Spatial Angular Compounding ofPhotoacoustic Images

Abstract

Photoacoustic (PA) images utilize pulsed lasers and ultrasound transducers to visualize targets with higher optical absorption than the surrounding medium. However, they are susceptible to acoustic clutter and background noise artifacts that obfuscate biomedical structures of interest. We investigated three spatial-angular compounding methods to improve PA image quality for biomedical applications, implemented by combining multiple images acquired as an ultrasound probe was rotated about the elevational axis with the laser beam and target fixed. Compounding with conventional averaging was based on the pose information of each PA image, while compounding with weighted and selective averaging utilized both the pose and image content information. Weighted-average compounding enhanced PA images with the least distortion of signal size, particularly when there were large (i.e. 2.5 mm and 7°) perturbations from the initial probe position. Selective-average compounding offered the best improvement in image quality with up 181, 1665, and 1568 times higher contrast, CNR, and SNR, respectively, compared to the mean values of individual PA images. The three presented spatial compounding methods have promising potential to enhance image quality in multiple photoacoustic applications.

Tomographic imaging with an ultrasound and LED-based photoacoustic system

“…[10,23] developed a multi-view Hilbert transform for the circular scanning configuration using a linear array and showed resolution improvement with this approach. Imaging quality improvement based on resolution, signal to noise ratio (SNR) and contrast was also studied for linear array-based tomographic imaging [24,25]. The above-mentioned advantages are associated with tomographic imaging with the long axis of the transducer array rotating around the target, where all the angular views are imaging the same plane and the focusing of the transducer is advantageous to eliminate out-of-plane artifacts.…”

Opto-Acoustic Image Reconstruction and Motion Tracking Using Convex Optimization

“…Grüneisen parameter y(λ, r, t) opto-acoustic signal amplitude h(r, t) statio-temporal impulse response g (r, t) ideal impulse response fa(r) transducer aperture function α(r) obliquity factor β(t) electro-mechanical impulse response δ(t) Dirac delta function c 0 speed of sound t elapsed time r ∈ R 3 spatial position λ optical wavelength λ j index for optical wavelength of j-th frame m λ j ∈ R nµ optical absorption vector at wavelength λ j y j (r l , t) time-domain signal of transducer l in frame j ψ j (r) initial excess pressure of frame j τ j (r) affine transform function for frame j rigid deformation with internal tissue motion [29], [30], but this is different from solving probe motion and requires many additional variables. In other work, sensors have been used to determine OA probe motion [31]. In ultrasound, motion tracking has been well studied [32]- [40], but previous sensorfree approaches involving ultrasonic speckle correlation are not fully applicable to OA.…”

Multiview spatial compounding using lens-based photoacoustic imaging system

“…Spatial compounding involves acquiring subimages from different angles and combining them to form the final image. The final image possesses higher image quality compared to the single view sub-image [20] , [21] . As noted earlier, the PSF has an asymmetry with respect to the lens axis.…”