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Sparsity transform k-t principal component analysis for accelerating cine three-dimensional flow measurements


Knobloch, Verena; Boesiger, Peter; Kozerke, Sebastian (2013). Sparsity transform k-t principal component analysis for accelerating cine three-dimensional flow measurements. Magnetic Resonance in Medicine, 70(1):53-63.

Abstract

Time-resolved three-dimensional flow measurements are limited by long acquisition times. Among the various acceleration techniques available, k-t methods have shown potential as they permit significant scan time reduction even with a single receive coil by exploiting spatiotemporal correlations. In this work, an extension of k-t principal component analysis is proposed utilizing signal differences between the velocity encodings of three-directional flow measurements to further compact the signal representation and hence improve reconstruction accuracy. The effect of sparsity transform in k-t principal component analysis is demonstrated using simulated and measured data of the carotid bifurcation. Deploying sparsity transform for 8-fold undersampled simulated data, velocity root-mean-square errors were found to decrease by 52 ± 14%, 59 ± 11%, and 16 ± 32% in the common, external, and internal carotid artery, respectively. In vivo, errors were reduced by 15 ± 17% in the common carotid artery with sparsity transform. Based on these findings, spatial resolution of three-dimensional flow measurements was increased to 0.8 mm isotropic resolution with prospective 8-fold undersampling and sparsity transform k-t principal component analysis reconstruction. Volumetric data were acquired in 6 min. Pathline visualization revealed details of helical flow patterns partially hidden at lower spatial resolution.

Abstract

Time-resolved three-dimensional flow measurements are limited by long acquisition times. Among the various acceleration techniques available, k-t methods have shown potential as they permit significant scan time reduction even with a single receive coil by exploiting spatiotemporal correlations. In this work, an extension of k-t principal component analysis is proposed utilizing signal differences between the velocity encodings of three-directional flow measurements to further compact the signal representation and hence improve reconstruction accuracy. The effect of sparsity transform in k-t principal component analysis is demonstrated using simulated and measured data of the carotid bifurcation. Deploying sparsity transform for 8-fold undersampled simulated data, velocity root-mean-square errors were found to decrease by 52 ± 14%, 59 ± 11%, and 16 ± 32% in the common, external, and internal carotid artery, respectively. In vivo, errors were reduced by 15 ± 17% in the common carotid artery with sparsity transform. Based on these findings, spatial resolution of three-dimensional flow measurements was increased to 0.8 mm isotropic resolution with prospective 8-fold undersampling and sparsity transform k-t principal component analysis reconstruction. Volumetric data were acquired in 6 min. Pathline visualization revealed details of helical flow patterns partially hidden at lower spatial resolution.

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16 citations in Web of Science®
18 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Language:English
Date:2013
Deposited On:14 Feb 2013 09:56
Last Modified:05 Apr 2016 16:26
Publisher:Wiley-Blackwell
ISSN:0740-3194
Publisher DOI:https://doi.org/10.1002/mrm.24431
PubMed ID:22887065

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