Accuracy of peak-power compensation in fiber-guided and free-space acoustic-resolution photoacoustic microscopy.

Acoustic resolution photoacoustic microscopy (AR-PAM) has gained much attention in the past two decades due to its high contrast, scalable resolution, and relatively higher imaging depth. Multimode optical fibers (MMF) are extensively used to transfer light to AR-PAM imaging scan-head from the laser source. Typically, peak-power-compensation (PPC) is used to reduce the effect of pulse-to-pulse peak-power variation in generated photoacoustic (PA) signals. In MMF, the output intensity profile fluctuates due to the coherent nature of light and mode exchange caused by variations in the bending of the fibers during scanning. Therefore, using a photodiode (PD) to capture a portion of the total power of pulses as a measure of illuminated light on the sample may not be appropriate for accurate PPC. In this study, we have investigated the accuracy of PPC in fiber-guided and free-space AR-PAM systems. Experiments were conducted in the transparent and highly scattering medium. Based on obtained results for the MMF-based system, to apply PPC to the generated PA signals, tightly focused light confocal with the acoustic focus in a transparent medium must be used. In the clear medium and highly focused illumination, enhancement of about 45% was obtained in the homogeneity of an optically homogeneous sample image. In addition, it is shown that, as an alternative, free-space propagation of the laser pulses results in more accurate PPC in both transparent and highly scattering mediums. In free-space light transmission, enhancement of 25-75% was obtained in the homogeneity of the optically homogeneous sample image.

Axial accuracy and signal enhancement in acoustic-resolution photoacoustic microscopy by laser jitter effect correction and pulse energy compensation.

In recent years, photoacoustic imaging has found vast applications in biomedical imaging. Photoacoustic imaging has high optical contrast and high ultrasound resolution allowing deep tissue non-invasive imaging beyond the optical diffusion limit. Q-switched lasers are extensively used in photoacoustic imaging due to the availability of high energy and short laser pulses, which are essential for high-resolution photoacoustic imaging. In most cases, this type of light source suffers from pulse peak-power energy variations and timing jitter noise, resulting in uncertainty in the output power and arrival time of the laser pulses. These problems cause intensity degradation and temporal displacement of generated photoacoustic signals which in turn deteriorate the quality of the acquired photoacoustic images. In this study, we used a high-speed data acquisition system in combination with a fast photodetector and a software-based approach to capture laser pulses precisely in order to reduce the effect of timing jitter and normalization of the photoacoustic signals based on pulse peak-powers simultaneously. In the experiments, maximum axial accuracy enhancement of 14 µm was achieved in maximum-amplitude projected images on XZ and YZ planes with ±13.5 ns laser timing jitter. Furthermore, photoacoustic signal enhancement of 77% was obtained for 75% laser pulses peak-power stability.

Holographic optical field recovery using a regularized untrained deep decoder network.

Image reconstruction using minimal measured information has been a long-standing open problem in many computational imaging approaches, in particular in-line holography. Many solutions are devised based on compressive sensing (CS) techniques with handcrafted image priors or supervised deep neural networks (DNN). However, the limited performance of CS methods due to lack of information about the image priors and the requirement of an enormous amount of per-sample-type training resources for DNNs has posed new challenges over the primary problem. In this study, we propose a single-shot lensless in-line holographic reconstruction method using an untrained deep neural network which is incorporated with a physical image formation algorithm. We demonstrate that by modifying a deep decoder network with simple regularizers, a Gabor hologram can be inversely reconstructed via a minimization process that is constrained by a deep image prior. The outcoming model allows to accurately recover the phase and amplitude images without any training dataset, excess measurements, or specific assumptions about the object's or the measurement's characteristics.