Abstract
.Since it was first demonstrated more than a decade ago, the single-pixel camera concept has been used in numerous applications in which it is necessary or advantageous to reduce the channel count, cost, or data volume. Here, three-dimensional (3-D), compressed-sensing photoacoustic tomography (PAT) is demonstrated experimentally using a single-pixel camera. A large area collimated laser beam is reflected from a planar Fabry–Pérot ultrasound sensor onto a digital micromirror device, which patterns the light using a scrambled Hadamard basis before it is collected into a single photodetector. In this way, inner products of the Hadamard patterns and the distribution of thickness changes of the FP sensor—induced by the photoacoustic waves—are recorded. The initial distribution of acoustic pressure giving rise to those photoacoustic waves is recovered directly from the measured signals using an accelerated proximal gradient-type algorithm to solve a model-based minimization with total variation regularization. Using this approach, it is shown that 3-D PAT of imaging phantoms can be obtained with compression rates as low as 10%. Compressed sensing approaches to photoacoustic imaging, such as this, have the potential to reduce the data acquisition time as well as the volume of data it is necessary to acquire, both of which are becoming increasingly important in the drive for faster imaging systems giving higher resolution images with larger fields of view.
Highlights
Photoacoustic tomography (PAT) is a hybrid imaging technique based on the use of laser-generated ultrasound within soft tissue that has been demonstrated in a wide variety of applications in preclinical research and clinical medicine.[1,2]
When a short pulse of near infrared (NIR) light is absorbed by chromophores within soft tissue, it gives rise to a pressure increase that propagates through the tissue as an ultrasound pulse and can be detected at the surface
The images were obtained using 100%, 50%, 20%, and 10% of the scrambled Hadamard patterns
Summary
Photoacoustic tomography (PAT) is a hybrid imaging technique based on the use of laser-generated ultrasound within soft tissue that has been demonstrated in a wide variety of applications in preclinical research and clinical medicine.[1,2] When a short pulse of near infrared (NIR) light is absorbed by chromophores within soft tissue, it gives rise to a pressure increase that propagates through the tissue as an ultrasound pulse and can be detected at the surface. This paper is concerned with measurements made with a 2-D planar sensor interrogated with patterns.[6] As well as a variety of detection schemes, a number of approaches to reconstruction from sparse data have been proposed for PAT: principal component analysis,[21] sparsifying transforms,[18,30] deep learning,[17,16,25,26,31] and variational approaches that minimize a functional[11,14,15,29,28,32] including joint motion estimation.[24] Here a variational minimization approach will be taken.[29] (For clarity, the term “compressed sensing” has been used in the photoacoustic imaging literature to refer to 2-D photoacoustic imaging using patterned excitation light.[33,34,35] This is difficult to extend to 3-D imaging and is quite a different idea from the patterned acoustic sensing described here.)
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