Spatial lung imaging in clinical and translational settings.
For many severe lung diseases, non-invasive biomarkers from imaging could improve early detection of lung injury or disease onset, establish a diagnosis, or help follow-up disease progression and treatment strategies. Imaging of the thorax and lung is challenging due to its size, respiration movemen...
| Publicado en: | Breathe Vol. 20; no. 3; pp. 1 - 12 |
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| Autores principales: | , , , |
| Formato: | diagnostic images exam questions pictorial review tables/charts Journal Article |
| Publicado: |
European Respiratory Society
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180118176&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180118176 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18106838 85SQ jtl: Breathe issn: 18106838 maglogo: N pubinfo: dt: Oct2024 vid: 20 iid: 3 pid: 76609 pub: European Respiratory Society artinfo: ui: 180118176 180118176 180118176 10.1183/20734735.0224-2023 180118176 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Spatial lung imaging in clinical and translational settings. aug: au: Mahmutovic Persson, Irma Bozovic, Gracijela Westergren-Thorsson, Gunilla Rolandsson Enes, Sara affil: Lund University BioImaging Centre (LBIC), Faculty of Medicine, Lund University, Lund, Sweden sug: subj: Lung Diseases Radiography Diagnostic Imaging Methods Imaging, Three-Dimensional Radiographic Image Enhancement Artificial Intelligence Machine Learning ab: For many severe lung diseases, non-invasive biomarkers from imaging could improve early detection of lung injury or disease onset, establish a diagnosis, or help follow-up disease progression and treatment strategies. Imaging of the thorax and lung is challenging due to its size, respiration movement, transferred cardiac pulsation, vast density range and gravitation sensitivity. However, there is extensive ongoing research in this fast-evolving field. Recent improvements in spatial imaging have allowed us to study the three-dimensional structure of the lung, providing both spatial architecture and transcriptomic information at single-cell resolution. This fast progression, however, comes with several challenges, including significant image file storage and network capacity issues, increased costs, data processing and analysis, the role of artificial intelligence and machine learning, and mechanisms to combine several modalities. In this review, we provide an overview of advances and current issues in the field of spatial lung imaging. pubtype: Academic Journal doctype: diagnostic images exam questions pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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