Improved compressed sensing reconstruction for 19F magnetic resonance imaging

• Verfasst von: Kampf, Thomas [VerfasserIn]
• Verfasst von: Sturm, Volker Jörg Friedrich [VerfasserIn]
• Verfasst von: Buschle, Lukas R. [VerfasserIn]
• Verfasst von: Kurz, Felix T. [VerfasserIn]
• Verfasst von: Schlemmer, Heinz-Peter [VerfasserIn]
• Verfasst von: Ziener, Christian H. [VerfasserIn]
• Verfasst von: Heiland, Sabine [VerfasserIn]
• Verfasst von: Bendszus, Martin [VerfasserIn]
• Titel: Improved compressed sensing reconstruction for 19F magnetic resonance imaging
• Verf.angabe: Thomas Kampf, Volker J.F. Sturm, Thomas C. Basse-Lüsebrink, André Fischer, Lukas R. Buschle, Felix T. Kurz, Heinz-Peter Schlemmer, Christian H. Ziener, Sabine Heiland, Martin Bendszus, Mirko Pham, Guido Stoll, Peter M. Jakob
• E-Jahr: 2019
• Jahr: 2 January 2019
• Umfang: 15 S.
• Fussnoten: Im Text ist die Zahl 19 hochgestellt ; Gesehen am 20.05.2019
• Titel Quelle: Enthalten in: Magnetic resonance materials in physics, biology and medicine
• Ort Quelle: Heidelberg : Springer, 1993
• Jahr Quelle: 2019
• Band/Heft Quelle: 32(2019), 1, Seite 63-77
• ISSN Quelle: 1352-8661
• DOI: doi:10.1007/s10334-018-0729-1
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: 19F
• Sach-SW: Artifact
• Sach-SW: Compressed sensing
• Sach-SW: CSI
• Sach-SW: MRI
• Sach-SW: MRSI
• Sach-SW: Sparse
• K10plus-PPN: 1665925108

Abstract

ObjectiveIn magnetic resonance imaging (MRI), compressed sensing (CS) enables the reconstruction of undersampled sparse data sets. Thus, partial acquisition of the underlying k-space data is sufficient, which significantly reduces measurement time. While 19F MRI data sets are spatially sparse, they often suffer from low SNR. This can lead to artifacts in CS reconstructions that reduce the image quality. We present a method to improve the image quality of undersampled, reconstructed CS data sets.Materials and methodsTwo resampling strategies in combination with CS reconstructions are presented. Numerical simulations are performed for low-SNR spatially sparse data obtained from 19F chemical-shift imaging measurements. Different parameter settings for undersampling factors and SNR values are tested and the error is quantified in terms of the root-mean-square error.ResultsAn improvement in overall image quality compared to conventional CS reconstructions was observed for both strategies. Specifically spike artifacts in the background were suppressed, while the changes in signal pixels remained small. DiscussionThe proposed methods improve the quality of CS reconstructions. Furthermore, because resampling is applied during post-processing, no additional measurement time is required. This allows easy incorporation into existing protocols and application to already measured data.