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Acoustic-resolution photoacoustic micro- scopy (AR-PAM) system can provide 3-D images of facial tissues. The lateral resolution of AR-PAM depends on the numerical aperture (NA) of the acoustic lens and the central frequency of the ultrasonic transducer. There is a trade-off between resolution enhancement and imaging depth. The acoustic beam is tight in the acoustic focal plane but expands in the out-of-focus regions, deteriorating the resolution. High-NA AR-PAM has depth-variant resolution. Synthetic aperture focusing technique (SAFT) based on a virtual detector (VD) concept can compensate for the beam shape and improve the lateral resolution via beamforming. Although, beamforming can enhance the resolution but the lateral resolution in the focal plane is still limited by acoustic diffraction. Structured-illumination can shift the spatial spectrum of an image to low frequencies hence high-frequency contents can be reserved to overcome the diffraction limit. Conventional structured-illumination via using a three-phase-shifting method can improve the resolution by two folds. Here, a modified phase-shifting method is used to generate the second harmonic of the fringes and double the spectral shift. In this idea, higher frequency information compared to the three-phase shifting method can fall into the band-limited system response. The modified phase-shifting method expands the spatial bandwidth and increases the lateral resolution by five folds. The mathematical relations and the theory are discussed in the context. Tungsten filament result shows resolution improvement from 44.6 $\mu \text{m}$ to 11.3 $\mu \text{m}$ by the modified structured illumination. In vivo and ex vivo experimental results validate the system performance. [ABSTRACT FROM AUTHOR]

Two-way dynamic focusing in synthetic transmit aperture (STA) beamforming can benefit high-quality ultrasound imaging with higher lateral spatial resolution and contrast resolution. However, STA requires the complete dataset for beamforming in a relatively low frame rate and transmit power. This paper proposes a deep-learning architecture to achieve high frame rate STA imaging with two-way dynamic focusing. The network consists of an encoder and a joint decoder. The encoder trains a set of binary weights as the apodizations of the high-frame-rate plane wave transmissions. In this respect, we term our network ApodNet. The decoder can recover the complete dataset from the acquired channel data to achieve dynamic transmit focusing. We evaluate the proposed method by simulations at different levels of noise and in-vivo experiments on the human biceps brachii and common carotid artery. The experimental results demonstrate that ApodNet provides a promising strategy for high frame rate STA imaging, obtaining comparable lateral resolution and contrast resolution with four-times higher frame rate than conventional STA imaging in the in-vivo experiments. Particularly, ApodNet improves contrast resolution of the hypoechoic targets with much shorter computational time when compared with other high-frame-rate methods in both simulations and in-vivo experiments. [ABSTRACT FROM AUTHOR]

A novel super-resolution volumetric photoacoustic microscopy, based on the theory of structured-illumination, is proposed in this paper. The structured-illumination will be introduced in order to surpass the diffraction limit in a photoacoustic microscopy (PAM) structure. Through optical excitation of the targeted object with a sinusoidal spatial fringe pattern, the object’s frequency spectrum is forced to shift in the spatial frequency domain. The shifting in the desired direction leads to the passage of the high-frequency contents of the object through the passband of the acoustic diffraction frequency response. Finally, combining the low-frequency image with the high-frequency parts in four regular orientations in the spatial frequency domain is equivalent to imaging the targeted object with an imaging system of two-fold bandwidth and thus half lateral resolution. In order to obtain the image of out-of-focus regions and improve the lateral resolution outside the focal region of a PAM imaging system, Fourier-domain reconstruction algorithm based on the synthetic aperture focusing technique (SAFT) using the virtual detector concept is utilized for reduction in the required computational load and time. The performance of the proposed imaging system is validated with in vivo and ex vivo targets. The experimental results obtained from several tungsten filaments in the depth range of 1.2 mm, show an improvement of −6 dB lateral resolution from 55–287 μm to 25–29 μm and also an improvement of signal-to-noise ratio (SNR) from 16–22 dB to 27–33 dB in the proposed system. [ABSTRACT FROM AUTHOR]

Photoacoustic endoscopy (PAE), combining both advantages of optical contrast and acoustic resolution, can visualize the chemical-specific optical information of tissues inside human-body. Recently, its corresponding reconstruction methods have been extensively researched. However, most of them are limited on cylindrical scan trajectories, rather than a helical scan which is more clinically practical. On this note, this article proposes a methodology of imaging reconstruction and evaluation for helical scan guided PAE. Different from traditional reconstruction method, synthetic aperture focusing technique (SAFT), our method reconstructs image using wavefield extrapolation which significantly improves computational efficiency and even takes only 0.25 seconds for 3-D reconstructions. In addition, the proposed evaluation methodology can estimate the resolutions and deviations of reconstructed images in advance, and then can be used to optimize the PAE scan parameters. Groups of simulations as well as ex-vivo experiments with different scan parameters are provided to fully demonstrate the performance of the proposed techniques. The quantitatively measured angular resolutions and deviations agree well with our theoretical derivation results ${D}{\sqrt {r_{s}^{2} + \overline {h}^{{2}}} } / {[{1.25}({r}_{s} {r}_{d} + \overline {h}^{{2}})] }$ (rad) and $- \overline {h} {l} / ({r}_{s} {r}_{d} + \overline {h}^{{2}})$ (rad), respectively ${D},{r}_{d},\;{r}_{s},\overline {h} $ and ${l}$ represent transducer diameter, radius of scan trajectory, radius of source position, unit helical pitch and the distance from targets to helical scan plane, respectively). This theoretical result also suits for circular and cylindrical scan in case of $\overline {h} = {0}$. [ABSTRACT FROM AUTHOR]

Biomedical imaging is unequivocally dependent on the ability to reconstruct interpretable and high-quality images from acquired sensor data. This reconstruction process is pivotal across many applications, spanning from magnetic resonance imaging to ultrasound imaging. While advanced data-adaptive reconstruction methods can recover much higher image quality than traditional approaches, their implementation often poses a high computational burden. In ultrasound imaging, this burden is significant, especially when striving for low-cost systems, and has motivated the development of high-resolution and high-contrast adaptive beamforming methods. Here we show that deep neural networks, that adopt the algorithmic structure and constraints of adaptive signal processing techniques, can efficiently learn to perform fast high-quality ultrasound beamforming using very little training data. We apply our technique to two distinct ultrasound acquisition strategies (plane wave, and synthetic aperture), and demonstrate that high image quality can be maintained when measuring at low data-rates, using undersampled array designs. Beyond biomedical imaging, we expect that the proposed deep learning based adaptive processing framework can benefit a variety of array and signal processing applications, in particular when data-efficiency and robustness are of importance. [ABSTRACT FROM AUTHOR]