Motion Artifact Correction in Deep-Tissue Three-Photon Fluorescence Microscopy Using Adaptive Optical Flow Learning With Transformer.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Motion Artifact Correction in Deep-Tissue Three-Photon Fluorescence Microscopy Using Adaptive Optical Flow Learning With Transformer.
Συγγραφείς:	Li Y; State Key Laboratory of Extreme Photonics and Instrumentation, Centre for Optical and Electromagnetic Research, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, China., Zhang R; The School of Electrical and Information Engineering, Tianjin University, Tianjin, China., Li K; Zhejiang University-University of Edinburgh Institute, Zhejiang University, School of Medicine, Haining, China., Wang Y; School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China., He M; State Key Laboratory of Extreme Photonics and Instrumentation, Centre for Optical and Electromagnetic Research, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, China., Qian J; State Key Laboratory of Extreme Photonics and Instrumentation, Centre for Optical and Electromagnetic Research, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, China.
Πηγή:	Journal of biophotonics [J Biophotonics] 2025 Dec; Vol. 18 (12), pp. e202500407. Date of Electronic Publication: 2025 Aug 17.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101318567 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1864-0648 (Electronic) Linking ISSN: 1864063X NLM ISO Abbreviation: J Biophotonics Subsets: MEDLINE
Imprint Name(s):	Original Publication: Weinheim : Wiley-VCH
Ιατρικοί όροι (MeSH):	Microscopy, Fluorescence, Multiphoton/methods , Image Processing, Computer-Assisted/methods , Artifacts* , Deep Learning*, Brain/diagnostic imaging ; Brain/blood supply ; Intestines/diagnostic imaging ; Intestines/blood supply ; Imaging, Three-Dimensional ; Movement ; Animals
Περίληψη:	Three-photon fluorescence microscopy (3PFM) enables high-resolution volumetric imaging in deep tissues but is often hindered by motion artifacts in dynamic physiological environments. Existing solutions, including surgical fixation and conventional image registration algorithms, frequently fail under intense and nonuniform motions, particularly in low-texture or highly deformed regions. To overcome these problems, we propose StabiFormer, a transformer-based optical flow learning network designed for robust motion correction. Central to StabiFormer is the stable-dynamic feature extractor, which captures interlayer dynamics to facilitate accurate image registration. Our validation across cerebrovascular and intestinal 3PFM datasets demonstrates that StabiFormer achieves near-zero displacement error relative to ground truth in brain vasculature. Furthermore, it enables artifact-free 3D visualization of intestinal macrophages and vasculature at 300 μm depth, a physiologically relevant depth for studying intestinal immune microvasculature. These results establish a noninvasive computational solution for motion-artifact-free volumetric imaging, paving the way for quantitative investigations in previously inaccessible dynamic organ systems. (© 2025 Wiley‐VCH GmbH.)
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Grant Information:	U23A20487 the National Natural Science Foundation of China; 61975172 the National Natural Science Foundation of China; 2024YFF1206700 the National Key R&D Program of China; Dr. Li Dak Sum & Yip Yio Chin Development Fund for Regenerative Medicine, Zhejiang University
Contributed Indexing:	Keywords: intravital imaging; motion artifact correction; optical flow learning; three‐photon fluorescence microscopy; transformer network
Entry Date(s):	Date Created: 20250817 Date Completed: 20251214 Latest Revision: 20260708
Update Code:	20260708
DOI:	10.1002/jbio.202500407
PMID:	40819894
Βάση Δεδομένων:	MEDLINE

Περιγραφή
ISSN:	1864-0648
DOI:	10.1002/jbio.202500407