Error decomposition for parallel imaging reconstruction using modulation-domain representation of undersampled data

Quantitative Imaging in Medicine and Surgery

Yu Li¹ 

[1] Imaging Research Center, Radiology Department, Cincinnati Children’s Hospital Medical Center 3333 Burnet Avenue, Cincinnati, OH 45229,USA

关键词: Parallel imaging;    modulation-domain;    error decomposition;   

DOI  :  10.3978/j.issn.2223-4292.2014.04.07

学科分类：外科医学

来源: AME Publications

【 摘 要 】

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.

【 授权许可】

Unknown