Recently, a model based dynamic imaging algorithm called k-t BLAST/SENSE has drawn significant attentions from MR imaging community due to its improved spatio-temporal resolution for dynamic MR imaging. In our previous work, we proved that k-t BLAST/SENSE can be derived as the first step of FOCal Underdetermined System Solver (FOCUSS) that exploits the sparsity of x-f support. Furthermore, the newly derived algorithm called k-t FOCUSS can be shown optimal from compressed sensing perspective. In this paper, the k-t FOCUSS algorithm is extended to radial trajectory. More specifically, the
radial data are transformed to Cartesian domain implicitly during
the FOCUSS iterations without explicit gridding to prevent error propagation. Thanks to the implicit gridding that allows fast Fourier transform, we can reduce the computational burden
significantly. Additionally, a novel concept of motion estimation and compensation (ME/MC) is proposed to
improve the performance of the algorithm significantly. In our ME/MC framework, we additionally obtain one reference sinogram with the full view, then the reference signogram is subtracted from all the radial data. Then, we can apply motion estimation/ motion compensation (ME/MC) to improve the final reconstruction. The experimental results show that our new method can provide very high resolution even from very limited radial data set.
KEYWORDS: Reconstruction algorithms, Magnetic resonance imaging, Image resolution, Medical imaging, Compressed sensing, Spatial resolution, Optimization (mathematics), Data acquisition, In vivo imaging, Computer simulations
This paper is concerned about high resolution reconstruction of projection reconstruction MR imaging from
angular under-sampled k-space data. A similar problem has been recently addressed in the framework of compressed
sensing theory. Unlike the existing algorithms used in compressed sensing theory, this paper employs
the FOCal Underdetermined System Solver(FOCUSS), which was originally designed for EEG and MEG source
localization to obtain sparse solution by successively solving quadratic optimization. We show that FOCUSS
is very effective for the projection reconstruction MRI, because the medical images are usually sparse in image
domain, and the center region of the under-sampled radial k-space data still provides a meaningful low resolution
image, which is essential for the convergence of FOCUSS. We applied FOCUSS for projection reconstruction MR
imaging using single coil. Extensive experiments confirms that high resolution reconstruction with virtually free
of angular aliasing artifacts can be obtained from severely under-sampled k-space data.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.