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Deep learning protocol for improved photoacoustic brain imaging
Author(s) -
Manwar Rayyan,
Li Xin,
Mahmoodkalayeh Sadreddin,
Asano Eishi,
Zhu Dongxiao,
Avanaki Kamran
Publication year - 2020
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.202000212
Subject(s) - photoacoustic imaging in biomedicine , neuroimaging , translation (biology) , biomedical engineering , deep learning , brain tissue , computer science , photoacoustic tomography , medical physics , medicine , artificial intelligence , chemistry , optics , physics , biochemistry , psychiatry , messenger rna , gene
Abstract One of the key limitations for the clinical translation of photoacoustic imaging is penetration depth that is linked to the tissue maximum permissible exposures (MPE) recommended by the American National Standards Institute (ANSI). Here, we propose a method based on deep learning to virtually increase the MPE in order to enhance the signal‐to‐noise ratio of deep structures in the brain tissue. The proposed method is evaluated in an in vivo sheep brain imaging experiment. We believe this method can facilitate clinical translation of photoacoustic technique in brain imaging, especially in transfontanelle brain imaging in neonates.