Comprehensive evaluation of microvascular function under normal and pathological conditions requires high-resolution three-dimensional microangiography capable of providing both morphological and functional information. Herein, we propose the stereovision Diffuse Optical Localization imaging (sDOLI) approach to attain transcranial volumetric brain microangiography through triangulation and stereo-matching of images collected with two short-wave infrared cameras. The spatio-temporal sparsity of flowing microparticles allows their precise localization while minimizing structural overlaps occurring in the dual-view projections. sDOLI is shown to preserve high spatial resolution which enables transcranial mapping of murine cortical microcirculation at capillary resolution while retrieving quantitative functional information across the entire mouse cortex.
Acoustic-resolution optoacoustic microscopy (AR-OAM) visualizes internal tissue structures at millimeter to centimeter scale depths with high spatial resolution. The imaging performance mainly depends on the geometry and detection characteristics of the ultrasound transducer. Reconstruction methods incorporating transducer effects are essential to optimize achievable resolution, contrast and overall image quality. Model-based (MB) reconstruction has been shown to provide excellent imaging performance in several optoacoustic embodiments, due to its capacity to accurately model the transducer. However, the applicability of MB reconstruction methods in AR-OAM has been hampered by the high computational cost. Here, we propose an efficient MB reconstruction framework for largescale AR-OAM by considering scanning symmetries, which enabled capitalizing the computational power of a graphics processing unit. The suggested MB reconstruction method is shown to significantly improve the imaging performance of AR-OAM compared to synthetic aperture focusing technique, as validated in in vivo mouse skin experiment.
Melanoma, developing from melanocytes, is the deadliest type of malignant skin tumors in the world. Due to high light absorption of melanin, rare circulating melanoma cells, as an endogenous marker for metastasis at the early stage, can be quantitatively detected in small superficial vessels of mouse ears by in vivo photoacoustic flow cytometry (PAFC). Before clinical application, the capability of promising PAFC platform should be verified and optimized by mouse vessels, which are similar in size and depth to human vessels. In the current study, compared with optical resolution PAFC (OR-PAFC), we build acoustic resolution PAFC (AR-PAFC) using focused ultrasonic transducer and 1064 nm laser with lower pulse rate, leading to higher detection depth and lower laser power density in mouse models. Besides, based on laser frequency doubling and high absorption coefficient of hemoglobin at 532nm wavelength, the blood vessels can be positioned by lowcost navigation system rather than the expensive system of two coupled lasers or charged coupled device with depth limitation. We confirm that AR-PAFC can be applied to noninvasive label-free counting of circulating melanoma cells in mouse tail veins, and validated by in vitro assays using phantom models, which simulates the scattering and absorption coefficients of living tissue. These results show that AR-PAFC platform has great potential for preoperative diagnosis and postoperative evaluation of melanoma patients.
Melanoma is a malignant tumor whose circulating tumor cell (CTC) count has been shown as a prognostic marker for metastasis development. Therefore detection of circulating melanoma cells plays an important role in monitoring tumor metastasis and prevention after diagnosis. In Vivo Photoacoustic Flow Cytometry (PAFC) is established here to achieve in vivo melanoma inspection, meanwhile guarantees non-invasive and real-time detection.Accurate tumor cell detection is of great significance to achieve highly specific diagnosis and avoid unnecessary medical tests.However, the amount of data detected by PAFC is large and original photoacoustic signal is mixed with various noises.The traditional triple mean square deviation method has lower accuracy and consumes a lot of time in data processing. Here, a classification approach in photoacoustic is proposed, which could discriminate signals and noises based on features extracted from photoacoustic waves compared to normal cells using Support Vector Machines algorithm. Due to similar shape and structure of cells, the photoacoustic signals usually have similar vibration mode. By analyzing the correlations and the signal features in time domain and frequency domain, we finally choose the continuity, amplitude, and photoacoustic waveform pulse width as the features to characterize the signal.More than 600,000 samples were selected as the training set (normalized in advance), and a classifier with a precision of 85% accuracy to filter out the photoacoustic signals rapidly was trained by the support vector machine method.The classifier introduced here has proved to optimize the signal acquisition and reduce signal processing time, realizing real-time detection and real-time analysis in PAFC.
