DCE-MRI and DWI Integration for Breast Lesions Assessment and Heterogeneity Quantification
In order to better predict and follow treatment responses in cancer patients, there is growing interest in noninvasively characterizing tumor heterogeneity based on MR images possessing different contrast and quantitative information. This requires mechanisms for integrating such data and reducing t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2012/676808 |
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| author | C. Andrés Méndez Francesca Pizzorni Ferrarese Paul Summers Giuseppe Petralia Gloria Menegaz |
| author_facet | C. Andrés Méndez Francesca Pizzorni Ferrarese Paul Summers Giuseppe Petralia Gloria Menegaz |
| author_sort | C. Andrés Méndez |
| collection | DOAJ |
| description | In order to better predict and follow treatment responses in cancer patients, there is growing interest in noninvasively characterizing tumor heterogeneity based on MR images possessing different contrast and quantitative information. This requires mechanisms for integrating such data and reducing the data dimensionality to levels amenable to interpretation by human readers. Here we propose a two-step pipeline for integrating diffusion and perfusion MRI that we demonstrate in the quantification of breast lesion heterogeneity. First, the images acquired with the two modalities are aligned using an intermodal registration. Dissimilarity-based clustering is then performed exploiting the information coming from both modalities. To this end an ad hoc distance metric is developed and tested for tuning the weighting for the two modalities. The distributions of the diffusion parameter values in subregions identified by the algorithm are extracted and compared through nonparametric testing for posterior evaluation of the tissue heterogeneity. Results show that the joint exploitation of the information brought by DCE and DWI leads to consistent results accounting for both perfusion and microstructural information yielding a greater refinement of the segmentation than the separate processing of the two modalities, consistent with that drawn manually by a radiologist with access to the same data. |
| format | Article |
| id | doaj-art-1c0d4ca126e44ff8b495bcc82e04f482 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-1c0d4ca126e44ff8b495bcc82e04f4822025-02-03T07:24:33ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962012-01-01201210.1155/2012/676808676808DCE-MRI and DWI Integration for Breast Lesions Assessment and Heterogeneity QuantificationC. Andrés Méndez0Francesca Pizzorni Ferrarese1Paul Summers2Giuseppe Petralia3Gloria Menegaz4Dipartimento di Informatica, Universita degli Studi di Verona, Strada le Grazie 15, CA'Vignal, 37134 Verona, ItalyDipartimento di Informatica, Universita degli Studi di Verona, Strada le Grazie 15, CA'Vignal, 37134 Verona, ItalyDivisione di Radiologia, Istituto Europeo di Oncologia, Via Ripamonti 435, 20141 Milano, ItalyDivisione di Radiologia, Istituto Europeo di Oncologia, Via Ripamonti 435, 20141 Milano, ItalyDipartimento di Informatica, Universita degli Studi di Verona, Strada le Grazie 15, CA'Vignal, 37134 Verona, ItalyIn order to better predict and follow treatment responses in cancer patients, there is growing interest in noninvasively characterizing tumor heterogeneity based on MR images possessing different contrast and quantitative information. This requires mechanisms for integrating such data and reducing the data dimensionality to levels amenable to interpretation by human readers. Here we propose a two-step pipeline for integrating diffusion and perfusion MRI that we demonstrate in the quantification of breast lesion heterogeneity. First, the images acquired with the two modalities are aligned using an intermodal registration. Dissimilarity-based clustering is then performed exploiting the information coming from both modalities. To this end an ad hoc distance metric is developed and tested for tuning the weighting for the two modalities. The distributions of the diffusion parameter values in subregions identified by the algorithm are extracted and compared through nonparametric testing for posterior evaluation of the tissue heterogeneity. Results show that the joint exploitation of the information brought by DCE and DWI leads to consistent results accounting for both perfusion and microstructural information yielding a greater refinement of the segmentation than the separate processing of the two modalities, consistent with that drawn manually by a radiologist with access to the same data.http://dx.doi.org/10.1155/2012/676808 |
| spellingShingle | C. Andrés Méndez Francesca Pizzorni Ferrarese Paul Summers Giuseppe Petralia Gloria Menegaz DCE-MRI and DWI Integration for Breast Lesions Assessment and Heterogeneity Quantification International Journal of Biomedical Imaging |
| title | DCE-MRI and DWI Integration for Breast Lesions Assessment and Heterogeneity Quantification |
| title_full | DCE-MRI and DWI Integration for Breast Lesions Assessment and Heterogeneity Quantification |
| title_fullStr | DCE-MRI and DWI Integration for Breast Lesions Assessment and Heterogeneity Quantification |
| title_full_unstemmed | DCE-MRI and DWI Integration for Breast Lesions Assessment and Heterogeneity Quantification |
| title_short | DCE-MRI and DWI Integration for Breast Lesions Assessment and Heterogeneity Quantification |
| title_sort | dce mri and dwi integration for breast lesions assessment and heterogeneity quantification |
| url | http://dx.doi.org/10.1155/2012/676808 |
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