Snapshot time-reversed ultrasonically encoded optical focusing guided by time-reversed photoacoustic wave.

Deep-tissue optical imaging is a longstanding challenge limited by scattering. Both optical imaging and treatment can benefit from focusing light in deep tissue beyond one transport mean free path. Wavefront shaping based on time-reversed ultrasonically encoded (TRUE) optical focusing utilizes ultrasound focus, which is much less scattered than light in biological tissues as the 'guide star'. However, the traditional TRUE is limited by the ultrasound focusing area and pressure tagging efficiency, especially in acoustically heterogeneous medium. Even the improved version of iterative TRUE comes at a large time consumption, which limits the application of TRUE. To address this problem, we proposed a method called time-reversed photoacoustic wave guided time-reversed ultrasonically encoded (TRPA-TRUE) optical focusing by integrating accurate ultrasonic focusing through acoustically heterogeneous medium guided by time-reversing PA signals, and the ultrasound modulation of diffused coherent light with optical phase conjugation (OPC), achieving dynamic focusing of light into scattering medium. Simulation results show that the focusing accuracy of the proposed method has been significantly improved compared with conventional TRUE, which is more suitable for practical applications that suffers severe acoustic distortion, e.g. transcranial optical focusing.

Compressed sensing for photoacoustic computed tomography based on an untrained neural network with a shape prior.

Photoacoustic (PA) computed tomography (PACT) shows great potential in various preclinical and clinical applications. A great number of measurements are the premise that obtains a high-quality image, which implies a low imaging rate or a high system cost. The artifacts or sidelobes could pollute the image if we decrease the number of measured channels or limit the detected view. In this paper, a novel compressed sensing method for PACT using an untrained neural network is proposed, which decreases a half number of the measured channels and recovers enough details. This method uses a neural network to reconstruct without the requirement for any additional learning based on the deep image prior. The model can reconstruct the image only using a few detections with gradient descent. As an unlearned strategy, our method can cooperate with other existing regularization, and further improve the quality. In addition, we introduce a shape prior to easily converge the model to the image. We verify the feasibility of untrained network-based compressed sensing in PA image reconstruction and compare this method with a conventional method using total variation minimization. The experimental results show that our proposed method outperforms 32.72% (SSIM) with the traditional compressed sensing method in the same regularization. It could dramatically reduce the requirement for the number of transducers, by sparsely sampling the raw PA data, and improve the quality of PA image significantly.

Frequency-domain Dual-contrast Photoacoustic Imaging with Chirp Modulation.

Photoacoustic imaging has shown its great potential in biomedical imaging. A variety of imaging applications, like blood oxygenation for functional imaging, have been widely studied during the past few decades. Most of the previous works are based on the tissue's endogenous or nanoprobe's extraneous optical absorbance. In this paper, we proposed frequency-domain dual-contrast photoacoustic imaging aiming at exploring both optical absorption and mechanical property (e.g., viscoelasticity) of tissue. Instead of conventionally used pulsed excitation, a chirp-modulated laser signal is used to excite the sample to induce photoacoustic signals. On one hand, the optical absorption contrast is obtained by cross-correlating the PA signals with the chirp pattern. On the other hand, mechanical property is obtained by performing the Fourier transform to analyze the frequency spectrum. Experimental results revealed that samples with higher density-to-viscoelasticity ratio show larger quality factor in the received PA signals' spectrum. Both theoretical analysis and experimental demonstrations are performed to prove the feasibility of the proposed method.

Photoacoustic-guided Transcranial HIFU with Combined Time-reversal and Genetic Algorithm.

High intensity focused ultrasound (HIFU) is a noninvasive therapy used to induce tissue ablation for treating malignant tissues. Photoacoustic (PA) has recently been proposed as an alternative method to guide HIFU. In this paper, we present a method of HIFU guided by time-reversing the transcranial PA signals of an optically selective target in a nonselective background. To improve the focus performance on target area, we further propose to utilize the time-reversed PA signals as the initial population of Genetic Algorithm (GA) to optimize the focusing iteratively. In particular, we mimic both optical and acoustic parameters of the human brain and intracranial media in the simulation study. Experimental results show that the focusing accuracy of the proposed method has been significantly improved compared to just one-step PA time-reversal. At the same time, the combination of TR and GA makes the iteration time consumption of the optimization process less than other traditional algorithms without TR, showing its potential HIFU in clinical scenarios.

Human Breast Numerical Model Generation Based on Deep Learning for Photoacoustic Imaging.

Photoacoustic imaging which combines high contrast of optical imaging and high resolution of ultrasound imaging, can provide functional information, potentially playing a crucial role in the study of breast cancer diagnostics. However, open source dataset for PA imaging research is insufficient on account of lacking clinical data. To tackle this problem, we propose a method to automatically generate breast numerical model for photoacoustic imaging. The different type of tissues is automatically extracted first by employing deep learning and other methods from mammography. And then the tissues are combined by mathematical set operation to generate a new breast image after being assigned optical and acoustic parameters. Finally, breast numerical model with proper optical and acoustic properties are generated, which are specifically suitable for PA imaging studies, and the experiment results indicate that our method is feasible with high efficiency.