AbstractSpectral imaging has the potential to become a key technique in interventional medicine as it unveils much richer optical information compared to conventional RBG (red, green, and blue)-based imaging. Thus allowing for high-resolution functional tissue analysis in real time. Its higher information density particularly shows promise for the development of powerful perfusion monitoring methods for clinical use.

摘要光谱成像有可能成为介入医学的关键技术，因为与传统的基于RBG（红色，绿色和蓝色）的成像相比，它揭示了更丰富的光学信息。因此可以实时进行高分辨率功能组织分析。它更高的信息密度特别显示出开发用于临床的强大灌注监测方法的前景。

However, even though in vivo validation of such methods is crucial for their clinical translation, the biomedical field suffers from a lack of publicly available datasets for this purpose. Closing this gap, we generated the SPECTRAL Perfusion Arm Clamping dAtaset (SPECTRALPACA). It comprises ten spectral videos (∼20 Hz, approx.

然而，尽管这些方法的体内验证对于其临床翻译至关重要，但生物医学领域缺乏为此目的公开可用的数据集。缩小这一差距，我们生成了光谱灌注臂钳制数据集（SPECTRALPACA）。它包括十个光谱视频（〜20 Hz，约。

20,000 frames each) systematically recorded of the hands of ten healthy human participants in different functional states. We paired each spectral video with concisely tracked regions of interest, and corresponding diffuse reflectance measurements recorded with a spectrometer. Providing the first openly accessible in human spectral video dataset for perfusion monitoring, our work facilitates the development and validation of new functional imaging methods..

每个20000帧）系统地记录了十名处于不同功能状态的健康人类参与者的手。我们将每个光谱视频与简要跟踪的感兴趣区域配对，并用光谱仪记录相应的漫反射测量值。我们的工作为灌注监测提供了第一个可公开访问的人类光谱视频数据集，有助于开发和验证新的功能成像方法。。

Background & SummaryThe enhanced perception of tissues and structures that are invisible to the naked eye, conventionally by RGB (red, green, and blue)-based imaging, has become indispensable for various types of interventional clinical procedures. As these imaging methods mimic the function of the human eye by collecting light in three broad spectral regions (red, green, and blue), they inherently limit practitioners to the morphological analysis of tissue.

背景与总结通常通过基于RGB（红色，绿色和蓝色）的成像增强对肉眼看不见的组织和结构的感知，已成为各种类型的介入临床程序必不可少的。由于这些成像方法通过在三个宽光谱区域（红色，绿色和蓝色）收集光线来模拟人眼的功能，因此它们固有地限制了从业者对组织的形态分析。

However, the ability to functionally assess tissue in real time is crucial in a number of medical interventions, such as partial kidney resection (nephrectomy)1, anastomosis2,3, and wound monitoring4,5.Addressing this clinical need, spectral imaging (SI)2 techniques have evolved as a promising alternative.

然而，实时功能评估组织的能力在许多医学干预中至关重要，例如部分肾切除术（肾切除术）1，吻合术2,3和伤口监测4,5。为了满足这种临床需求，光谱成像（SI）2技术已经发展成为一种有前途的替代方案。

In contrast to RGB imaging, SI is capable of collecting light in many more and narrower regions of the optical spectrum. This property, in combination with tissue-specific spectral signatures, caused by the unique interaction of different molecules with light, enables SI to encode optical information of much higher density than traditional imaging techniques, ultimately allowing for the development of methods that offer functional tissue assessment in real time.

与RGB成像相反，SI能够在光谱的更多和更窄的区域收集光。这种特性与由不同分子与光的独特相互作用引起的组织特异性光谱特征相结合，使SI能够编码比传统成像技术高得多的密度的光学信息，最终允许开发实时提供功能组织评估的方法。

Over the last decade, many approaches to functional tissue monitoring in vivo, notably of perfusion and oxygenation, have emerged, using both gray-scale and spectral imaging. These approaches can be classified into two main categories: a) model-based approaches2,4,5,6,7,8,9,10, and b) approaches that leverage machine learning-based tools1,11,12,13,14.

