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. 2023 Jun 19;10:29. doi: 10.1186/s40580-023-00377-3

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 9 — A deep-learning-assisted PAI system and its results. a Schematic illustration of a PAI system with a down-up oriented 1024-element spherical array transducer. Inset depicts active and inactive elements in the transducer in each acquisition mode. b Rat whole-body images in vivo before and after intravenous injection of ICG solution. Top row, with full data acquisition (4 laser shots, 1064 elements); middle row, with cluster data acquisition (1 laser shot, 256 elements); bottom row, predicted images generated by a deep-learning algorithm. c Spectrally unmixed SO₂ distribution of the brain in rats breathing different mixtures of O₂ and N₂ gas in alternation. PAI, photoacoustic imaging; US, ultrasound; OPO, optical parametric oscillator; DAQ, data acquisition module; MUX, multiplexer; ICG, indocyanine green; SO₂, hemoglobin oxygen saturation; O₂, oxygen; N₂, nitrogen. The images are reproduced with permission from [87]