Computation of Photoacoustic Absorber Size from Deconvolved Photoacoustic Signal Using Estimated System Impulse Response

Abstract

Photoacoustic signal recorded by photoacoustic imaging system can be modeled as convolution of initial photoacoustic response by the photoacoustic absorber with the system impulse response. Our goal was to compute the size of photoacoustic absorber using the initial photoacoustic response, deconvolved from the recorded photoacoustic data. For deconvolution, we proposed to use the impulse response of the photoacoustic system, estimated using discrete wavelet transform based homomorphic filtering. The proposed method was implemented on experimentally acquired photoacoustic data generated by different phantoms and also verified by a simulation study involving photoacoustic targets, identical to the phantoms in experimental study. The photoacoustic system impulse response, which was estimated using the acquired photoacoustic signal corresponding to a lead pencil, was used to extract initial photoacoustic response corresponding to a mustard seed of 0.65 mm radius. The recovered radius values of the mustard seed, corresponding to the experimental and simulation studies were 0.6 mm and 0.7 mm.

Wideband photoacoustic imaging in vivo with complementary frequency conventional ultrasound transducers

“…The average sensitivity compensation ratio (S) over frequencies within -3 dB level of the spectra was then computed using Eqn. (8). Eqn.…”

“…Transducer specifications are typically based on the target location, the field of view, penetration depth, and resolution requirements. In photoacoustics, the frequency bandwidth of the emitted acoustic pressure depends on the size distribution of the optical absorbers in the field of view: larger absorbers emit primarily lower frequencies, while small objects generate higher frequency acoustic waves [6][7][8][9][10][11]. For example, a target of size 1 mm emits a frequency spectrum with center frequency at ~0.5 MHz while a 60 μm target emits a spectrum with center frequency ~11 MHz [12].…”

Dynamic modeling of photoacoustic sensor data to classify human blood samples

“…Among the available methods, time-domain waveform analysis provides quantitative information about the composition and structure of the tissue under test [ 38 , 39 ]. However, scarce information is reported about robust methods for PA signal processing in the time domain [ 40 , 41 ]. Interestingly, as shown in [ 41 ], a PA signal can be regarded as the impulse response of a linear model.…”

“…However, scarce information is reported about robust methods for PA signal processing in the time domain [ 40 , 41 ]. Interestingly, as shown in [ 41 ], a PA signal can be regarded as the impulse response of a linear model. This approach paves the road for exploiting the dynamics of the PA signals and the flexibility of data-driven methods to discover helpful information in a low-dimensional space [ 42 – 44 ].…”

A hybrid multispectral photoacoustic-ultrasound breast imager: from the lab towards the clinic

“…Spheres are, for example, known to generate N-shaped time traces, with a pulse width that is proportional to the sphere diameter, and with a time of arrival that is proportional to the distance between the source and the detector [289,288]. The amplitude spectrum of such an N-shaped pulse shows an oscillatory behaviour, with peak amplitudes decreasing with frequency and where the peak locations relate to the sphere size [290,291,289,292,293]. Measuring the PA signal only at a single detection point can therefore allow to locate the PA source in space, while also to recover its 3D geometry, without the need for detector arrays, and reconstruction algorithms.…”