Academic Journal

Denoising-Assisted Rapid Multiphoton Imaging for Analysing Traumatic Penumbra Microenvironment in Rat Brain.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Denoising-Assisted Rapid Multiphoton Imaging for Analysing Traumatic Penumbra Microenvironment in Rat Brain.
Συγγραφείς: Guo P; Chongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing, China., Chen L; Chongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing, China., Jiang S; Chongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing, China., Ai L; Postdoctoral Workstation, The Central Hospital Affiliated to Chongqing University of Technology, Chongqing University of Technology, Chongqing, China., Shi S; Chongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing, China., Yang D; Postdoctoral Workstation, The Central Hospital Affiliated to Chongqing University of Technology, Chongqing University of Technology, Chongqing, China., Xu J; Chaohu University, Chaohu, China., Chen J; Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China., Lu H; Postdoctoral Workstation, The Central Hospital Affiliated to Chongqing University of Technology, Chongqing University of Technology, Chongqing, China.
Πηγή: Journal of biophotonics [J Biophotonics] 2026 Jun; Vol. 19 (6), pp. e70303.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101318567 Publication Model: Print 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): Brain Injuries, Traumatic*/diagnostic imaging , Brain Injuries, Traumatic*/pathology , Microscopy, Fluorescence, Multiphoton*/methods , Image Processing, Computer-Assisted*/methods , Brain*/diagnostic imaging , Signal-To-Noise Ratio* , Cellular Microenvironment*, Animals ; Rats ; Time Factors ; Rats, Sprague-Dawley ; Male
Περίληψη: Accurate identification of the traumatic penumbra (TP) is essential for understanding secondary injury after traumatic brain injury. However, conventional imaging cannot capture microenvironmental alterations at subcellular resolution. In this study, denoising-assisted multiphoton microscopy (MPM) was used to investigate the microstructural features of the TP in rat brain tissue. Image processing and quantitative analysis were performed to characterize normal tissue, penumbra, and core regions. To accelerate high-quality imaging, we proposed a Dual-Branch Collaborative Network (DBCNet) to restore low-noise images from single-scan data using multi-scan references. The results show that MPM reveals key pathological features such as intracellular edema, vasogenic edema, and cytoplasmic matrix disruption. DBCNet effectively suppresses noise, enhances fine structures, and reduces imaging time to one-fourth without hardware modification. These findings demonstrate that denoising-assisted MPM provides a rapid and efficient strategy for high-resolution imaging and quantitative assessment of the TP microenvironment.
(© 2026 Wiley‐VCH GmbH.)
References: M. C. Dewan, A. Rattani, S. Gupta, et al., “Estimating the Global Incidence of Traumatic Brain Injury,” Journal of Neurosurgery 130 (2019): 1080–1097.
M. B. Cieri and A. J. Ramos, “Astrocytes, Reactive Astrogliosis, and Glial Scar Formation in Traumatic Brain Injury,” Neural Regeneration Research 20 (2025): 20–989.
M. Khan, Y.‐B. Im, A. Shunmugavel, et al., “Administration of S‐Nitrosoglutathione After Traumatic Brain Injury Protects the Neurovascular Unit and Reduces Secondary Injury in a Rat Model of  Controlled Cortical Impact,” Journal of Neuroinflammation 6 (2009): 32.
K. Wang, B. Liu, and J. Ma, “Histiocytoid Sweet's Syndrome Associated With Rheumatoid Arthritis and Pleuritis,” Chinese Medical Journal 127 (2014): 127.
O. S. Al‐Kadi, I. Almallahi, A. Abu‐Srhan, A. M. Mohammad Abushariah, and W. Mahafza, “Unpaired MR‐CT Brain Dataset for Unsupervised Image Translation,” Data in Brief 42 (2022): 108109.
M. Romano, A. Bravin, A. Mittone, et al., “A Multi‐Scale and Multi‐Technique Approach for the Characterization of the Effects of Spatially Fractionated X‐Ray Radiation Therapies in a Preclinical Model,” Cancers (Basel) 13 (2021): 4953.
