Sub-Nyquist sampling-based high-frequency photoacoustic computed tomography.

High-frequency (greater than 30 MHz) photoacoustic computed tomography (PACT) provides the opportunity to reveal finer details of biological tissues with high spatial resolution. To record photoacoustic signals above 30 MHz, sampling rates higher than 60 MHz are required according to the Nyquist sampling criterion. However, the highest sampling rates supported by existing PACT systems are typically within the range of 40-60 MHz. Herein, we propose a novel PACT imaging method based on sub-Nyquist sampling. The results of numerical simulation, phantom experiment, and in vivo experiment demonstrate that the proposed imaging method can achieve high-frequency PACT imaging with a relatively low sampling rate. An axial resolution of 22 µm is achieved with a 30-MHz transducer and a 41.67-MHz sampling rate. To the best of our knowledge, this is the highest axial resolution ever achieved in PACT based on a sampling rate of not greater than 60 MHz. This work is expected to provide a practical way for high-frequency PACT imaging with limited sampling rates.

On the imaging depth limit of photoacoustic tomography in the visible and first near-infrared windows.

It is well known that photoacoustic tomography (PAT) can circumvent the photon scattering problem in optical imaging and achieve high-contrast and high-resolution imaging at centimeter depths. However, after two decades of development, the long-standing question of the imaging depth limit of PAT in biological tissues remains unclear. Here we propose a numerical framework for evaluating the imaging depth limit of PAT in the visible and the first near-infrared windows. The established framework simulates the physical process of PAT and consists of seven modules, including tissue modelling, photon transportation, photon to ultrasound conversion, sound field propagation, signal reception, image reconstruction, and imaging depth evaluation. The framework can simulate the imaging depth limits in general tissues, such as the human breast, the human abdomen-liver tissues, and the rodent whole body and provide accurate evaluation results. The study elucidates the fundamental imaging depth limit of PAT in biological tissues and can provide useful guidance for practical experiments.

Multibeam minimum variance beamforming for ring array ultrasound imaging.

Objective. Delay-and-sum (DAS) and minimum variance (MV) are two of the most important beamformers researched in ultrasound imaging. Compared with DAS, MV beamformer is different in respect of the aperture weights calculation, and can enhance the image quality by minimizing interference signal power. Various MV beamformers in linear array are studied, but linear array only provides a limited field of view. Ring array can provide better resolution and a full viewing angle; however, few studies have been explored based on ring array transducers.Approach. In this study, we proposed the multibeam MV (MB-MV) beamformer based on the conventional MV to enhance the image quality in ring array ultrasound imaging. To assess the effectiveness of the proposed approach, we conducted simulations, phantom experiments, andin vivohuman experiments to compare MB-MV with DAS and spatial smoothing (SS) MV beamformers.Main results. The results show that the MB-MV method achieves at least 50% enhancement in terms of full width at half maximum compared to the others. Additionally, the MB-MV method improves the contrast ratio by approximate 6 dB and 4 dB compared with DAS and SS MV, respectively.Significance. This work demonstrates the feasibility of MB-MV method for ring array ultrasound imaging, and proves that MB-MV can improve the imaging quality in medical ultrasound imaging. According to our results, MB-MV method provides great potential in distinguishing between lesion and non-lesion areas in clinics, and further promotes the practical application of ring arrays in ultrasound imaging.

Ultrasound-guided adaptive photoacoustic tomography.

Image formation in photoacoustic tomography (PAT) is generally based on the assumption that biological tissues are acoustically homogeneous. However, this does not hold, especially when strongly heterogeneous tissues, such as bones and air cavities, are present. Tissue heterogeneity can cause acoustic reflection, refraction, and scattering at interfaces, which may create distortions and artifacts in final images. To mitigate this problem, we propose an adaptive photoacoustic (PA) image reconstruction method based on prior structural information of an acoustically heterogeneous region extracted from ultrasound images. The method works in three steps: acoustic heterogeneity identification via ultrasound imaging; acoustically heterogeneous region segmentation; and adaptive time-domain raw data truncation and image reconstruction. The data truncation is based on a variable cutoff time, which can be adaptively determined according to the relative position of a transducer and an acoustically heterogeneous region. Numerical and in vivo experimental imaging results of human fingers demonstrate that the proposed ultrasound-guided adaptive image reconstruction method can effectively suppress acoustic heterogeneity-induced artifacts and substantially improve image quality. This work provides a practical way to mitigate the influence of acoustic heterogeneity in PAT.

Ultracompact meta-imagers for arbitrary all-optical convolution.