在过去的十年中，已经出现了许多使用灰度和光谱成像进行体内功能组织监测的方法，特别是灌注和氧合。这些方法可以分为两大类：a）基于模型的方法2,4,5,6,7,8,9,10，以及b）利用基于机器学习的工具的方法1,11,12,13,14。

For the remainder of the manuscript, we will use the term perfusion to refer to properties related to the assessment of tissue perfusion such as oxygenation and blood volume fraction.The majority of these met.

对于手稿的其余部分，我们将使用术语灌注来指代与组织灌注评估相关的特性，例如氧合和血容量分数。其中大多数人都遇到了。

(1)

(1)

Subsequently, an \({\ell }_{1}\)-normalization across the different bands was performed to compensate for the influence of light source intensity changes due to variation in the distance of the camera to the surface of the hand:$${({\widehat{I}}_{(i,j)})}_{k}=\frac{{({\bar{I}}_{(i,j)})}_{k}}{{\parallel {\bar{I}}_{(i,j)}\parallel }_{1}}\quad ;\quad {\parallel {\bar{I}}_{(i,j)}\parallel }_{1}=\mathop{\sum }\limits_{k=1}^{B}{({\bar{I}}_{(i,j)})}_{k}$$.

随后，对不同波段进行了\（{\ ell}\{1}\）归一化，以补偿由于相机到手表面的距离变化而引起的光源强度变化的影响：$${（{\ widehat{I}}{（I，j）}}{k}=\ frac{（{\ bar{I}}{（I，j）}}}{k}{\ parallel{\ bar{I}}{（I，j）}\并行}{1}}\四元；\quad{\ parallel{\ bar{I}}{（I，j）}\ parallel}{1}=\ mathop{\ sum}\ limits\u{k=1}^{B}{（{\ bar{I}}{（I，j）}}}{k}$$。

(2)

(2)

The resulting spectra \({({\widehat{I}}_{(i,j)})}_{k}\) can be compared between different image sequences.As part of the validation of the spectral camera, the diffuse reflectance data measured with the spectrometer \(r(\lambda )\) were transformed into the spectral camera space, thus yielding the measurement \({r}_{k}\) at band k according to:$${r}_{k}=\frac{{\int }_{{\lambda }_{min}}^{{\lambda }_{max}}{\mathcal{J}}(\lambda )\cdot J(\lambda )\cdot {f}_{k}(\lambda )\cdot r(\lambda )d\lambda }{{\int }_{{\lambda }_{min}}^{{\lambda }_{max}}{\mathcal{J}}(\lambda )\cdot J(\lambda )\cdot {f}_{k}(\lambda )d\lambda },$$.

可以在不同的图像序列之间比较得到的光谱\（{（{\ widehat{I}}}{（I，j）}）}{k}\）。作为光谱相机验证的一部分，用光谱仪（r（\ lambda））测量的漫反射数据被转换到光谱相机空间，从而产生测量结果\({r}_{k} \）在k波段，根据：$${r}_{k} =\frac{{\int}\u{{\ lambda}\u{min}}}^{\lambda}\u{max}}{\mathcal{J}（\lambda）\cdot J（\lambda）\cdot{f}_{k} （\lambda）\cdot r（\lambda）d\lambda}{{\int}\u{{\ lambda}\u{min}}^{{\lambda}\u{max}}{\mathcal{J}（\lambda）\cdot J（\lambda）\cdot{f}_{k} （\λ）d \λ}，$$。

(3)

(3)

were \([{\lambda }_{min},{\lambda }_{max}]\) is the spectral range of each band k of the spectral camera, \(I(\lambda )\) represents the optical transmission profile of the optical components of our hardware setup (Fig. 2), \({\mathcal{J}}(\lambda )\) is the relative irradiance of the light source, and \({f}_{k}(\lambda )\) characterizes the kth optical filter response of the camera.Data RecordsThe SPECTRALPACA27 dataset can be downloaded from Synapse (SynID syn51625685) following the instructions provided in this link.

是光谱相机每个波段k的光谱范围，\（I（\ lambda）\）表示我们硬件设置的光学组件的光学传输曲线（图2），\（{\ mathcal{J}}（\ lambda）\）是光源的相对辐照度，并且\({f}_{k} （\ lambda）\）表征相机的第k个光学滤波器响应。数据记录SPECTRALPACA27数据集可以按照此链接中提供的说明从Synapse（SynID syn51625685）下载。

The dataset is mainly composed of spectral videos and spectrometer diffuse reflectance data, and is organized in two levels. The first level refers to the two types of data included in the archive: spectral videos and spectrometer measurements. The spectral videos are provided as individual files for all ten subjects, which can be loaded with the sample Python code provided together with the dataset.

