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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-53713

Yankam Njiwa, J; Baltes, C; Rudin, M (2011). Analytic image concept combined to SENSE reconstruction. Magnetic Resonance Materials in Physics Biology and Medicine, 24(5):305-313.

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Abstract

Two approaches of reconstructing undersampled partial k-space data, acquired with multiple coils are compared: homodyne detection combined with SENSE (HM_SENSE) and analytic image reconstruction combined with SENSE (AI_SENSE). The latter overcomes limitations of HM_ SENSE by considering aliased images as analytic thus avoiding the need for phase correction required for HM_SENSE.

MATERIALS AND METHODS: In vivo imaging experiments were carried out in male Lewis rats using both gradient echo and spin echo sequences. Accelerated images obtained by using the various reconstruction algorithms were compared to fully sampled reference images both qualitatively and quantitatively.

RESULTS: For the various sampling patterns evaluated, both HM_SENSE and AI_SENSE were found to yield robust image reconstruction with small deviations from the reference image. Even for high acceleration factors AI_SENSE still provided useful results and was found superior compared to HM_SENSE.

CONCLUSION: Combination of partial k-space sampling and parallel image acquisition allows for further acceleration of data acquisition as compared to each method alone. Image reconstruction from undersampled data sets using the AI_SENSE algorithm was found to considerably reduce reconstruction errors and artifacts observed for HM_SENSE reconstruction caused by errors in phase estimation.

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Pharmacology and Toxicology
04 Faculty of Medicine > Institute of Biomedical Engineering
DDC:570 Life sciences; biology
170 Ethics
610 Medicine & health
Language:English
Date:2011
Deposited On:13 Jan 2012 11:24
Last Modified:29 Dec 2013 18:22
Publisher:Springer
ISSN:0968-5243
Publisher DOI:10.1007/s10334-011-0274-7
PubMed ID:21833790
Citations:Web of Science®
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