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Imaging Dynamics Beneath Turbid Media via Parallelized Single‐Photon Detection

Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well‐established that the temporal correlation of sc...

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Detalles Bibliográficos
Autores principales: Xu, Shiqi, Yang, Xi, Liu, Wenhui, Jönsson, Joakim, Qian, Ruobing, Konda, Pavan Chandra, Zhou, Kevin C., Kreiß, Lucas, Wang, Haoqian, Dai, Qionghai, Berrocal, Edouard, Horstmeyer, Roarke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9404405/
https://www.ncbi.nlm.nih.gov/pubmed/35748188
http://dx.doi.org/10.1002/advs.202201885
Descripción
Sumario:Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well‐established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such temporal correlation data to demonstrate deep‐tissue video reconstruction of decorrelation dynamics. In this work, a single‐photon avalanche diode array camera is utilized to simultaneously monitor the temporal dynamics of speckle fluctuations at the single‐photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. Then a deep neural network is applied to convert the acquired single‐photon measurements into video of scattering dynamics beneath rapidly decorrelating tissue phantoms. The ability to reconstruct images of transient (0.1–0.4 s) dynamic events occurring up to 8 mm beneath a decorrelating tissue phantom with millimeter‐scale resolution is demonstrated, and it is highlighted how the model can flexibly extend to monitor flow speed within buried phantom vessels.