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Accelerated imaging of rest and stress myocardial perfusion MRI using multi-coil k-t SLR: a feasibility study
© Lingala et al; licensee BioMed Central Ltd. 2012
- Published: 1 February 2012
- Myocardial Perfusion Imaging
- Stress Myocardial Perfusion
- Sparse Property
- Flash Saturation
- Spectral Regularization
In myocardial perfusion imaging (MPI), highly accelerated acquisition can be used towards reducing the compromises in the image quality (spatio-temporal resolutions, volume coverage, SNR) routinely observed with the current clinical protocols. In this study, we demonstrate the feasibility of a recent accelerated dynamic MRI scheme, k-t SLR (based on sparse and low rank properties) (Lingala et al. '11) in free breathing MPI. k-t SLR exploits natural redundancy in MPI by using (a) the high degree of temporal correlations, and (b) the sparse properties of the images in appropriate transform domains (finite difference transforms along space and time). It poses the recovery as a spectral regularization problem, allowing for the use of fast optimization solvers and flexible non-Cartesian sampling schemes. Here, we also include information from multiple coils to improve data consistency in the recovery. We demonstrate high accelerations with both rest and stress data sets. Comparisons are made with existing schemes such as spatio-temporal constrained reconstruction (STCR, Adluru et al '09) and k-t SPARSE/SENSE (Otazo et al '10).
A radial FLASH saturation recovery sequence (TR/TE~2.5/1.3ms) was used. 3 slices with 72 rays uniformly spaced within a frame and offset between frames were acquired. Datasets for 3 normal subjects and 1 patient with disease were imaged in rest and adenosine stress. These 72 ray sets correspond to an acceleration of R ~ 3.4 compared to Nyquist sampling. Residual streaking was resolved in these sets by using a standard STCR algorithm and formed the ‘reference’ images. We performed retrospective undersampling using 21 rays (R~12) and 18 rays (R~14) respectively for the stress and rest sets and reconstructed with multi-coil k-t SLR, multi-coil STCR and multi-coil k-t SPARSE. Golden ratio, uniform and random sampling were respectively used for k-t SLR, STCR and k-t SPARSE/SENSE, based on experiments of which sampling pattern was best for each algorithm.
We observe k-t SLR to provide good correlation with the reference images. k-t SLR is robust to artifacts such as over-smoothing and motion blur, yielding better slope estimates compared to STCR and k-t SPARSE/SENSE (fig 1). In comparison to existing schemes, k-t SLR obtained significantly less error in the metrics over all the data sets (fig 2).
We demonstrated high accelerations in rest and stress imaging. This can be used towards improving a number of factors such as increasing the number of slices, and performing rapid scans such as systolic imaging or ungated imaging, where the acquisition window is significantly shorter than what is usually seen in diastolic imaging.
NSF AWARD CCF-0844812 and in part by NIH R01EB006155.
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.