D. Chen, D. W. Nauen, H.‐C. Park, et al., “Label‐Free Imaging of Human Brain Tissue at Subcellular Resolution for Potential Rapid Intra‐Operative Assessment of Glioma Surgery,” Theranostics 11 (2021): 7222–7234.
N. Fang, L. Shi, X. Su, et al., “Texture Analysis of Fibrous Meningioma Using Label‐Free Multiphoton Microscopy,” Journal of Biophotonics 18 (2025): e202400241.
M. Kotaro and S. Masaaki, “Multiphoton Imaging of Hippocampal Neural Circuits: Techniques and Biological Insights Into Region‐, Cell‐Type‐, and Pathway‐Specific Functions,” Neurophotonics 11 (2024): 33406.
C. A. Renteria, J. Park, C. Zhang, et al., “Large Field‐Of‐View Metabolic Profiling of Murine Brain Tissue Following Morphine Incubation Using Label‐Free Multiphoton Microscopy,” Journal of Neuroscience Methods 408 (2024): 110171.
S. Wang, Y. Li, Y. Xu, et al., “Resection‐Inspired Histopathological Diagnosis of Cerebral Cavernous Malformations Using Quantitative Multiphoton Microscopy,” Theranostics 12 (2022): 6595–6610.
M. Weigert, U. Schmidt, T. Boothe, et al., “Content‐Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy,” Nature Methods 15 (2018): 1090–1097.
Z. Wu, X. Wang, N. Fang, et al., “Automatic and Label‐Free Analysis of the Microstructure Feature Differences Between Normal Brain Tissue, Low‐Grade, and High‐Grade Gliomas Using the Combination of Multiphoton Microscopy and Image Analysis,” Frontiers in Physics 10 (2022): 865455.
C. Tian, Y. Xu, and W. Zuo, “Image Denoising Using Deep Cnn With Batch Renormalization,” Neural Networks 121 (2020): 461–473.
L. Chen, S. Jiang, P. Guo, et al., “UVT—A Network for Super‐Resolution Multiphoton Fluorescence Image Reconstruction,” Biomedical Signal Processing and Control 120 (2026): 110237.
K. Zhang, Y. Li, W. Zuo, L. Zhang, L. V. Gool, and R. Timofte, “Plug‐and‐Play Image Restoration With Deep Denoiser Prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (2022): 6360–6376.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising,” IEEE Transactions on Image Processing 26 (2017): 3142–3155.
J. Zhang, Y. Zhang, J. Gu, J. Dong, L. Kong, and X. J. A. Yang, “Xformer: Hybrid X‐Shaped Transformer for Image Denoising,” 2024 (International Conference on Learning Representations, 2024), 13333–13345.
X. Chu, Z. Tian, Y. Wang, et al., “Twins: Revisiting Spatial Attention Design in Vision Transformers,” in Proceedings of the 35th International Conference on Neural Information Processing Systems, vol. 34 (ACM, 2021).
C. M. Fan, T. J. Liu, and K. H. Liu, “SUNet: Swin Transformer UNet for Image Denoising,” in 2022 IEEE International Symposium on Circuits and Systems (ISCAS) (IEEE, 2022), 2333–2337.
C. Ledig, L. Theis, F. Huszár, et al., “Photo‐Realistic Single Image Super‐Resolution Using a Generative Adversarial Network,” (2017) 105–114.
W. Xu, Y. Xu, T. Chang, and Z. Tu, “Co‐Scale Conv‐Attentional Image Transformers,” in IEEE International Conference on Computer Vision (IEEE, 2021), 9961–9970.
S. W. Zamir, A. Arora, S. H. Khan, M. Hayat, F. S. Khan, and M.‐H. J. I. C. C. o. C. V. Yang, “Recognition in Restormer: Efficient Transformer for High‐Resolution Image Restoration,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2021 (IEEE, 2022), 5718–5729.
Y. Lu, Y. Ying, C. Lin, et al., “UNet‐Att: A Self‐Supervised Denoising and Recovery Model for Two‐Photon Microscopic Image,” Complex & Intelligent Systems 11 (2024): 55.
