Constrained alternating minimization for parameter mapping (CAMP).
Autor: | Elsaid NMH; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA., Dispenza NL; Siemens Healthcare GmbH Allee am Röthelheimpark, Erlangen 91052, Germany; Department of Biomedical Engineering, Yale University, New Haven, CT, USA., Hu C; The Institute of Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China., Peters DC; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA., Constable RT; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale University, New Haven, CT 06520, USA., Tagare HD; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA., Galiana G; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA. |
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Jazyk: | angličtina |
Zdroj: | Magnetic resonance imaging [Magn Reson Imaging] 2024 Jul; Vol. 110, pp. 176-183. Date of Electronic Publication: 2024 Apr 23. |
DOI: | 10.1016/j.mri.2024.04.029 |
Abstrakt: | Objective: To improve image quality in highly accelerated parameter mapping by incorporating a linear constraint that relates consecutive images. Approach: In multi-echo T Main Results: CAMP is demonstrated for accelerated radial and Cartesian acquisitions in T Significance: For a wide array of applications, CAMP linearizes the model cost function without sacrificing model accuracy so that the well-conditioned and highly efficient reconstruction algorithm improves the image quality of accelerated parameter maps. (Copyright © 2024 Elsevier Inc. All rights reserved.) |
Databáze: | MEDLINE |
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