Reconstruction of parallel MRI Images Using High Resolution Image Reconstruction Techniques (original) (raw)
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Magnetic Resonance in Medicine, 2007
The reconstruction of artifact-free images from radially encoded MRI acquisitions poses a difficult task for undersampled data sets, that is for a much lower number of spokes in k-space than data samples per spoke. Here, we developed an iterative reconstruction method for undersampled radial MRI which (i) is based on a nonlinear optimization, (ii) allows for the incorporation of prior knowledge with use of penalty functions, and (iii) deals with data from multiple coils. The procedure arises as a twostep mechanism which first estimates the coil profiles and then renders a final image that complies with the actual observations. Prior knowledge is introduced by penalizing edges in coil profiles and by a total variation constraint for the final image. The latter condition leads to an effective suppression of undersampling (streaking) artifacts and further adds a certain degree of denoising. Apart from simulations, experimental results for a radial spin-echo MRI sequence are presented for phantoms and human brain in vivo at 2.9 T using 24, 48, and 96 spokes with 256 data samples. In comparison to conventional reconstructions (regridding) the proposed method yielded visually improved image quality in all cases. Magn Reson Med 57:1086-1098, 2007.
Regularization of parallel MRI reconstruction using in vivo coil sensitivities
2009
Parallel MRI can achieve increased spatiotemporal resolution in MRI by simultaneously sampling reduced k-space data with multiple receiver coils. One requirement that different parallel MRI techniques have in common is the need to determine spatial sensitivity information for the coil array. This is often done by smoothing the raw sensitivities obtained from low-resolution calibration images, for example via polynomial fitting. However, this sensitivity post-processing can be both time-consuming and error-prone. Another important factor in Parallel MRI is noise amplification in the reconstruction, which is due to non-unity transformations in the image reconstruction associated with spatially correlated coil sensitivity profiles. Generally, regularization approaches, such as Tikhonov and SVD-based methods, are applied to reduce SNR loss, at the price of introducing residual aliasing. In this work, we present a regularization approach using in vivo coil sensitivities in parallel MRI to overcome these potential errors into the reconstruction. The mathematical background of the proposed method is explained, and the technique is demonstrated with phantom images. The effectiveness of the proposed method is then illustrated clinically in a whole-heart 3D cardiac MR acquisition within a single breath-hold. The proposed method can not only overcome the sensitivity calibration problem, but also suppress a substantial portion of reconstruction-related noise without noticeable introduction of residual aliasing artifacts.
Hybrid Sampling Technique for MRI Image Reconstruction Tanuj Kumar Jhamb
2015
Abstract: The k-space data is obtained from signals generated by Magnetic Resonance Imaging (MRI) scanning machine, and these signals get captured at the radio frequency coils. Accuracy of reconstruction of MRI images involves many factors like data acquisition, data sampling and reconstruction algorithms. This paper investigates the effect of the type of sampling on the accuracy of the reconstruction. In this context, various sampling techniques have been reviewed, and the frequency encoding and the phase encoding for the k-space data have been explained. The related works in image reconstruction like, Sensitivity Encoding approach, SURE approach and signal acquisition protocol have been reviewed. The mechanism of k-space acquisition has been discussed, and a new approach for the k-space sampling has been proposed to improve the sampling of the captured k-space, and hence to provide a better reconstructed image. The performance of the proposed approach is evaluated using mean absol...
A Review on Image Reconstruction through MRI k-Space Data
International Journal of Image, Graphics and Signal Processing, 2015
Image reconstruction is the process of generating an image of an object from the signals captured by the scanning machine. Medical imaging is an interdisciplinary field combining physics, biology, mathematics and computational sciences. This paper provides a complete overview of image reconstruction process in MRI (Magnetic Resonance Imaging). It reviews the computational aspect of medical image reconstruction. MRI is one of the commonly used medical imaging techniques. The data collected by MRI scanner for image reconstruction is called the k-space data. For reconstructing an image from k-space data, there are various algorithms such as Homodyne algorithm, Zero Filling method, Dictionary Learning, and Projections onto Convex Set method. All the characteristics of k-space data and MRI data collection technique are reviewed in detail. The algorithms used for image reconstruction discussed in detail along with their pros and cons. Various modern magnetic resonance imaging techniques like functional MRI, diffusion MRI have also been introduced. The concepts of classical techniques like Expectation Maximization, Sensitive Encoding, Level Set Method, and the recent techniques such as Alternating Minimization, Signal Modeling, and Sphere Shaped Support Vector Machine are also reviewed. It is observed that most of these techniques enhance the gradient encoding and reduce the scanning time. Classical algorithms provide undesirable blurring effect when the degree of phase variation is high in partial k-space. Modern reconstructions algorithms such as Dictionary learning works well even with high phase variation as these are iterative procedures.
New approach for data acquisition and image reconstruction in parallel magnetic resonance imaging
2009
In this study, we propose a novel data acquisition and image reconstruction method for parallel magnetic resonance imaging (MRI). The proposed method improves the GRAPPA algorithm by simultaneously collecting data using the body coil in addition to localized surface coils. The body coil data is included in the GRAPPA reconstruction as an additional coil. The reconstructed body coil image shows greater uniformity over the field of view than the conventional sum-of-squares (SoS) reconstruction that is conventionally used with GRAPPA. The body coil image can also be used to correct for spatial inhomogeneity in the SoS image. The algorithm has been tested using numerical and real MRI phantom data.
