PurposeVascular distribution is important information for diagnosing diseases and supporting surgery. Photoacoustic imaging is a technology that can image blood vessels noninvasively and with high resolution. In photo...
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PurposeVascular distribution is important information for diagnosing diseases and supporting surgery. Photoacoustic imaging is a technology that can image blood vessels noninvasively and with high resolution. In photoacoustic imaging, a hemispherical arraysensor is especially suitable for measuring blood vessels running in various directions. However, as a hemispherical arraysensor, a sparse array sensor is often used due to technical and cost issues, which causes artifacts in photoacoustic images. Therefore, in this study, we reduce these artifacts using deep learning technology to generate signals of virtual dense array *** 2D virtual arraysensor signals using a 3D convolutional neural network (CNN) requires huge computational costs and is impractical. Therefore, we installed virtual sensors between the real sensors along the spiral pattern in three different directions and used a 2D CNN to generate signals of the virtual sensors in each direction. Then we reconstructed a photoacoustic image using the signals from both the real sensors and the virtual *** evaluated the proposed method using simulation data and human palm measurement data. We found that these artifacts were significantly reduced in the images reconstructed using the proposed method, while the artifacts were strong in the images obtained only from the real sensor *** the proposed method, we were able to significantly reduce artifacts, and as a result, it became possible to recognize deep blood vessels. In addition, the processing time of the proposed method was sufficiently applicable to clinical measurement.
High-frequency fringe patterns found in non-null interferometric testing of aspheres require the use of special detector arrays containing small, widely spaced pixels. A sparsearray camera with the ability to detect ...
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High-frequency fringe patterns found in non-null interferometric testing of aspheres require the use of special detector arrays containing small, widely spaced pixels. A sparsearray camera with the ability to detect high spatial frequencies has been developed. The modulation transfer function (MTF) of the camera is measured using a sinusoidal fringe pattern generated by a Mach-Zehnder interferometer. Spatial frequencies up to 400 cycles/mm are generated and used to characterize the MTF of the camera. (C) 2004 Society of Photo-Optical Instrumentation Engineers.
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