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Therefore, it is of paramount importance to find an effective way to sense vessel structures. Traditional methods, including infrared and Doppler sensing modalities, are limited by optical diffusion and ultrasonic scattering that are not good at vessel sensing with high performance. In comparison, we argue photoacoustic (PA) sensing is an emerging technique that can image 3D vessel structure deep in tissue with high-resolution visualization, maintaining the advantages of both optical and ultrasound methods. In this work, we propose and develop PAvessel, a practical 3D vessel structure sensing system based on PA effects. The entire sensing system comprises two key components, PA sensing hardware and PA sensing software. Specifically, the hardware mainly consists of a linear ultrasound transducer array, an ultrasound data acquisition system, and a neodymium-doped yttrium aluminum garnet (Nd:YAG) laser. After receiving the PA raw data, we use the advanced image reconstruction and 3D photoacoustic vein model to establish the 3D vessel structure model. We validated its effectiveness, cost-effectiveness and high resolution of PAvessel in the evaluation. The system achieves 52% higher signal-to-noise ratio (SNR) compared to the other methods. Furthermore, considering the 3D palm vein contains high dimensional human features and is almost impossible to forge, we also explored its applications in palm biometrics. In a pilot study with 10 participants, PAvessel, combined with a 3D vessel structure matching algorithm (EMD-VT), has proven to possess high accuracy and robustness as a biometric. PAvessel achieves the precision and recall of 98.33% and 97.37%, respectively.<\/jats:p>","DOI":"10.1145\/3264932","type":"journal-article","created":{"date-parts":[[2018,9,19]],"date-time":"2018-09-19T11:58:41Z","timestamp":1537358321000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["PAvessel"],"prefix":"10.1145","volume":"2","author":[{"given":"Zhengxiong","family":"Li","sequence":"first","affiliation":[{"name":"University at Buffalo, the State University of New York, Amherst, NY, USA"}]},{"given":"Yuehang","family":"Wang","sequence":"additional","affiliation":[{"name":"University at Buffalo, the State University of New York, Amherst, NY, USA"}]},{"given":"Aditya Singh","family":"Rathore","sequence":"additional","affiliation":[{"name":"University at Buffalo, the State University of New York, Amherst, NY, USA"}]},{"given":"Chen","family":"Song","sequence":"additional","affiliation":[{"name":"University at Buffalo, the State University of New York, Amherst, NY, USA"}]},{"given":"Nikhila","family":"Nyayapathi","sequence":"additional","affiliation":[{"name":"University at Buffalo, the State University of New York, Amherst, NY, USA"}]},{"given":"Tri","family":"Vu","sequence":"additional","affiliation":[{"name":"University at Buffalo, the State University of New York, Amherst, NY, USA"}]},{"given":"Jun","family":"Xia","sequence":"additional","affiliation":[{"name":"University at Buffalo, the State University of New York, Amherst, NY, USA"}]},{"given":"Wenyao","family":"Xu","sequence":"additional","affiliation":[{"name":"University at Buffalo, the State University of New York, Amherst, NY, USA"}]}],"member":"320","published-online":{"date-parts":[[2018,9,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Doppler ultrasound color flow imaging in the study of breast cancer: preliminary findings. 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