A locally segmented reconstruction method for parallel imaging. Issue 3 (18th February 2020)

• Record Type: Journal Article
• Authors: So, Seohee; Seo, Hyunseok; Park, HyunWook
• Is Part Of: Magnetic resonance in medicine. Volume 84:Issue 3(2020)
• Page Start: 1638
• Page End: 1647
• Publication Date: 2020-02-18
• Subjects: aliasing artifacts -- locally segmented reconstruction -- multichannel receiver coil -- parallel imaging; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28193
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: A locally segmented parallel imaging reconstruction method is proposed that efficiently utilizes sensitivity distribution of multichannel receiver coil. Theory and Methods: A method of locally segmenting a MR signal is introduced to maximize the differences in sensitivity between receiver channels. A 1D Fourier transformation of the undersampled k‐space data is performed along the readout direction, which generates a hybrid 2D space. The hybrid space is partitioned into localized segments along the readout direction. In every localized segment, kernels representing relation between adjacent signals are estimated from autocalibration signals, and data at unsampled points are estimated using the kernels. Then, the images are reconstructed from full k‐space data that consists of the sampled data and the estimated data at unsampled points. Results: In a computer simulation and in vivo experiments, the locally segmented reconstruction method produced fewer residual artifacts compared to the conventional parallel imaging reconstruction methods with the same kernel geometry. The performance gain of the proposed method comes from maximizing encoding capability of receiver channels, thus resulting in the accurately estimated kernel weights that reflect the relation between adjacent signals. Conclusion: The proposed spatial segmentation method maximally utilizes differences in the sensitivity of receiver channels to reconstruct images with reduced artifacts.