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Phase retrieval based on deep learning in grating interferometer

التفاصيل البيبلوغرافية
العنوان: Phase retrieval based on deep learning in grating interferometer
المؤلفون: Ohsung Oh, Youngju Kim, Daeseung Kim, Daniel. S. Hussey, Seung Wook Lee
المصدر: Scientific Reports, Vol 12, Iss 1, Pp 1-10 (2022)
بيانات النشر: Nature Portfolio, 2022.
سنة النشر: 2022
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: Abstract Grating interferometry is a promising technique to obtain differential phase contrast images with illumination source of low intrinsic transverse coherence. However, retrieving the phase contrast image from the differential phase contrast image is difficult due to the accumulated noise and artifacts from the differential phase contrast image (DPCI) reconstruction. In this paper, we implemented a deep learning-based phase retrieval method to suppress these artifacts. Conventional deep learning based denoising requires noise/clean image pair, but it is not feasible to obtain sufficient number of clean images for grating interferometry. In this paper, we apply a recently developed neural network called Noise2Noise (N2N) that uses noise/noise image pairs for training. We obtained many DPCIs through combination of phase stepping images, and these were used as input/target pairs for N2N training. The application of the N2N network to simulated and measured DPCI showed that the phase contrast images were retrieved with strongly suppressed phase retrieval artifacts. These results can be used in grating interferometer applications which uses phase stepping method.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-022-10551-y
URL الوصول: https://doaj.org/article/376ceceba0bb405fb79d275643c6414d
رقم الأكسشن: edsdoj.376ceceba0bb405fb79d275643c6414d
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20452322
DOI:10.1038/s41598-022-10551-y