Investigation of Techniques to increase the Field of View of a Staring Transducer Array for Photoacoustic Imaging

Photoacoustic imaging (PAI) is a hybrid imaging modality that takes advantage of both optical and acoustic techniques for biomedical imaging. It is believed that PAI can successfully assess the margins of lumpectomy specimens in the operating room, decreasing the number of surgeries and wait time for patients. However, current PAI systems do not have sufficient field of view (FOV) to accommodate the size of lumpectomy specimens. In this work, transducer directionality and the use of a shaped matching layer were explored as means to increase the FOV of a staring photoacoustic transducer array. The results indicated that applying a convex matching layer to the face of transducers and directing them toward the centre of the array provides optimal sensitivity throughout the imaging volume. By employing these techniques, any PAI system’s effective FOV can be increased without replacing existing transducers. The optimized system can now be investigated for lumpectomy margin assessment

Objective Assessment and Design Improvement of a Staring, Sparse Transducer Array by the Spatial Crosstalk Matrix for 3D Photoacoustic Tomography.

Accurate reconstruction of 3D photoacoustic (PA) images requires detection of photoacoustic signals from many angles. Several groups have adopted staring ultrasound arrays, but assessment of array performance has been limited. We previously reported on a method to calibrate a 3D PA tomography (PAT) staring array system and analyze system performance using singular value decomposition (SVD). The developed SVD metric, however, was impractical for large system matrices, which are typical of 3D PAT problems. The present study consisted of two main objectives. The first objective aimed to introduce the crosstalk matrix concept to the field of PAT for system design. Figures-of-merit utilized in this study were root mean square error, peak signal-to-noise ratio, mean absolute error, and a three dimensional structural similarity index, which were derived between the normalized spatial crosstalk matrix and the identity matrix. The applicability of this approach for 3D PAT was validated by observing the response of the figures-of-merit in relation to well-understood PAT sampling characteristics (i.e. spatial and temporal sampling rate). The second objective aimed to utilize the figures-of-merit to characterize and improve the performance of a near-spherical staring array design. Transducer arrangement, array radius, and array angular coverage were the design parameters examined. We observed that the performance of a 129-element staring transducer array for 3D PAT could be improved by selection of optimal values of the design parameters. The results suggested that this formulation could be used to objectively characterize 3D PAT system performance and would enable the development of efficient strategies for system design optimization

Staring transducer array design for 3D PAT.

<p>(a) A 3D CAD rendering of the plastic shell used to hold the transducers. (b) A 3D sketch of object space in relation to plastic shell. (c) Photograph of a custom-built transducer (left) and front face of the transducer (right). The red scale bar represents 1 cm. (d) Example of a photoacoustic pressure signal from a photoacoustic point source (~100 μm) averaged over 5 triggers acquired with one transducer at 40 MHz sampling rate. The amplitude represents the counts on the digital converter and ranges from ±2048 counts.</p

Effects of angular coverage on system performance.

<p>Metric maps for (a) RMSE, (b) PSNR, (c) MAE, and (d) 3D-SSIM displayed as a function of array angular coverage (top to bottom corresponds to 0° to 60°). (e)-(h) System performance figures of merit (RMSE, PSNR, MAE, and 3D-SSIM reading clockwise starting from top left panel) plotted as a function of cube contours with varying array angular coverage (legend shown in panel (e)).</p

System sensitivity and aliasing as a function of array angular coverage.

<p>(a) Point cloud representations of transducer arrays as a function of array angular coverage (top to bottom corresponds to 0° to 60°). Side views are shown in the right-hand column. (b) Normalized sensitivity maps (scaled to 30% max for display purposes) for each array coverage angle (top to bottom row corresponds to 0° to 60°). (c) Independently normalized aliasing maps (scaled to 10% max for display purposes) for the center voxel and left edge voxel (d) for each array coverage angle. Image planes correspond with those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124759#pone.0124759.g005" target="_blank">Fig 5</a>.</p

System sensitivity and aliasing for the <i>experimental transducer arrangement</i> and <i>uniform sampling arrangement</i>.

<p>(a) Transducer response profile to a PA point source (~100 μm) averaged over 64000 triggers acquired with one transducer at a 40 MHz sampling rate. The amplitude represents the counts on the digital-to-analog converter and has been normalized to the maximum sensitivity. (b) Point cloud representations of the <i>experimental transducer arrangement</i> (top) and <i>uniform sampling arrangement</i> (bottom). Darker shaded area represents exterior of shell closest to reader. Lighter shaded area represents interior surface of the shell. (c) Same as (b) from a side-view. (d) Normalized sensitivity maps for the two arrangements and scaled to 30% max sensitivity for display purposes. Aliasing maps for the center voxel (e) and a voxel along the left edge (f) shown for the two arrangements (<i>experimental transducer arrangement</i> in the top row and <i>uniform sampling arrangement</i> in the bottom row). Each arrangement was independently normalized and scaled to 10% max for display purposes. The image planes are 2 x 2 cm² and correspond to every other xy-plane of object space (left to right corresponds to bottom (z = -2 cm) to top (z = 1.8 cm) planes at 2 mm step size).</p

Figures-of-merit for system performance.

<p>Figures-of-merit (clockwise from top left: RMSE, PSNR, MAE, and 3D-SSIM) averaged over object space and plotted as a function of transducer count and sampling rate (legend shown in panel (a)).</p