C. Tian, M. Zheng, W. Zuo, S. Zhang, Y. Zhang, and C.‐W. Lin, “A Cross Transformer for Image Denoising,” Information Fusion 102 (2024): 102043.
W. Wu, S. Liu, Y. Xia, and Y. Zhang, “Dual Residual Attention Network for Image Denoising,” Pattern Recognition 149 (2024): 110291.
J. Hu, L. Shen, and G. Sun, “Squeeze‐and‐Excitation Networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (IEEE, 2018), 7132–7141.
S. Woo, J. Park, J.‐Y. Lee, and I.‐S. J. A. Kweon, “CBAM: Convolutional Block Attention Module” (2018).
Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “ECA‐Net: Efficient Channel Attention for Deep Convolutional Neural Networks,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2020), 11531–11539.
F. P. Nugraha and Q. Shao, “Machine Learning‐Based Predictive Modeling for Designing Transmon Superconducting Qubits,” in 2023 IEEE International Conference on Quantum Computing and Engineering (QCE). (IEEE, 2023), 1360–1368.
U. Sara, M. Akter, and M. S. J. J. o. C. Uddin, “Image Quality Assessment Through FSIM, SSIM, MSE and PSNR—A Comparative Study,” Communications Journal of Computer and Communications 7 (2019): 8–18.
W. Zhou, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing 13 (2004): 600–612.
C. S. Kidwell, J. R. Alger, and J. L. Saver, “Beyond Mismatch,” Stroke 34 (2003): 2729–2735.
J. Liang, J. Cao, G. Sun, K. Zhang, L. V. Gool, and R. Timofte, “SwinIR: Image Restoration Using Swin Transformer,” (2021), 1833–1844.
J. Gurrola‐Ramos, O. Dalmau, and T. E. Alarcón,“A Residual Dense U‐Net Neural Network for Image Denoising,” IEEE Access 9 (2021): 31742–31754.
S. W. Zamir, A. Arora, S. Khan, et al., Learning Enriched Features for Real Image Restoration and Enhancement (Springer International Publishing, 2020), 492–511.
C. Tian, M. Zheng, C. W. Lin, Z. Li, and D. Zhang, “Heterogeneous Window Transformer for Image Denoising,” IEEE Transactions on Systems, Man, and Cybernetics: Systems 54 (2024): 6621–6632.
I. Saytashev, R. Glenn, G. A. Murashova, et al., “Multiphoton Excited Hemoglobin Fluorescence and Third Harmonic Generation for Non‐Invasive Microscopy of Stored Blood,” Biomedical Optics Express 7 (2016): 3449–3460.
S. Wang, X. Liu, Y. Li, et al., “A Deep Learning‐Based Stripe Self‐Correction Method for Stitched Microscopic Images,” Nature Communications 14 (2023): 5393.
W. Wang, J. Wen, Y. Sheng, et al., “Shot‐Noise Limited Nonlinear Optical Imaging Excited With GHz Femtosecond Pulses and Denoised by Deep‐Learning,” Journal of Biophotonics 17 (2024): e202400186.
A. Zhou, S. A. Mihelic, S. A. Engelmann, A. Tomar, A. K. Dunn, and V. M. Narasimhan, “A Deep Learning Approach for Improving Two‐Photon Vascular Imaging Speeds,” Bioengineering 11 (2024): 111.
Grant Information: 2023BM07 the Anhui Provincial Engineering Technology Research Center for Biomedical Instrument; 2024AH051341 the Natural Science Foundation of the Higher Education Institutions of Anhui Province; KJQN202501117 the Science and Technology Research Program of Chongqing Municipal Education Commission; KJZD-K202301105 the Science and Technology Research Program of Chongqing Municipal Education Commission
Contributed Indexing: Keywords: collaborative network; image denoising; multiphoton imaging; traumatic penumbra
Entry Date(s): Date Created: 20260602 Date Completed: 20260607 Latest Revision: 20260607
Update Code: 20260607
DOI: 10.1002/jbio.70303
PMID: 42227888
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1864-0648
DOI:10.1002/jbio.70303