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001-es BibID:BIBFORM070522
Első szerző:Béres Mónika (képalkotó diagnoszta, eü. mérnökinformatikus)
Cím:2D and 3D texture analysis to differentiate brain metastases on MR images : proceed with caution / Béresová Monika, Larroza Andrés, Arana Estanislao, Varga József, Balkay László, Moratal David
Dátum:2018
ISSN:0968-5243
Megjegyzések:ObjectiveTo find structural differences between brain metastases of lung and breast cancer, computing their heterogeneity parameters by means of both 2D and 3D texture analysis (TA).Materials and methodsPatients with 58 brain metastases from breast (26) and lung cancer (32) were examined by MR imaging. Brain lesions were manually delineated by 2D ROIs on the slices of contrast-enhanced T1-weighted (CET1) images, and local binary patterns (LBP) maps were created from each region. Histogram-based (minimum, maximum, mean, standard deviation, and variance), and co-occurrence matrix-based (contrast, correlation, energy, entropy, and homogeneity) 2D, weighted average of the 2D slices, and true 3D TA were obtained on the CET1 images and LBP maps.ResultsFor LBP maps and 2D TA contrast, correlation, energy, and homogeneity were identified as statistically different heterogeneity parameters (SDHPs) between lung and breast metastasis. The weighted 3D TA identified entropy as an additional SDHP. Only two texture indexes (TI) were significantly different with true 3D TA: entropy and energy. All these TIs discriminated between the two tumor types significantly by ROC analysis. For the CET1 images there was no SDHP at all by 3D TA.ConclusionOur results indicate that the used textural analysis methods may help with discriminating between brain metastases of different primary tumors.
Tárgyszavak:Orvostudományok Egészségtudományok idegen nyelvű folyóiratközlemény külföldi lapban
Computer-assisted
Image processing
Texture analysis
Magnetic resonance imaging
Brain neoplasms
Metastasis
Breast cancer
Lung cancer
Megjelenés:Magnetic Resonance Materials In Physics Biology And Medicine. - 31 : 2 (2018), p. 285-294. -
További szerzők:Larroza, Andrés Arana, Estanislao Varga József (1955-) (fizikus) Balkay László (1963-) (biofizikus) Moratal, David
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Intézményi repozitóriumban (DEA) tárolt változat
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2.

001-es BibID:BIBFORM078799
035-os BibID:(PMID)31108468 (cikkazonosító)125016
Első szerző:Forgács Attila (fizikus)
Cím:Impact of intensity discretization on textural indices of [18F]FDG-PET tumour heterogeneity in lung cancer patients / Attila Forgács, Monika Béresová, Ildikó Garai, Martin L. Lassen, Thomas Beyer, Matthew D. DiFranco, Ervin Berényi, László Balkay
Dátum:2019
ISSN:0031-9155 1361-6560
Megjegyzések:Quantifying tumour heterogeneity from [18F]FDG-PET images promises benefits for treatment selection of cancer patients. Here, the calculation of texture parameters mandates an initial discretization step (binning) to reduce the number of intensity levels. Typically, three types of discrimination methods are used: lesion relative resampling (LRR) with fixed bin number, lesion absolute resampling (LAR) and absolute resampling (AR) with fixed bin widths. We investigated the effects of varying bin widths or bin number using 27 commonly cited local and regional texture indices (TIs) applied on lung tumour volumes. The data set were extracted from 58 lung cancer patients, with three different and robust tumour segmentation methods. In our cohort, the variations of the mean value as the function of the bin widths were similar for TIs calculated with LAR and AR quantification. The TI histograms calculated by LRR method showed distinct behaviour and its numerical values substantially effected by the selected bin number. The correlations of the AR and LAR based TIs demonstrated no principal differences between these methods. However, no correlation was found for the interrelationship between the TIs calculated by LRR and LAR (or AR) discretization method. Visual classification of the texture was also performed for each lesion. This classification analysis revealed that the parameters show statistically significant correlation with the visual score, if LAR or AR discretization method is considered, in contrast to LRR. Moreover, all the resulted tendencies were similar regardless the segmentation methods and the type of textural features involved in this work.
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
Megjelenés:Physics in Medicine & Biology. - 64 : 12 (2019), p. 1-20. -
További szerzők:Béres Mónika (1982-) (képalkotó diagnoszta, eü. mérnökinformatikus) Garai Ildikó (1966-) (radiológus) Lassen, Martin Lyngby Beyer, Thomas DiFranco, Matthew D. Berényi Ervin (1964-) (radiológus) Balkay László (1963-) (biofizikus)
Pályázati támogatás:EFOP-3.6.3-VEKOP-16-2017-00009
EFOP
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DOI
Intézményi repozitóriumban (DEA) tárolt változat
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3.

001-es BibID:BIBFORM085584
Első szerző:Tecklenburg, Kristian
Cím:Performance evaluation of a novel multi-pinhole collimator for dopamine transporter SPECT / Tecklenburg Kristian, Forgács Attila, Apostolova Iva, Lehnert Wencke, Klutmann Susanne, Csirik János, Garutti Erika, Buchert Ralph
Dátum:2020
ISSN:0031-9155
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Physics In Medicine And Biology. - 65 : 16 (2020), p. 165015. -
További szerzők:Forgács Attila (1985-) (fizikus) Apostolova, Iva Lehnert, Wencke Klutmann, Susanne Csirik János Garutti, Erika Buchert, Ralph
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Intézményi repozitóriumban (DEA) tárolt változat
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