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用于测量搏动性血流、临界关闭压和颅内高压的改良比尔-朗伯算法。

Modified Beer-Lambert algorithm to measure pulsatile blood flow, critical closing pressure, and intracranial hypertension.

作者信息

Baker Wesley B, Forti Rodrigo M, Heye Pascal, Heye Kristina, Lynch Jennifer M, Yodh Arjun G, Licht Daniel J, White Brian R, Hwang Misun, Ko Tiffany S, Kilbaugh Todd J

机构信息

Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Division of General, Thoracic and Fetal Surgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.

出版信息

Biomed Opt Express. 2024 Aug 27;15(9):5511-5532. doi: 10.1364/BOE.529150. eCollection 2024 Sep 1.

DOI:10.1364/BOE.529150
PMID:39296411
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11407241/
Abstract

We introduce a frequency-domain modified Beer-Lambert algorithm for diffuse correlation spectroscopy to non-invasively measure flow pulsatility and thus critical closing pressure (CrCP). Using the same optical measurements, CrCP was obtained with the new algorithm and with traditional nonlinear diffusion fitting. Results were compared to invasive determination of intracranial pressure (ICP) in piglets (n = 18). The new algorithm better predicted ICP elevations; the area under curve (AUC) from logistic regression analysis was 0.85 for ICP ≥ 20 mmHg. The corresponding AUC for traditional analysis was 0.60. Improved diagnostic performance likely results from better filtering of extra-cerebral tissue contamination and measurement noise.

摘要

我们引入了一种用于扩散相关光谱学的频域修正比尔-朗伯算法，以无创测量血流搏动性，从而测量临界关闭压（CrCP）。使用相同的光学测量方法，通过新算法和传统非线性扩散拟合获得了CrCP。将结果与仔猪（n = 18）颅内压（ICP）的有创测定结果进行了比较。新算法能更好地预测ICP升高；对于ICP≥20 mmHg，逻辑回归分析的曲线下面积（AUC）为0.85。传统分析的相应AUC为0.60。诊断性能的提高可能源于对脑外组织污染和测量噪声的更好过滤。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/94fbf76433a9/boe-15-9-5511-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/3bc13dfffe6c/boe-15-9-5511-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/d6eb96fdbd6f/boe-15-9-5511-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/bf9e4343cda4/boe-15-9-5511-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/21033ec9cdad/boe-15-9-5511-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/b4799fe01bbf/boe-15-9-5511-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/94fbf76433a9/boe-15-9-5511-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/3bc13dfffe6c/boe-15-9-5511-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/d6eb96fdbd6f/boe-15-9-5511-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/bf9e4343cda4/boe-15-9-5511-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/21033ec9cdad/boe-15-9-5511-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/b4799fe01bbf/boe-15-9-5511-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb8/11407241/94fbf76433a9/boe-15-9-5511-g006.jpg

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