Work

Cross‐validation‐based kernel support selection for improved GRAPPA reconstruction

Year: 2008

Type: article

Abstract: The extended version of the generalized autocalibrating partially parallel acquisition (GRAPPA) technique incorporates multiple lines and multiple columns of measured k-space data to estimate missing data. For a given accelerated dataset, the selection of the measured data points for fitting a missing datum (i.e., the kernel support) that provides optimal reconstruction depends on coil array configuration, noise level in the acquired data, imaging configuration, and number and position of autocalibrating signal lines. In this work, cross-validation is used to select the kernel support that best balances the conflicting demands of fit accuracy and stability in GRAPPA reconstruction. The result is an optimized tradeoff between artifacts and noise. As demonstrated with experimental data, the method improves image reconstruction with GRAPPA. Because the method is simple and applied in postprocessing, it can be used with GRAPPA routinely.

Source: Magnetic Resonance in Medicine

DOI: 10.1002/mrm.21535

Authors: Roger Nana, Tiejun Zhao, Keith Heberlein, Stephen M. LaConte, Xiaoping Hu

Institutions: Georgia Institute of Technology, Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Siemens Healthcare (United States)

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: bronze

Funders: Georgia Research Alliance, National Institute of Biomedical Imaging and Bioengineering

Award: Improvement and Applications of fMRI