Tag: Hospital Germans Trias i Pujol

  • CPAP STUDY – DATABASE

    CPAP STUDY – DATABASE

    The aim of CPAP study is to test the effect of applying positive airway pressure (PAP) during CT acquisition to improve segmentation, particularly at end-expiration.

    CPAP DataBase is a register built for this study and records data on CT acquisitions in inspiration and expiration with 4 PAP protocols. Were recorded prospectively and compared to baseline inspiratory acquisitions in 20 patients. The 4 protocols explored differences between devices (flow vs. turbine), exposures (within seconds vs. 15-min) and pressure levels (10 vs. 14 cmH2O).

    CT scans were acquired with a 320-detector row, 0.5 mm slice thickness, at intervals of 0.4 mm, 80 × 0.5 mm collimator, tube voltage of 100 kVp, and tube current adapted for sex and body mass index.

    CITATION

    If you want to use this dataset in your research, please cite this database as:

    Diez-Ferrer M, Gil D, Carreñ̃o E, et al. Positive airway pressure-enhanced CT to improve virtual bronchoscopic navigation. In: AABIP-CHEST; 2016

    CLINICAL PARTNERS

    Hospital Germans Trias i Pujol, Hospital de Bellvitge

    FUNDING

    Fundació La Marató de TV3. Spanish projects RTI2018- 095209 -B-C21 and CERCA-Programme


    DOWNLOAD

    Database Version 3, dated December 7, 2023, comprises a total of 96 pulmonary nodules. It includes all the nodules present in the previous versions (Version 1 and 2), thereby incorporating the data from the earlier database and including the diagnosis and scan’s acquisition parameters. It can be downloaded below.

    Click here to dowload the database.

  • RADIOLUNG – DATABASE

    RADIOLUNG – DATABASE

    CONTENT

    Radiolung dataset: This dataset can be found in UAB Digital Repository of Documents and can be accessed through CORA (doi: https://doi.org/10.34810/data1972)

    The dataset contains 4 types of files:
    1. NII.GZ files: anonymized 3D volumes Fromm CT Scans.

    2. ACSV files: Text files generated by 3D Slicer, associated to an individual NII.GZ file, which include the coordinates and size of the volume that encompasses the nodule found in the CT.

    3. XLSX files: One spreadsheet with the metadata corresponding to the acquisition parameters of each CT (11112024_AcqParams), and one spreadsheet with the metadata corresponding to each patient, CT scan, and nodule. (11112024_BDMetaData). The description, and possible values, of each column are written in a dictionary in DOCX format

    4. DOCX files: Text file with the description, and possible values, of the metadata for patient, CT scans, and nodules, included in the dataset.

    XLSX and DOCX files are found in the main folder of the dataset.

    The anonymized CT scans, and their corresponding ACSV files, are stored in separate folders, using the patient identifier as the folder name. These folders are stored inside the folder “CT”.

    DESCRIPTION

    This prospective cohort study, initiated in December 2019, aims to evaluate nodules of patients who underwent surgery for pulmonary nodules (PN) in routine practice and as part of Lung Cancer Screening (LCS). To determine the malignancy of the nodules and their histopathological type, a biopsy was performed on each PN.

    Informed consent was obtained from all subjects involved in the study. The study was conducted according to the guidelines of the Declaration of Helsinki-Fortaleza/Brazil, 2013, and approved by the Institutional Review Board of Hospital Universitari Germans Trias i Pujol (protocol code PI-19-169 and date 6 September 2019).

    The CT scans were acquired using standardized parameters, including 120 kV, 100-350 mA (dose modulation range), soft tissue reconstructions, and high-frequency algorithms. For more detailed information, please refer to the article titled “An Intelligent Radiomic Approach for Lung Cancer Screening” (full reference provided above).

