Baichuan Jiang, Ernest Graham, Mathias Unberath, Russell H Taylor, Raymond C Koehler, Jeeun Kang, Emad M Boctor
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Therefore, it is highly desirable to effectively detect brain hypoxia during labor and postnatally for HIE management.</p><p><strong>Aim: </strong>We recently validated the feasibility of transcranial photoacoustic (PA) imaging for oxyhemoglobin saturation measurement at the superior sagittal sinus ( <math> <mrow> <msub><mrow><mi>O</mi></mrow> <mrow><mn>2</mn></mrow> </msub> <msub><mrow><mi>Sat</mi></mrow> <mrow><mi>ss</mi></mrow> </msub> </mrow> </math> ) in the neonatal piglet brain, at which overall oxygen supply status can be reflected as a primary collective vein. We aim to automate the PA-based workflow of at-risk subject detection and enable fully autonomous and continuous perinatal monitoring.</p><p><strong>Approach: </strong>We proposed a two-step algorithm that focuses on the most informative region of the brain for oxygenation status, the superior sagittal sinus (SSS). First, a convolutional neural network (U-Net) is trained to detect the location of SSS in the coronal cross-section PA images. Then, an optimized region of interest patch around the predicted SSS location is cropped from the spectral unmixed image and averaged as the <math> <mrow> <msub><mrow><mi>O</mi></mrow> <mrow><mn>2</mn></mrow> </msub> <msub><mrow><mi>Sat</mi></mrow> <mrow><mi>ss</mi></mrow> </msub> </mrow> </math> measurement. A confidence score can be computed for the measurement via Monte Carlo dropout (MCD), which infers the prediction uncertainty for better clinical decision-making.</p><p><strong>Results: </strong>The algorithm was evaluated on an <i>in vivo</i> piglet brain imaging dataset containing 84 independent experimental settings from 10 piglet subjects. A 10-fold leave-one-subject-out cross-validation experiment reports 85.2% sensitivity and 93.3% specificity for healthy/hypoxia classification with an <math><mrow><mi>R</mi></mrow> </math> -squared value of 0.708 and a confidence score of 94.06% based on MCD computation, well agreed with our ground-truth given by blood gas measurements.</p><p><strong>Conclusions: </strong>The proposed automatic <math> <mrow> <msub><mrow><mi>O</mi></mrow> <mrow><mn>2</mn></mrow> </msub> <msub><mrow><mi>Sat</mi></mrow> <mrow><mi>ss</mi></mrow> </msub> </mrow> </math> monitoring solution demonstrated a hypoxia detection capability comparable to the human expert manual annotation on the same task. We concluded with high feasibility for a noninvasive PA-based continuous monitoring of the perinatal brain.</p>","PeriodicalId":15264,"journal":{"name":"Journal of Biomedical Optics","volume":"30 7","pages":"076004"},"PeriodicalIF":3.0000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245645/pdf/","citationCount":"0","resultStr":"{\"title\":\"Automatic photoacoustic monitoring of perinatal brain hypoxia with superior sagittal sinus detection.\",\"authors\":\"Baichuan Jiang, Ernest Graham, Mathias Unberath, Russell H Taylor, Raymond C Koehler, Jeeun Kang, Emad M Boctor\",\"doi\":\"10.1117/1.JBO.30.7.076004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Significance: </strong>Despite advances in perinatal medicine over decades, perinatal hypoxic-ischemic encephalopathy (HIE) remains a significant cause of fetal cerebral palsy and can lead to other severe medical sequelae or death. Therefore, it is highly desirable to effectively detect brain hypoxia during labor and postnatally for HIE management.</p><p><strong>Aim: </strong>We recently validated the feasibility of transcranial photoacoustic (PA) imaging for oxyhemoglobin saturation measurement at the superior sagittal sinus ( <math> <mrow> <msub><mrow><mi>O</mi></mrow> <mrow><mn>2</mn></mrow> </msub> <msub><mrow><mi>Sat</mi></mrow> <mrow><mi>ss</mi></mrow> </msub> </mrow> </math> ) in the neonatal piglet brain, at which overall oxygen supply status can be reflected as a primary collective vein. We aim to automate the PA-based workflow of at-risk subject detection and enable fully autonomous and continuous perinatal monitoring.</p><p><strong>Approach: </strong>We proposed a two-step algorithm that focuses on the most informative region of the brain for oxygenation status, the superior sagittal sinus (SSS). First, a convolutional neural network (U-Net) is trained to detect the location of SSS in the coronal cross-section PA images. Then, an optimized region of interest patch around the predicted SSS location is cropped from the spectral unmixed image and averaged as the <math> <mrow> <msub><mrow><mi>O</mi></mrow> <mrow><mn>2</mn></mrow> </msub> <msub><mrow><mi>Sat</mi></mrow> <mrow><mi>ss</mi></mrow> </msub> </mrow> </math> measurement. A confidence score can be computed for the measurement via Monte Carlo dropout (MCD), which infers the prediction uncertainty for better clinical decision-making.</p><p><strong>Results: </strong>The algorithm was evaluated on an <i>in vivo</i> piglet brain imaging dataset containing 84 independent experimental settings from 10 piglet subjects. A 10-fold leave-one-subject-out cross-validation experiment reports 85.2% sensitivity and 93.3% specificity for healthy/hypoxia classification with an <math><mrow><mi>R</mi></mrow> </math> -squared value of 0.708 and a confidence score of 94.06% based on MCD computation, well agreed with our ground-truth given by blood gas measurements.</p><p><strong>Conclusions: </strong>The proposed automatic <math> <mrow> <msub><mrow><mi>O</mi></mrow> <mrow><mn>2</mn></mrow> </msub> <msub><mrow><mi>Sat</mi></mrow> <mrow><mi>ss</mi></mrow> </msub> </mrow> </math> monitoring solution demonstrated a hypoxia detection capability comparable to the human expert manual annotation on the same task. 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引用次数: 0
摘要
意义:尽管近几十年来围产期医学取得了进步,但围产期缺氧缺血性脑病(HIE)仍然是导致胎儿脑瘫的重要原因,并可导致其他严重的医学后遗症或死亡。因此,在分娩和产后有效检测脑缺氧对HIE的管理是非常必要的。目的:我们最近验证了经颅光声成像(PA)测量新生儿仔猪脑上矢状窦(o2satss)血红蛋白饱和度的可行性,该位置的整体供氧状态可以作为主要集体静脉反映出来。我们的目标是自动化基于pa的高危受试者检测工作流程,并实现完全自主和连续的围产期监测。方法:我们提出了一种两步算法,重点关注大脑中氧合状态信息最多的区域,即上矢状窦(SSS)。首先,训练卷积神经网络(U-Net)在冠状截面PA图像中检测SSS的位置。然后,从未混合的光谱图像中裁剪出预测SSS位置周围的优化感兴趣区域,并将其平均为o2 Sat ss测量值。通过蒙特卡罗dropout (MCD)可以计算出测量的置信度分数,从而推断出预测的不确定性,从而更好地进行临床决策。结果:该算法在包含来自10只仔猪的84个独立实验设置的体内仔猪脑成像数据集上进行了评估。一项10倍留一受试者的交叉验证实验报告了健康/缺氧分类的敏感性为85.2%,特异性为93.3%,基于MCD计算的R平方值为0.708,置信度评分为94.06%,与血气测量给出的基本事实完全一致。结论:提出的自动o2sat监测解决方案在相同任务上显示出与人类专家手动注释相当的缺氧检测能力。我们的结论是,基于pa的无创围产期大脑连续监测具有很高的可行性。
Automatic photoacoustic monitoring of perinatal brain hypoxia with superior sagittal sinus detection.
Significance: Despite advances in perinatal medicine over decades, perinatal hypoxic-ischemic encephalopathy (HIE) remains a significant cause of fetal cerebral palsy and can lead to other severe medical sequelae or death. Therefore, it is highly desirable to effectively detect brain hypoxia during labor and postnatally for HIE management.
Aim: We recently validated the feasibility of transcranial photoacoustic (PA) imaging for oxyhemoglobin saturation measurement at the superior sagittal sinus ( ) in the neonatal piglet brain, at which overall oxygen supply status can be reflected as a primary collective vein. We aim to automate the PA-based workflow of at-risk subject detection and enable fully autonomous and continuous perinatal monitoring.
Approach: We proposed a two-step algorithm that focuses on the most informative region of the brain for oxygenation status, the superior sagittal sinus (SSS). First, a convolutional neural network (U-Net) is trained to detect the location of SSS in the coronal cross-section PA images. Then, an optimized region of interest patch around the predicted SSS location is cropped from the spectral unmixed image and averaged as the measurement. A confidence score can be computed for the measurement via Monte Carlo dropout (MCD), which infers the prediction uncertainty for better clinical decision-making.
Results: The algorithm was evaluated on an in vivo piglet brain imaging dataset containing 84 independent experimental settings from 10 piglet subjects. A 10-fold leave-one-subject-out cross-validation experiment reports 85.2% sensitivity and 93.3% specificity for healthy/hypoxia classification with an -squared value of 0.708 and a confidence score of 94.06% based on MCD computation, well agreed with our ground-truth given by blood gas measurements.
Conclusions: The proposed automatic monitoring solution demonstrated a hypoxia detection capability comparable to the human expert manual annotation on the same task. We concluded with high feasibility for a noninvasive PA-based continuous monitoring of the perinatal brain.
期刊介绍:
The Journal of Biomedical Optics publishes peer-reviewed papers on the use of modern optical technology for improved health care and biomedical research.