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. 2021 Mar 23;10:62. doi: 10.1038/s41377-021-00506-9

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© The Author(s) 2021

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Recurrent-MZ volumetric imaging framework. M is the number of input scans (2D images), and each input scan is paired with its corresponding DPM (Digital Propagation Matrix). b Recurrent-MZ network structure. The network and training details are elucidated in the Materials and Methods section. Z is the set of all the discretized axial positions within the target sample volume, composed of |Z| unique axial planes. Typically, M = 2 or 3 and |Z| ≫ M