Malignant melanoma, developing from melanocytes, is a kind of high metastatic tumor. Circulating melanoma cells, as a marker for metastasis development, are found in blood or lymphatic system at the early stage. Thus, quantitative detection of circulating melanoma cells has great significance to diagnose carcinoma and monitor tumor metastasis. In contrast to in vitro detection methods and in vivo fluorescence-based flow cytometry (IVFC), the in vivo photoacoustic flow cytometry (PAFC) utilizes melanoma cells’ predominant optical absorption in the near-infrared range over other absorbers to receive the photoacoustic (PA) signals without fluorescent dye labeling in a non-invasive way. The sensitivity of the PAFC system was verified by in vitro and in vivo experiments. Besides, we solves the technical problem that blood vessels cannot be positioned in the process of in vivo detection by designing the laser signal positioning navigation system using frequency doubling technique. PAFC provides a new tool for in vivo, label-free, and noninvasive detection of circulating tumor cells (CTCs) and has strong practicality and favorable clinical prospects.
Melanoma, developing from melanocytes, is the most serious type of skin cancer. Circulating melanoma cells, the prognosis marker for metastasis, are present in the circulation at the early stage. Thus, quantitative detection of rare circulating melanoma cells is essential for monitoring tumor metastasis and prognosis evaluation. Compared with in vitro assays, in vivo flow cytometry is able to identify circulating tumor cells without drawing blood. Here, we built in vivo photoacoustic flow cytometry based on the high absorption coefficient of melanoma cells, which is applied to labelfree counting of circulating melanoma cells in tumor-bearing mice.
Melanoma is a kind of a malignant tumor of melanocytes with the properties of high mortality and high metastasis rate. The circulating melanoma cells with the high content of melanin can be detected by light absorption to diagnose and treat cancer at an early stage. Compared with conventional detection methods such as in vivo flow cytometry (IVFC) based on fluorescence, the in vivo photoacoustic flow cytometry (PAFC) utilizes melanin cells as biomarkers to collect the photoacoustic (PA) signals without toxic fluorescent dyes labeling in a non-invasive way. The information of target tumor cells is helpful for data analysis and cell counting. However, the raw signals in PAFC system contain numerous noises such as environmental noise, device noise and in vivo motion noise. Conventional denoising algorithms such as wavelet denoising (WD) method and means filter (MF) method are based on the local information to extract the data of clinical interest, which remove the subtle feature and leave many noises. To address the above questions, the nonlocal means (NLM) method based on nonlocal data has been proposed to suppress the noise in PA signals. Extensive experiments on in vivo PA signals from the mice with the injection of B16F10 cells in caudal vein have been conducted. All the results indicate that the NLM method has superior noise reduction performance and subtle information reservation.
Melanoma is known as a malignant tumor of melanocytes, which usually appear in the blood circulation at the metastasis stage of cancer. Thus the detection of circulating melanoma cells is useful for early diagnosis and therapy of cancer. Here we have developed an in vivo photoacoustic flow cytometry (PAFC) based on the photoacoustic effect to detect melanoma cells. However, the raw signals we obtain from the target cells contain noises such as environmental sonic noises and electronic noises. Therefore we apply correlation comparison and feature separation methods to the detection and verification of the in vivo signals. Due to similar shape and structure of cells, the photoacoustic signals usually have similar vibration mode. By analyzing the correlations and the signal features in time domain and frequency domain, we are able to provide a method for separating photoacoustic signals generated by target cells from background noises. The method introduced here has proved to optimize the signal acquisition and signal processing, which can improve the detection accuracy in PAFC.
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