2013
DOI: 10.1002/mrm.24928
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MR image reconstruction from generalized projections

Abstract: For complex spatial encoding strategies that involve nonlinear gradient fields, fast and accurate image reconstruction methods are provided that are particularly efficient for high-resolution anatomical imaging.

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Cited by 16 publications
(15 citation statements)
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“…The strategy we present provides T2-weighted contrast and reduces the number of needed repetition times while maintaining resilience to undersampling. However, other strategies share features with this work, including multicontrast encoding schemes (8,(10)(11)(12)(13)35,48,49), extended two-dimensional trajectories with multicontrast properties (50)(51)(52)(53)(54)(55)(56), and single-shot nonlinear gradient encoding strategies (20,21,27,29,(57)(58)(59)(60)(61).…”
Section: Discussionmentioning
confidence: 99%
“…The strategy we present provides T2-weighted contrast and reduces the number of needed repetition times while maintaining resilience to undersampling. However, other strategies share features with this work, including multicontrast encoding schemes (8,(10)(11)(12)(13)35,48,49), extended two-dimensional trajectories with multicontrast properties (50)(51)(52)(53)(54)(55)(56), and single-shot nonlinear gradient encoding strategies (20,21,27,29,(57)(58)(59)(60)(61).…”
Section: Discussionmentioning
confidence: 99%
“…To acquire data, the magnet is physically rotated around the sample in discrete steps. At each rotation step, generalized projections onto the nonlinear field are acquired (similar to those described in Schultz et al) . Examples of these projections are shown in Figure for a simple two‐sphere phantom.…”
Section: Methodsmentioning
confidence: 99%
“…Other researchers have taken more empirical approaches to designing nonlinear encoding trajectories, such as the MDE work by Lin (20) and optimization schemes being pursued by Layton et al (21). Much effort has also gone into improving the reconstruction algorithms for data obtained with nonlinear SEMs (22)(23)(24)(25)(26)(27)(28).…”
Section: Introductionmentioning
confidence: 99%