{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T15:09:34Z","timestamp":1784905774753,"version":"3.55.0"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Allen Discovery Center program","award":["NS108484"],"award-info":[{"award-number":["NS108484"]}]},{"name":"Allen Discovery Center program","award":["3T32EB007507"],"award-info":[{"award-number":["3T32EB007507"]}]},{"name":"Allen Discovery Center program","award":["5T32LM012414"],"award-info":[{"award-number":["5T32LM012414"]}]},{"name":"National Institutes of Health","award":["NS108484"],"award-info":[{"award-number":["NS108484"]}]},{"name":"National Institutes of Health","award":["3T32EB007507"],"award-info":[{"award-number":["3T32EB007507"]}]},{"name":"National Institutes of Health","award":["5T32LM012414"],"award-info":[{"award-number":["5T32LM012414"]}]},{"name":"director\u2019s discretionary fund at the Texas Advanced Computing Cluster","award":["NS108484"],"award-info":[{"award-number":["NS108484"]}]},{"name":"director\u2019s discretionary fund at the Texas Advanced Computing Cluster","award":["3T32EB007507"],"award-info":[{"award-number":["3T32EB007507"]}]},{"name":"director\u2019s discretionary fund at the Texas Advanced Computing Cluster","award":["5T32LM012414"],"award-info":[{"award-number":["5T32LM012414"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based (PSSR Res-U-Net architecture) algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acquiring low- and high-resolution training pairs without compromising vectorization outcomes, and thus open the possibility of utilizing such approaches for other MPM tasks where collecting training data is challenging. We applied our approach to images with large fields of view from a mouse model and show that our method generalizes across imaging depths, disease states and other differences in neurovasculature. Our pretrained models and lightweight architecture can be used to reduce MPM imaging time by up to fourfold without any changes in underlying hardware, thereby enabling deployability across a range of settings.<\/jats:p>","DOI":"10.3390\/bioengineering11020111","type":"journal-article","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T07:42:16Z","timestamp":1706082136000},"page":"111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Deep Learning Approach for Improving Two-Photon Vascular Imaging Speeds"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0479-2941","authenticated-orcid":false,"given":"Annie","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keeton C0800, Austin, TX 78712, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel A.","family":"Mihelic","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keeton C0800, Austin, TX 78712, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaun A.","family":"Engelmann","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keeton C0800, Austin, TX 78712, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alankrit","family":"Tomar","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keeton C0800, Austin, TX 78712, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6500-5783","authenticated-orcid":false,"given":"Andrew K.","family":"Dunn","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keeton C0800, Austin, TX 78712, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vagheesh M.","family":"Narasimhan","sequence":"additional","affiliation":[{"name":"Department of Integrative Biology, The University of Texas at Austin, 2415 Speedway C0930, Austin, TX 78712, USA"},{"name":"Department of Statistics and Data Sciences, The University of Texas at Austin, 105 E. 24th St D9800, Austin, TX 78712, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1111\/j.1748-1716.2011.02256.x","article-title":"The neurovascular unit in brain function and disease","volume":"203","author":"Lecrux","year":"2011","journal-title":"Acta Physiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1152\/japplphysiol.00966.2005","article-title":"Neurovascular coupling in the normal brain and in hypertension, stroke, and Alzheimer disease","volume":"100","author":"Girouard","year":"2006","journal-title":"J. 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