数据集主要由光谱视频和光谱仪漫反射数据组成，分为两个层次。第一级是指档案中包含的两种类型的数据：光谱视频和光谱仪测量。光谱视频作为所有十个主题的单独文件提供，可以与数据集一起提供的示例Python代码一起加载。

Each spectral video is named subject_NN.b2nd, where NN represents the subject ID (see Table 1), and is stored in a lossless, highly optimized compressed format developed by BLOSC (NDArray). This facilitates fast data decompression, and loading of individual frames from each video. The spectral videos are accompanied by dark and white reference measurements that can be used to calibrate the spectral videos.

每个光谱视频都被命名为subject\u NN.b2nd，其中NN代表主题ID（见表1），并以BLOSC（NDArray）开发的无损、高度优化的压缩格式存储。这有助于快速解压缩数据，并从每个视频中加载单个帧。光谱视频伴随着黑白参考测量，可用于校准光谱视频。

In addition, the filter responses of the spectral camera and the relative irradiance of the light source used to record the spectral videos is provided as comma-separated values (CSV) and in Feather format for faster data loading. The spectrometer measurements for all subjects are contained in a single file, provided in both CSV and Feather format.Table 1 Human subjects recruited for our clinical study.Full size tableSimilarly to the spectr.

此外，光谱相机的滤波器响应和用于记录光谱视频的光源的相对辐照度以逗号分隔值（CSV）和羽毛格式提供，以更快地加载数据。所有受试者的光谱仪测量都包含在一个文件中，以CSV和Feather格式提供。表1招募用于我们临床研究的人类受试者。与光谱相似的全尺寸表。

Code availability

代码可用性

The SPECTRALPACA27 dataset includes the Python code that facilitates loading of the spectral data. This code and its usage instructions can be found in the dataset archive in the folder named code.

SPECTRALPACA27数据集包含有助于加载光谱数据的Python代码。此代码及其使用说明可以在名为code的文件夹中的dataset archive中找到。

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Download referencesAcknowledgementsThis project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (NEURAL SPICING, Grant agreement No. 101002198). This project also received support from the Helmholtz Association under the joint research school “HIDSS4Health – Helmholtz Information and Data Science School for Health”.

下载参考文献致谢该项目获得了欧盟地平线2020研究与创新计划（NEURAL SPICING，Grant agreement No.101002198）下欧洲研究理事会（ERC）的资助。该项目还得到了联合研究学校“HIDSS4Health–亥姆霍兹信息和数据科学健康学校”下亥姆霍兹协会的支持。

This work was also supported by the “Model-Based AI” project, which is funded by the Carl Zeiss Foundation. Some icons shown in the figures were provided by Freepik and DinosoftLabs from Flaticon.com.FundingOpen Access funding enabled and organized by Projekt DEAL.Author informationAuthors and AffiliationsGerman Cancer Research Center (DKFZ), Division of Intelligent Medical Systems, Heidelberg, GermanyLeonardo Ayala, Diana Mindroc-Filimon, Maike Rees, Marco Hübner, Jan Sellner, Silvia Seidlitz, Minu Tizabi, Sebastian Wirkert, Alexander Seitel & Lena Maier-HeinMedical Faculty, Heidelberg University, Heidelberg, GermanyLeonardo Ayala & Lena Maier-HeinFaculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, GermanyMaike Rees, Marco Hübner, Jan Sellner, Silvia Seidlitz & Lena Maier-HeinHelmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, GermanyJan Sellner, Silvia Seidlitz & Lena Maier-HeinNational Center for Tumor Diseases (NCT) Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, GermanyJan Sellner, Silvia Seidlitz & Lena Maier-HeinAuthorsLeonardo AyalaView author publicationsYou can also search for this author in.

