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About Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors
1. This article presents a novel approach to predict pseudo CT images from MRI data through feature matching with learned nonlinear local descriptors. 2. Nonlinear local descriptors are obtained by projecting linear descriptors into a nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. 3. The nearest neighbors of each local descriptor in the input MR images are searched among the training dataset, and pseudo CT patches are estimated through k-nearest neighbor regression.
Detailed Information
| Author: | ['A'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 11 |
| Language: | English |
| Format: | |
| Price: | FREE |
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