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Radial k-t FOCUSS for high-resolution cardiac cine MRI

✍ Scribed by Hong Jung; Jaeseok Park; Jaeheung Yoo; Jong Chul Ye


Book ID
102954326
Publisher
John Wiley and Sons
Year
2009
Tongue
English
Weight
755 KB
Volume
63
Category
Article
ISSN
0740-3194

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✦ Synopsis


Abstract

A compressed sensing dynamic MR technique called k‐t FOCUSS (k‐t FOCal Underdetermined System Solver) has been recently proposed. It outperforms the conventional k‐t BLAST/SENSE (Broad‐use Linear Acquisition Speed‐up Technique/SENSitivity Encoding) technique by exploiting the sparsity of x‐f signals. This paper applies this idea to radial trajectories for high‐resolution cardiac cine imaging. Radial trajectories are more suitable for high‐resolution dynamic MRI than Cartesian trajectories since there is smaller tradeoff between spatial resolution and number of views if streaking artifacts due to limited views can be resolved. As shown for Cartesian trajectories, k‐t FOCUSS algorithm efficiently removes artifacts while preserving high temporal resolution. k‐t FOCUSS algorithm applied to radial trajectories is expected to enhance dynamic MRI quality. Rather than using an explicit gridding method, which transforms radial k‐space sampling data to Cartesian grid prior to applying k‐t FOCUSS algorithms, we use implicit gridding during FOCUSS iterations to prevent k‐space sampling errors from being propagated. In addition, motion estimation and motion compensation after the first FOCUSS iteration were used to further sparsify the residual image. By applying an additional k‐t FOCUSS step to the residual image, improved resolution was achieved. In vivo experimental results show that this new method can provide high spatiotemporal resolution even from a very limited radial data set. Magn Reson Med, 2010. © 2009 Wiley‐Liss, Inc.


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k-t FOCUSS: A general compressed sensing
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## Abstract A model‐based dynamic MRI called k‐t BLAST/SENSE has drawn significant attention from the MR imaging community because of its improved spatio‐temporal resolution. Recently, we showed that the k‐t BLAST/SENSE corresponds to the special case of a new dynamic MRI algorithm called k‐t FOCUS

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## Abstract In __k__‐__t__ BLAST and __k__‐__t__ SENSE, data acquisition is accelerated by sparsely sampling __k__‐space over time. This undersampling in __k__‐__t__ space causes the object signals to be convolved with a point spread function in __x__‐__f__ space (__x__ = spatial position, __f__ =