    The database contains anonymized CT scans in NifTI format, along with the precise location of the nodules within the scans. These locations were marked by a respiratory medicine physician with seven years of experience using 3D-Slicer software (version 4.11.20210226). The physician utilized this tool to define a Volume of Interest (VOI) that encloses each nodule, resulting in the generation of a corresponding .ACSV file for each PN.

    For instance, let’s take the file R_1.acsv, which was generated using 3D-Slicer and contains a VOI represented by the following lines:

    line 24. # pointColumns = type|x|y|z|sel|vis

    line 25. point|82.3079|-85.7626|102.94|1|1

    line 26. point|11.9305|16.9547|10.8962|1|1

    Line 24 describes the positional items of the subsequent lines. The VOI is represented by a central point (x, y, z), which can be found in Line 25, and a shift in both directions (+/-) for each axis, specified in Line 26. This means that the VOI is delimited by the following two points:

    Point1 = (70.3774, −102.7173, 92.0438)

    Point2 = (94.2384, −68.8079, 113.8362)

    These points define the bounding box that encompasses the PN image. They are given in the world system coordinates relative to the CT scan and need to be mapped to the voxel coordinates for extracting the VOI of the nodule. To achieve this mapping, the affine matrix is employed to transform from the scanner coordinate system to the voxel coordinate system. Once the mapping is performed, the bounding box can be utilized to extract the pulmonary nodule from the CT scan.

    CITATION

    If you use the Radiolung Database in your research, please cite this database as:

    Torres, G.; Baeza, S.; Sanchez, C.; Guasch, I.; Rosell, A.; Gil D. An Intelligent Radiomic Approach for Lung Cancer Screening. Appl. Sci. 2022.

    CLINICAL PARTNERS

    Computer Science Department, Computer Vision Center (CVC), Universitat Autònoma de Barcelona (UAB), Barcelona, España.

    Direcció Clínica de l’Àrea del Tòrax, Germans Trias i Pujol University Hospital, Barcelona, España.

    FUNDING

    This project is supported by the Ministerio de Ciencia e Innovación (MCI), Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER), RTI2018-095209-B-C21 (MCI/AEI/FEDER, UE), Generalitat de Catalunya, 2017-SGR-1624 and CERCA-Programme. Acadèmia de Ciencies Mèdiques i de Salut de Catalunya i Balears, Barcelona Respiratory Network (BRN), Fundació Ramon Pla i Armengol, Talents Grant – Germans Trias and La Pedrera, Lung Ambition Alliance (LAA).

    Ministerio de Economía, Industria y Competitividad, Gobierno de España grant PID 2021-126776OB-C21, AGAUR grant 2021-SGR-01623 and CERCA Programme / Generalitat de Catalunya.


    DOWNLOAD

    Click here to dowload the database.

  • BronchoX

    BronchoX

    Project Description

    Virtual Bronchoscopy (VB) is a non-invasive exploration tool for intervention planning and navigation of possible pulmonary lesions (PLs). A VB software involves the location of a PL and the calculation of a route, starting from the trachea, to reach it. The selection of a VB software might be a complex process, and there is no consensus in the community of medical software developers in which is the best-suited system to use or framework to choose.
    We present BronchoX (Bronchoscopy EXploration), a VB software to plan biopsy interventions that generate physician-readable instructions to reach the pulmonary lesions. Our solution is open-source, multiplatform and extensible for future functionalities, designed by our multidisciplinary research and development group.
    BronchoX contains two main modules, the Planner module and the Intervention Module. The planner allows clinicians to browse the CT data, obtain a 3D reconstruction of the airway tree and plan the best path to reach the lesion. The planner provides different visualization tools that simulates a virtual navegation trough the respiratory tract. Finally, the Intervention module use the planner data to provide simple and clear instructions during operation time.

    Project demo : [link]


    CLINICAL PARTNERS

    Hospital Germans Trias i Pujol, Hospital de Bellvitge

    FUNDING

    Fundació La Marató de TV3, TIN2012-33116, 2014PROD00065, DPI2015-65286-R. The research leading to these results has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 712949 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia.