| |  Online-Ressource |
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| Verfasst von: | Yin, Yi [VerfasserIn]  |
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| | Sedlaczek, Oliver [VerfasserIn] |
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| | Müller, Benedikt [VerfasserIn] |
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| | Warth, Arne [VerfasserIn] |
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| | González-Vallinas Garrachón, Margarita [VerfasserIn] |
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| | Lahrmann, Bernd [VerfasserIn] |
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| | Grabe, Niels [VerfasserIn] |
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| | Kauczor, Hans-Ulrich [VerfasserIn] |
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| | Breuhahn, Kai [VerfasserIn] |
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| | Vignon-Clementel, Irene E. [VerfasserIn] |
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| | Drasdo, Dirk [VerfasserIn] |
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| Titel: | Tumor cell load and heterogeneity estimation from diffusion-weighted mri calibrated with histological data |
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| Titelzusatz: | an example from lung cancer |
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| Verf.angabe: | Yi Yin, Oliver Sedlaczek, Benedikt Müller, Arne Warth, Margarita González-Vallinas, Bernd Lahrmann, Niels Grabe, Hans-Ulrich Kauczor, Kai Breuhahn, Irene E. Vignon-Clementel, et al. |
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| Jahr: | 2018 |
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| Umfang: | 12 S. |
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| Fussnoten: | Date of publication: 27 April 2017 ; Gesehen am 22.04.2020 |
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| Titel Quelle: | Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on medical imaging |
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| Ort Quelle: | New York, NY : Institute of Electrical and Electronics Engineers, 1982 |
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| Jahr Quelle: | 2018 |
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| Band/Heft Quelle: | 37(2018), 1, Seite 35-46 |
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| ISSN Quelle: | 1558-254X |
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| Abstract: | Diffusion-weighted magnetic resonance imaging (DWI) is a key non-invasive imaging technique for cancer diagnosis and tumor treatment assessment, reflecting Brownian movement of water molecules in tissues. Since densely packed cells restrict molecule mobility, tumor tissues produce usually higher signal (a.k.a. less attenuated signal) on isotropic maps compared with normal tissues. However, no general quantitative relation between DWI data and the cell density has been established. In order to link low-resolution clinical cross-sectional data with high-resolution histological information, we developed an image processing and analysis chain, which was used to study the correlation between the diffusion coefficient (D value) estimated from DWI and tumor cellularity from serial histological slides of a resected non-small cell lung cancer tumor. Color deconvolution followed by cell nuclei segmentation was performed on digitized histological images to determine local and cell-type specific 2d (two-dimensional) densities. From these, the 3d cell density was inferred by a model-based sampling technique, which is necessary for the calculation of local and global 3d tumor cell count. Next, DWI sequence information was overlaid with high-resolution CT data and the resected histology using prominent anatomical hallmarks for co-registration of histology tissue blocks and non-invasive imaging modalities' data. The integration of cell numbers information and DWI data derived from different tumor areas revealed a clear negative correlation between cell density and D value. Importantly, spatial tumor cell density can be calculated based on DWI data. In summary, our results demonstrate that tumor cell count and heterogeneity can be predicted from DWI data, which may open new opportunities for personalized diagnosis and therapy optimization. |
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| DOI: | doi:10.1109/TMI.2017.2698525 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1109/TMI.2017.2698525 |
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| | Volltext: https://ieeexplore.ieee.org/document/7913723 |
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| | DOI: https://doi.org/10.1109/TMI.2017.2698525 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | 3d cell density |
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| | Algorithms |
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| | biodiffusion |
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| | biomedical MRI |
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| | Brownian movement |
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| | cancer |
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| | cancer diagnosis |
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| | Carcinoma, Non-Small-Cell Lung |
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| | Cell Count |
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| | cell density |
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| | cell nuclei segmentation |
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| | Cell Nucleus |
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| | cell-type specific 2d densities |
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| | cellular biophysics |
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| | color deconvolution |
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| | computerised tomography |
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| | Correlation |
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| | deconvolution |
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| | densely packed cells |
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| | diffusion coefficient |
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| | Diffusion Magnetic Resonance Imaging |
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| | diffusion-weighted MRI |
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| | digitized histological images |
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| | DWI |
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| | DWI sequence information |
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| | heterogeneity |
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| | heterogeneity estimation |
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| | high-resolution CT data |
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| | high-resolution histological information |
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| | Histocytochemistry |
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| | histological data |
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| | histology data |
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| | histopathology |
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| | Humans |
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| | image analysis |
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| | image co-registration |
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| | image colour analysis |
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| | Image Interpretation, Computer-Assisted |
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| | image processing |
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| | image segmentation |
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| | image sequences |
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| | isotropic maps |
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| | local 3D tumor cell count |
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| | low-resolution clinical cross-sectional data |
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| | lung |
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| | lung cancer |
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| | Lung Neoplasms |
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| | Lungs |
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| | magnetic resonance imaging |
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| | medical image processing |
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| | model-based sampling technique |
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| | molecule mobility |
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| | noninvasive imaging modalities |
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| | noninvasive imaging technique |
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| | optimisation |
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| | personalized diagnosis |
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| | resected nonsmall cell lung cancer tumor |
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| | serial histological slides |
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| | therapy optimization |
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| | Three-dimensional displays |
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| | tissues |
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| | tumor cell load |
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| | tumor cellularity |
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| | tumor tissues |
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| | tumor treatment |
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| | Tumors |
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| | tumours |
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| K10plus-PPN: | 1695644530 |
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| Verknüpfungen: | → Zeitschrift |
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Tumor cell load and heterogeneity estimation from diffusion-weighted mri calibrated with histological data / Yin, Yi [VerfasserIn]; 2018 (Online-Ressource)
