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Curvelets as a sparse basis for compressed sensing magnetic resonance imaging.

, , , и . Medical Imaging: Image Processing, том 8669 из SPIE Proceedings, стр. 866929. SPIE, (2013)

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Research and applications: Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy., , , , , , , , , и . J. Am. Medical Informatics Assoc., 20 (4): 688-695 (2013)Curvelets as a sparse basis for compressed sensing magnetic resonance imaging., , , и . Medical Imaging: Image Processing, том 8669 из SPIE Proceedings, стр. 866929. SPIE, (2013)Robustness of Quantitative Compressive Sensing MRI: The Effect of Random Undersampling Patterns on Derived Parameters for DCE- and DSC-MRI., , , , , , и . IEEE Trans. Medical Imaging, 31 (2): 504-511 (2012)Early DCE-MRI Changes after Longitudinal Registration May Predict Breast Cancer Response to Neoadjuvant Chemotherapy., , , , , , , , , и . WBIR, том 7359 из Lecture Notes in Computer Science, стр. 229-235. Springer, (2012)Towards quantitative quasi-static elastography with a gravity-induced deformation source., , , , и . Medical Imaging: Image-Guided Procedures, том 10135 из SPIE Proceedings, стр. 1013502. SPIE, (2017)Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data., , , , , и . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, том 9788 из SPIE Proceedings, стр. 978811. SPIE, (2016)