这项工作也得到了卡尔·蔡司基金会资助的“基于模型的人工智能”项目的支持。图中所示的一些图标是由Freepik和DinosoftLabs从Flaticon.com.FundingOpen-Access-funding提供的，由Projekt DEAL启用和组织。作者信息作者和附属机构德国癌症研究中心（DKFZ），智能医疗系统部，海德堡，德国莱昂纳多·阿亚拉，戴安娜·明德罗·菲利蒙，梅克·里斯，马尔科·赫布纳，扬·塞尔纳，西尔维亚·塞德利兹，米努蒂扎比，塞巴斯蒂安·维尔克特，亚历山大·塞特尔和莱娜·梅耶·海因德医学院，海德堡大学，海德堡，德国莱昂纳多·阿亚拉和莱娜·梅耶·海因德数学和计算机科学学院，海德堡，德国梅克·里斯，马尔科·赫布纳，扬·塞尔纳，西尔维亚·塞德勒伊茨和莱娜·迈尔·海因霍尔茨健康信息与数据科学学院，卡尔斯鲁厄/海德堡，德国扬·塞尔纳，西尔维亚·塞德利茨和莱娜·迈尔·海因霍尔茨国家肿瘤疾病中心（NCT）海德堡，DKFZ与海德堡大学医院合作，海德堡，德国扬·塞尔纳，西尔维亚·塞德利茨和莱娜·迈尔·海因霍尔茨作者莱昂纳多·阿亚拉维作者出版物你也可以在中搜索这位作者。

PubMed Google ScholarDiana Mindroc-FilimonView author publicationsYou can also search for this author in

PubMed Google Scholardina Mindroc FilimonView作者出版物您也可以在

PubMed Google ScholarMaike ReesView author publicationsYou can also search for this author in

PubMed谷歌学术类ReesView作者出版物您也可以在

PubMed Google ScholarMarco HübnerView author publicationsYou can also search for this author in

PubMed Google ScholarMarco HübnerView作者出版物您也可以在

PubMed Google ScholarJan SellnerView author publicationsYou can also search for this author in

PubMed Google ScholarJan SellnerView作者出版物您也可以在

PubMed Google ScholarSilvia SeidlitzView author publicationsYou can also search for this author in

PubMed Google ScholarSilvia SeidlitzView作者出版物您也可以在

PubMed Google ScholarMinu TizabiView author publicationsYou can also search for this author in

PubMed Google Scholarmarinu TizabiView作者出版物您也可以在

PubMed Google ScholarSebastian WirkertView author publicationsYou can also search for this author in

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PubMed Google ScholarLena Maier HeinView作者出版物您也可以在

PubMed Google ScholarContributionsL.A., D.M., M.T., A.S. and L.M. conceptualized and designed the clinical trial. L.A. D.M., M.H. and M.T. recorded the data. S.W. and L.M. developed the regression algorithm based on random forests. L.A., M.R., and L.M. interpreted the results of the project.

PubMed谷歌学术贡献l。A、 ，D.M.，M.T.，A.S.和L.M.概念化并设计了临床试验。五十、 A.D.M.，M.H.和M.T.记录了数据。S、 W.和L.M.开发了基于随机森林的回归算法。五十、 A.，M.R。和L.M.解释了项目的结果。

L.A., D.M., and M.R. performed the data analysis. J.S., and S.S provided guidance on the compression and organization of the dataset. L.A., M.R., L.M., S.S., and J.S. designed the figures. All authors contributed to writing and revising the manuscript.Corresponding authorCorrespondence to.

五十、 A.，D.M。和M.R.进行了数据分析。J、 S.和S.S为数据集的压缩和组织提供了指导。五十、 A.，M.R.，L.M.，S.S。和J.S.设计了这些数字。所有作者都为撰写和修改手稿做出了贡献。对应作者对应。

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Reprints and permissionsAbout this articleCite this articleAyala, L., Mindroc-Filimon, D., Rees, M. et al. The SPECTRAL Perfusion Arm Clamping dAtaset (SPECTRALPACA) for video-rate functional imaging of the skin.

转载和许可本文引用本文Ayala，L.，Mindroc-Filimon，D.，Rees，M。等人。用于皮肤视频功能成像的光谱灌注臂钳制数据集（SPECTRALPACA）。

Sci Data 11, 536 (2024). https://doi.org/10.1038/s41597-024-03307-yDownload citationReceived: 20 June 2023Accepted: 24 April 2024Published: 25 May 2024DOI: https://doi.org/10.1038/s41597-024-03307-yShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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