the grey-scale dynamic range shown on the right. Red arrows indicate the location of the detect.

Figure 8

Pixel intensity distribution of images produced by the iterations of stage 1 shown for (A) Case 1 and (B) Case 2.

Figure 9

Ultrasonic delay-and-sum images produced for (A–D) Case 1 and (E–H) Case 2 by array configurations utilising: (A,E) FMC, (B,F) Sub-sampled FMC, (C,G) SPT-SMC and (D,H) SED-SMC. The dynamic range used in all images is indicated on the right.

Figure 10

Close-up ultrasonic delay-and-sum images of the scatterer, produced for (A–D) Case 1 and (E,F) Case 2 by array configurations utilising: (A,E) FMC, (B,F) Sub-sampled FMC, (C,G) SPT-SMC and (D,H) SED-SMC.The dynamic range used in all images is indicated on the right.