@article{QIMS3729,
author = {Yu Li},
title = {Error decomposition for parallel imaging reconstruction using modulation-domain representation of undersampled data},
journal = {Quantitative Imaging in Medicine and Surgery},
volume = {4},
number = {2},
year = {2014},
keywords = {},
abstract = {This paper presents a quantitative approach to evaluating and optimizing parallel imaging reconstruction for a clinical requirement. By introducing a “modulation domain representation” for undersampled data, the presented approach decomposes parallel imaging reconstruction error into multiple error components that can be grouped into three categories: image fidelity error, residue aliasing artifacts, and amplified noise. It is experimentally found that these error components have different image-space patterns that compromise imaging quality in different fashions. An error function may be defined as the weighted summation of these error components. By choosing a set of weighting coefficients that can quantify desirable image quality, parallel imaging may be optimized for a clinical requirement. It is found that error decomposition model may improve clinical utility of parallel imaging, providing an application-oriented approach to clinical parallel imaging.},
issn = {2223-4306}, url = {https://qims.amegroups.org/article/view/3729}
}