Super resolution image reconstruction in parallel magnetic resonance imaging
IEEE ICCA 2010, 2010
In clinical applications, images with high resolution are often desired and required which may provide more details for doctors to make precise diagnosis. In this paper, an approach is proposed to increase image resolution of parallel magnetic resonance imaging. Since different receiver coils have different sensitivity profiles, different receiver channel models are constructed to map the original image information to low resolution images of different channels. Based on these models, the degradation function of every low resolution image can be obtained to compute the high resolution image iteratively using the well known super resolution approach. An in-vivo experiment is also provided to illustrate the feasibility and robustness of the proposed approach. I. INTRODUCTION Modern clinical diagnoses greatly rely on technologies of medical imageology such as computer tomography (CT), magnetic resonance imaging (MRI), etc. The quality of medical images plays very important role in diagnoses. For MRI, in order to increase the spatial resolution of reconstructed images, we may increase the number of phase encoding lines. However, this is at the expense of considerably longer scanning time. Recently, super resolution is widely used to increase the spatial resolution of medical images [9-11]. As a kind of image processing method, it can produce an image with high spatial resolution given a number of low resolution images, which may be translated, blurred, rotated or scaled. In this paper, we investigate the application of super resolution to parallel magnetic resonance imaging (PMRI). Parallel magnetic resonance imaging has been widely used in clinical applications. Since multiple receiver coils are used, acquisition time of PMRI may be significantly reduced. However, the spatial resolution of images may not meet the clinical
A NEW METHOD FOR DATA ACQUISITION AND IMAGE RECONSTRUCTION IN PARALLEL MAGNETIC RESONANCE IMAGING
We propose a novel data acquisition and image reconstruction method for parallel magnetic resonance imaging (MRI). The proposed method improves the GRAPPA (Generalized Auto-calibrating Partially Parallel Acquisitions) method by simultaneously collecting data using the body coil in addition to localized surface coils. The body coil data is included in the GRAPPA reconstruction as an additional coil. The reconstructed body coil image shows greater uniformity over the field of view than the conventional sum-of-squares reconstruction that is conventionally used with GRAPPA. The body coil image can also be used to correct for spatial inhomogeneity in the sum-of-squares image. The proposed method is tested using numerical and real MRI phantom data.
Parallel MRI reconstruction using variance partitioning regularization
Magnetic Resonance in Medicine, 2007
Multiple receivers can be utilized to enhance the spatiotemporal resolution of MRI by employing the parallel imaging technique. Previously, we have reported the L-curve Tikhonov regularization technique to mitigate noise amplification resulting from the geometrical correlations between channels in a coil array. Nevertheless, one major disadvantage of regularized image reconstruction is lengthy computational time in regularization parameter estimation. At a fixed noise level, L-curve regularization parameter estimation was also found not to be robust across repetitive measurements, particularly for low signal-tonoise ratio (SNR) acquisitions. Here we report a computationally efficient and robust method to estimate the regularization parameter by partitioning the variance of the noise-whitened encoding matrix based on the estimated SNR of the aliased pixel set in parallel MRI data. The proposed Variance Partitioning Regularization (VPR) method can improve computational efficiency by 2-5-fold, depending on image matrix sizes and acceleration rates. Our anatomical and functional MRI results show that the VPR method can be applied to both static and dynamic MRI experiments to suppress noise amplification in parallel MRI reconstructions for improved image quality. Magn Reson Med 58:735-744, 2007.
CALIBRATION-LESS MULTI-COIL MR IMAGE RECONSTRUCTION
State-of-the-art parallel MRI techniques either explicitly or implicitly require certain parameters to be estimated, e.g. the sensitivity map for SENSE, SMASH and interpolation weights for GRAPPA, SPIRiT. Thus all these techniques are sensitive to the calibration (parameter estimation) stage. In this work, we have proposed a parallel MRI technique that does not require any calibration but yields reconstruction results that are at par with (or even better than) state-of-the-art methods in parallel MRI. Our proposed method required solving non-convex analysis and synthesis prior joint-sparsity problems. This work also derives the algorithms for solving them. Experimental validation was carried out on two real datasets (8 channel brain and 4 channel UBC Phantom) and one simulated (8 channel Shepp-Logan phantom) dataset. Two sampling methods were used – Variable Density Random sampling and non-Cartesian Radial sampling. For the brain data, acceleration factor of 4 was used and for the others acceleration factor of 6 was used. The reconstruction results were quantitatively evaluated based on the Normalised Mean Squared Error between the reconstructed image and the originals. The qualitative evaluation was based on the actual reconstructed images. Results show that the previous methods (CS SENSE, GRAPPA/GROG and L1SPIRiT) are sensitive to the calibration stage and the reconstruction accuracy varies between 15 to 30%. Our proposed method yields reconstruction results that are at par with (or even better than) the best results obtained from state-of-the-art techniques.
A total variation-based reconstruction method for dynamic MRI
Computational and Mathematical Methods in Medicine, 2008
In recent years, total variation (TV) regularization has become a popular and powerful tool for image restoration and enhancement. In this work, we apply TV minimization to improve the quality of dynamic magnetic resonance images. Dynamic magnetic resonance imaging is an increasingly popular clinical technique used to monitor spatio-temporal changes in tissue structure. Fast data acquisition is necessary in order to capture the dynamic process. Most commonly, the requirement of high temporal resolution is fulfilled by sacrificing spatial resolution. Therefore, the numerical methods have to address the issue of images reconstruction from limited Fourier data. One of the most successful techniques for dynamic imaging applications is the reduced-encoded imaging by generalized-series reconstruction method of Liang and Lauterbur. However, even if this method utilizes a priori data for optimal image reconstruction, the produced dynamic images are degraded by truncation artifacts, most notably Gibbs ringing, due to the spatial low resolution of the data. We use a TV regularization strategy in order to reduce these truncation artifacts in the dynamic images. The resulting TV minimization problem is solved by the fixed point iteration method of Vogel and Oman. The results of test problems with simulated and real data are presented to illustrate the effectiveness of the proposed approach in reducing the truncation artifacts of the reconstructed images.