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Uncertainty Quantification for Cardiac Diffusion Tensor Imaging Without Additional Datasets
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DOI：10.1002/mrm.70414.png)
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Abstract
En 中文
Cardiac diffusion tensor imaging (cDTI) is subject to physiological noise, thermal noise, and signal corruption, which cause errors in diffusion measures. While a larger dataset can be decimated to investigate the general precision of measures from fitting smaller datasets, uncertainty quantification (UQ) methods for fitting entire particular datasets are required for UQ to be output from cDTI post-processing pipelines.
Keywords:
bootstrap
cardiac diffusion tensor imaging
magnetic resonance imaging
sampling distribution
uncertainty quantification
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