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Autocalibrating segmented diffusion‐weighted acquisitions
Author(s) -
Herbst Michael
Publication year - 2021
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28854
Subject(s) - computer science , calibration , diffusion mri , imaging phantom , phase (matter) , algorithm , iterative reconstruction , noise (video) , echo planar imaging , artificial intelligence , reconstruction algorithm , frame (networking) , computer vision , signal (programming language) , pattern recognition (psychology) , mathematics , image (mathematics) , nuclear medicine , magnetic resonance imaging , statistics , physics , radiology , medicine , telecommunications , quantum mechanics , programming language
Purpose Segmented echo‐planar imaging enables high‐resolution diffusion‐weighted imaging (DWI). However, phase differences between segments can lead to severe artifacts. This work investigates an algorithm to enable reconstruction of interleaved segmented acquisitions without the need of additional calibration or navigator measurements. Methods A parallel imaging algorithm is presented that jointly reconstructs all segments of one DWI frame maintaining their phase information. Therefore, the algorithm allows for an iterative improvement of the phase estimates included in the joint reconstruction. Given a limited number of interleaves, the initial‐phase estimates can be calculated by a traditional parallel‐imaging reconstruction, using the unweighted scan of the DWI measurement as a reference. Results Reconstruction of phantom data and g‐factor simulations show substantial improvement (up to 93% reduction in root mean square error) compared with a generalized auto‐calibrating partially parallel‐acquisition reconstruction. In vivo experiments show robust reconstruction outcomes in critical imaging situations, including small numbers of receiver channels or low signal‐to‐noise ratio. Conclusion An algorithm for the robust reconstruction of segmented DWI data is presented. The method requires neither navigator nor calibration measurements; therefore, it can be applied to existing DWI data sets.