Deep learning for biomedical photoacoustic imaging

• Verfasst von: Gröhl, Janek [VerfasserIn]
• Verfasst von: Schellenberg, Melanie [VerfasserIn]
• Verfasst von: Dreher, Kris [VerfasserIn]
• Verfasst von: Maier-Hein, Lena [VerfasserIn]
• Titel: Deep learning for biomedical photoacoustic imaging
• Titelzusatz: a review
• Verf.angabe: Janek Gröhl, Melanie Schellenberg, Kris Dreher, Lena Maier-Hein
• E-Jahr: 2021
• Jahr: 2 February 2021
• Umfang: 15 S.
• Fussnoten: Gesehen am 28.07.2021
• Titel Quelle: Enthalten in: Photoacoustics
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 2013
• Jahr Quelle: 2021
• Band/Heft Quelle: 22(2021) vom: Juni, Artikel-ID 100241, Seite 1-15
• ISSN Quelle: 2213-5979
• DOI: doi:10.1016/j.pacs.2021.100241
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Deep learning
• Sach-SW: Image reconstruction
• Sach-SW: Optoacoustic imaging
• Sach-SW: Photoacoustic imaging
• Sach-SW: Photoacoustic tomography
• Sach-SW: Signal quantification
• K10plus-PPN: 1764780000

Abstract

Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems, which have proven extremely difficult to solve. The application of deep learning methods has recently exploded in popularity, leading to impressive successes in the context of medical imaging and also finding first use in the field of PAI. Deep learning methods possess unique advantages that can facilitate the clinical translation of PAI, such as extremely fast computation times and the fact that they can be adapted to any given problem. In this review, we examine the current state of the art regarding deep learning in PAI and identify potential directions of research that will help to reach the goal of clinical applicability.