![]() ![]() The proposed EIV framework provides possibilities that noiseless GRAPPA reconstruction could be achieved by existing methods that solve EIV problem other than IV method. In this paper, we first analyze the GRAPPA noise problem from a noisy input-output system perspective then, a new framework based on errors-in-variables (EIV model is developed for analyzing noise generation mechanism in GRAPPA and designing a concrete method—instrument variables (IV GRAPPA to remove noise. The basic idea we proposed to improve GRAPPA is to remove noise from a system identification perspective. Noise, initially generated from scanner, propagates noise-related errors during fitting and interpolation procedures of GRAPPA to distort the final reconstructed image quality. However, noise deteriorates the reconstructed image when reduction factor increases or even at low reduction factor for some noisy datasets. Instrument Variables for Reducing Noise in Parallel MRI Reconstructionĭirectory of Open Access Journals (Sweden)įull Text Available Generalized autocalibrating partially parallel acquisition (GRAPPA has been a widely used parallel MRI technique. Experiment results from synthetic and in vivo data show that the proposed method introduces better reconstructed images than the analytic methods, even from highly subsampled data, and provides monotonic convergence properties compared to the conjugate gradient based reconstruction method. Then, to optimize the proposed method for radial p MRI, a reconstruction method that uses coil sensitivity information of multichannel RF coils is formulated. In this paper, we propose a novel reconstruction method based on the expectation maximization (EM) method, where the EM algorithm is remodeled for MRI so that complex images can be reconstructed. However, the quality of the reconstructed image from these analytic methods can be degraded when the number of acquired projection views is insufficient. For radial MRI, the image is usually reconstructed from projection data using analytic methods such as filtered back-projection or Fourier reconstruction after gridding. ![]() In MRI (magnetic resonance imaging), signal sampling along a radial k-space trajectory is preferred in certain applications due to its distinct advantages such as robustness to motion, and the radial sampling can be beneficial for reconstruction algorithms such as parallel MRI (p MRI) due to the incoherency. ![]() International Nuclear Information System (INIS)Ĭhoi, Joonsung Kim, Dongchan Oh, Changhyun Han, Yeji Park, HyunWook An iterative reconstruction method of complex images using expectation maximization for radial parallel MRI ![]()
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