---
title: "Computational Pipeline for Retinal Capillary Blood Flow Measurement with Adaptive Optics Line Conf"
id: "biorxiv-2-a-computational-pipeline-for-retinal-capillary-blood-flow-measurement-using"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-2-a-computational-pipeline-for-retinal-capillary-blood-flow-measurement-using"
content_type: "clinical_feed_article"
specialty: "Radiology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.21.753337v1?rss=1"
published_at: "2026-09-23T09:50:14.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Computational Pipeline for Retinal Capillary Blood Flow Measurement with Adaptive Optics Line Conf
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-2-a-computational-pipeline-for-retinal-capillary-blood-flow-measurement-using
- **Specialty:** [Radiology](https://medichelpline.com/clinical-feed/radiology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.21.753337v1?rss=1)
- **Published At:** 2026-09-23T09:50:14.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors present a computational pipeline to measure capillary-level retinal blood flow from **adaptive optics line confocal ophthalmoscopy (AOLCO)** videos, delivered as both a 3D Slicer extension and a batch command-line tool sharing a common analysis core. - The pipeline has four sequential stages: **preprocessing** (illumination correction and closed-eye frame detection), **registration** (GPU-based translation initialization, affine and optional B-Spline registration), **postprocessing** (frame differencing to suppress static structures and generate a 2D capillary projection), and **velocity measurement** (manual segment selection, spatiotemporal image extraction, and Radon-transform–based velocimetry). - The registration approach includes a robust, fast GPU translation-registration algorithm designed to handle large eye movements (e.g., microsaccades) that can displace frames by tens to hundreds of pixels. - Validation against synthetic ground-truth flow data showed the velocimetry recovered speed within an error of 4% (as reported in the source). - The GPU translation algorithm is substantially faster than the baseline method, running approximately 3.6× faster at 100 Hz and up to 38× faster at 400 Hz (reported ranges). - Applied to a cohort of 2,344 videos from 57 subjects with acquisition rates from 30 Hz to 400 Hz, the registration improved video frame structural similarity (SSIM) on every acquisition from 0.73 to 0.86. - The recovered blood flow velocities are described as physiologically plausible and of the same order as published adaptive-optics measurements (source statement). - The method produces spatiotemporal images along manually identified capillary segments from the frame-difference video; velocities are derived using the **Radon transform**. - The study is a preprint and has not been peer reviewed; funder information (NIH, 1OT2OD038131) and a declared absence of competing interests are reported in the source. - Specific implementation details, software availability links, parameter settings, and full experimental protocols beyond the summary were not reported in the source abstract and would require consulting the full preprint PDF for complete technical information.
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Syed Muhammad Hamza Shah 1 University of Miami; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Syed%2BMuhammad%2BHamza%2BShah%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Shah%20SM&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ASyed%2BMuhammad%2BHamza%2BShah%2B) Lin Tong 1 University of Miami; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Lin%2BTong%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Tong%20L&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ALin%2BTong%2B) Ruixue Liu 2 University of California, Los Angeles; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Ruixue%2BLiu%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Liu%20R&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ARuixue%2BLiu%2B) Yuhua Zhang 3 University of California- Los Angeles * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yuhua%2BZhang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Zhang%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYuhua%2BZhang%2B) * [ORCID record for Yuhua Zhang](http://orcid.org/0000-0002-1110-453X "Open in new tab") Jianhua Wang 1 University of Miami; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jianhua%2BWang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wang%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJianhua%2BWang%2B) Liang Liang 1 University of Miami; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Liang%2BLiang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Liang%20L&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ALiang%2BLiang%2B) * For correspondence: liang@cs.miami.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.09.21.753337v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5802828/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.09.21.753337v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5802828/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.09.21.753337v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5802828/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.09.21.753337v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5802828/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Background and Objective: High-speed and high-resolution retinal imaging using adaptive optics line confocal ophthalmoscopy (AOLCO) resolves individual red blood cells (erythrocytes) flowing through the smallest retinal capillaries, but quantifying their velocity first requires removing linear and nonlinear distortions caused by eye motion that may result in a displacement of tens to hundreds of pixels between frames. We present a computational pipeline that stabilizes AOLCO capillary video and measures erythrocyte velocity in true physical units, delivered as an interactive 3D Slicer extension and a batch command-line tool sharing one analysis core. Methods: Our video analysis pipeline has four stages: preprocessing, registration, postprocessing, and velocity measurement. During preprocessing, illumination is corrected, and closed-eye frames are identified. During registration, each frame is aligned to a reference frame using affine and, when needed, B-Spline registration, initialized by our robust and fast (GPU-based) translation-registration algorithm to accommodate large eye movements (e.g., microsaccades). During postprocessing, adjacent registered frames are differenced to suppress stationary structures, and a 2D projection of the frame-difference video reveals the capillary network. During velocity measurement, an operator manually identifies capillary segments on the 2D projection image. Spatiotemporal images are then generated along these segments from the frame-difference video, and blood-flow velocity curves are obtained from these images using the Radon transform. Results: We validate the velocimetry of the pipeline against synthetic ground-truth flow data, and the experiments show that the velocimetry recovers the speed within an error of 4% The GPU-based translation registration algorithm aligns the large eye motions (~90 um per frame on average) and runs about 3.6 x (100 Hz) to 38 x (400 Hz) faster than the baseline method. Across the full cohort (2344 videos, 57 subjects, 30 Hz to 400 Hz), our registration approach improves structural similarity (SSIM) of the video frames on every acquisition from 0.73 to 0.86. The recovered blood flow velocities are physiologically plausible, of the same order as published AO measurements. Conclusions: Our computational pipeline enables AOLCO velocimetry on the capillary-level. ### Competing Interest Statement The authors have declared no competing interest. ## Funder Information Declared NIH, 1OT2OD038131 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY 4.0 International license](http://creativecommons.org/licenses/by/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.09.21.753337v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/09/23/2026.09.21.753337.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) A Computational Pipeline for Retinal Capillary Blood Flow Measurement using Adaptive Optics Line Confocal Ophthalmoscopy Syed Muhammad Hamza Shah, Lin Tong, Ruixue Liu, Yuhua Zhang, Jianhua Wang, Liang Liang bioRxiv 2026.09.21.753337; doi: https://doi.org/10.64898/2026.09.21.753337 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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