Electronic digital convolutions could extract key features of objects for data processing and information identification in artificial intelligence, but they are time-cost and energy consumption due to the low response of electrons. Although massless photons enable high-speed and low-loss analog convolutions, two existing all-optical approaches including Fourier filtering and Green's function have either limited functionality or bulky volume, thus restricting their applications in smart systems. Here, we report all-optical convolutional computing with a metasurface-singlet or -doublet imager, considered as the third approach, where its point spread function is modified arbitrarily via a complex-amplitude meta-modulator that enables functionality-unlimited kernels. Beyond one- and two-dimensional spatial differentiation, we demonstrate real-time, parallel, and analog convolutional processing of optical and biological specimens with challenging pepper-salt denoising and edge enhancement, which significantly enrich the toolkit of all-optical computing. Such meta-imager approach bridges multi-functionality and high-integration in all-optical convolutions, meanwhile possessing good architecture compatibility with digital convolutional neural networks.

In Vivo Photoacoustic Sentinel Lymph Node Imaging Using Clinically-Approved Carbon Nanoparticles.

OBJECTIVE: Breast cancer is the most common type of invasive cancer and one of the leading causes of cancer death in women worldwide. Correct staging of breast cancer is critical to the survival rate of the patients. Sentinel lymph node (SLN) biopsy (SLNB), currently the gold standard technique for breast cancer staging, requires preoperative and intraoperative image guidance for noninvasive SLN identification and minimal surgical invasion. However, existing image guidance techniques suffer from a variety of limitations, such as ionizing radiation, high cost, and poor imaging depth. To address the clinical challenges, new methodology has to be developed. METHODS: We developed a photoacoustic (PA) imaging procedure for noninvasive and nonradioactive SLN identification and biopsy guidance enhanced with a clinically-approved lymphatic tracer, i.e., carbon nanoparticles (CNPs) suspension injection. RESULTS: In vivo experiments show that the proposed procedure could sensitively identify the SLN and provide high-contrast image guidance for fine-needle aspiration simulation. In addition, we demonstrated that CNPs have significantly better performance than other commonly-used contrast agents, such as methylene blue and indocyanine green. CONCLUSION: PA imaging technique using clinically-approved CNPs as the contrast agent is capable for noninvasive and nonradioactive SLN identification and high-contrast biopsy guidance, and should be considered as a new tool for assisting SLNB in breast cancer staging. SIGNIFICANCE: The proposed CNPs-enhanced PA imaging technique provides a practical way for SLN identification and biopsy guidance for breast cancer patients and paves the way for clinical translation of PA SLN imaging.

Three-dimensional interventional photoacoustic imaging for biopsy needle guidance with a linear array transducer.

Needle placement is important for many clinical interventions, such as tissue biopsy, regional anesthesia and drug delivery. It is essential to visualize the spatial position of the needle and the target tissue during the interventions using appropriate imaging techniques. Based on the contrast of optical absorption, photoacoustic imaging is well suited for the guidance of interventional procedures. However, conventional photoacoustic imaging typically provides two-dimensional (2D) slices of the region of interest and could only visualize the needle and the target when they are within the imaging plane of the probe at the same time. This requires great alignment skill and effort. To ease this problem, we developed a 3D interventional photoacoustic imaging technique by fast scanning a linear array ultrasound probe and stitching acquired image slices. in vivo sentinel lymph node biopsy experiment shows that the technique could precisely locate a needle and a sentinel lymph node in a tissue volume while a perfusion experiment demonstrates that the technique could visualize the 3D distribution of injected methylene blue dye underneath the skin at high temporal and spatial resolution. The proposed technique provides a practical way for photoacoustic image-guided interventions.

Fabrication of a multilayer tissue-mimicking phantom with tunable optical properties to simulate vascular oxygenation and perfusion for optical imaging technology.

Vast research has been carried out to fabricate tissue-mimicking phantoms, due to their convenient use and ease of storage, to assess and validate the performance of optical imaging devices. However, to the best of our knowledge, there has been little research on the use of multilayer tissue phantoms for optical imaging technology, although their structure is closer to that of real skin tissue. In this work, we design, fabricate, and characterize multilayer tissue-mimicking phantoms, with a morphological mouse ear blood vessel, that contain an epidermis, a dermis, and a hypodermis. Each tissue-mimicking phantom layer is characterized individually to match specific skin tissue layer characteristics. The thickness, optical properties (absorption coefficient and reduced scattering coefficient), oxygenation, and perfusion of skin are the most critical parameters for disease diagnosis and for some medical equipment. These phantoms can be used as calibration artifacts and help to evaluate optical imaging technologies.