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• W2963192882 abstract "Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical structures. However, the acquisition of MR volumes with high spatial resolution leads to long scan times. To this end, we propose volumetric super-resolution forests (VSRF) to enhance MRI resolution retrospectively. Our method learns a locally linear mapping between low-resolution and high-resolution volumetric image patches by employing random forest regression. We customize features suitable for volumetric MRI to train the random forest and propose a median tree ensemble for robust regression. VSRF out-performs state-of-the-art example-based super-resolution in terms of image quality and efficiency for model training and inference on different MRI datasets. It is also superior to unsupervised methods with just a handful or even a single volume to assemble training data." @default.
• W2963192882 created "2019-07-30" @default.
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• W2963192882 date "2018-10-01" @default.
• W2963192882 modified "2023-09-27" @default.
• W2963192882 title "Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests" @default.
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• W2963192882 doi "https://doi.org/10.1109/icip.2018.8451320" @default.
• W2963192882 hasPublicationYear "2018" @default.
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