Removal of partial Fourier-induced Gibbs (RPG) ringing artifacts in MRI.

PURPOSE: To investigate and remove Gibbs-ringing artifacts caused by partial Fourier (PF) acquisition and zero filling interpolation in MRI data. THEORY AND METHODS: Gibbs ringing of fully sampled data, leading to oscillations around tissue boundaries, is caused by the symmetric truncation of k-space. Such ringing can be removed by conventional methods, with the local subvoxel shifts method being the state-of-the-art. However, the asymmetric truncation of k-space in routinely used PF acquisitions leads to additional ringings of wider intervals in the PF sampling dimension that cannot be corrected solely based on magnitude images reconstructed via zero filling. Here, we develop a pipeline for the Removal of PF-induced Gibbs ringing (RPG) to remove ringing patterns of different periods by applying the conventional method twice. The proposed pipeline is validated on numerical phantoms, demonstrated on in vivo diffusion MRI measurements, and compared with the conventional method and neural network-based approach. RESULTS: For PF = 7/8 and 6/8, Gibbs-ringings and subsequent bias in diffusion metrics induced by PF acquisition and zero filling are robustly removed by using the proposed RPG pipeline. For PF = 5/8, however, ringing removal via RPG leads to excessive image blurring due to the interplay of image phase and convolution kernel. CONCLUSIONS: RPG corrects Gibbs-ringing artifacts in magnitude images of PF acquired data and reduces the bias in quantitative MR metrics. Considering the benefit of PF acquisition and the feasibility of ringing removal, we suggest applying PF = 6/8 when PF acquisition is necessary.

[Magnitude image-guided phase unwrapping algorithm of susceptibility weighted images].

To better use the phase information to compensate the influence of blood flow, the phase unwrapping problem in susceptibility weighted imaging (SWI) is studied in this paper. In order to improve the accuracy of unwrapping, this paper proposes a magnitude image-guided phase unwrapping algorithm of SWI. The basic idea is as follows: (1) reduce the influence of noise by improving the rotational invariant non-local principal component analysis method (PRI-NL-PCA); (2) extract the corresponding solid region in the phase image to avoid the influence of the background noise on the phase unwrapping method; (3) use the phase compensation method to constrain the phase image reconstructed by the K-space. Finally, the reliability of the unwrapping method is evaluated by using four kinds of statistics as quantification index: the number, mean (M), variance (Var), and positive percentage (Pos) and negative percentage (Neg) of phasic error points. By comparing the simulated data with 226 sets of true head SWI data, the results show that the proposed algorithm has high accuracy compared with the classical branch cut method and the least squares method.

A fast algorithm for denoising magnitude diffusion-weighted images with rank and edge constraints.

PURPOSE: To accelerate denoising of magnitude diffusion-weighted images subject to joint rank and edge constraints. METHODS: We extend a previously proposed majorize-minimize method for statistical estimation that involves noncentral χ distributions to incorporate joint rank and edge constraints. A new algorithm is derived which decomposes the constrained noncentral χ denoising problem into a series of constrained Gaussian denoising problems each of which is then solved using an efficient alternating minimization scheme. RESULTS: The performance of the proposed algorithm has been evaluated using both simulated and experimental data. Results from simulations based on ex vivo data show that the new algorithm achieves about a factor of 10 speed up over the original Quasi-Newton-based algorithm. This improvement in computational efficiency enabled denoising of large datasets containing many diffusion-encoding directions. The denoising performance of the new efficient algorithm is found to be comparable to or even better than that of the original slow algorithm. For an in vivo high-resolution Q-ball acquisition, comparison of fiber tracking results around hippocampus region before and after denoising will also be shown to demonstrate the denoising effects of the new algorithm. CONCLUSION: The optimization problem associated with denoising noncentral χ distributed diffusion-weighted images subject to joint rank and edge constraints can be solved efficiently using a majorize-minimize-based algorithm.

Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: reducing the noise floor using SENSE.

PURPOSE: To examine the effects of the reconstruction algorithm of magnitude images from multichannel diffusion MRI on fiber orientation estimation. THEORY AND METHODS: It is well established that the method used to combine signals from different coil elements in multichannel MRI can have an impact on the properties of the reconstructed magnitude image. Using a root-sum-of-squares approach results in a magnitude signal that follows an effective noncentral-χ distribution. As a result, the noise floor, the minimum measurable in the absence of any true signal, is elevated. This is particularly relevant for diffusion-weighted MRI, where the signal attenuation is of interest. RESULTS: In this study, we illustrate problems that such image reconstruction characteristics may cause in the estimation of fiber orientations, both for model-based and model-free approaches, when modern 32-channel coils are used. We further propose an alternative image reconstruction method that is based on sensitivity encoding (SENSE) and preserves the Rician nature of the single-channel, magnitude MR signal. We show that for the same k-space data, root-sum-of-squares can cause excessive overfitting and reduced precision in orientation estimation compared with the SENSE-based approach. CONCLUSION: These results highlight the importance of choosing the appropriate image reconstruction method for tractography studies that use multichannel receiver coils for diffusion MRI acquisition.

Understanding the morphological mismatch between magnetic susceptibility source and t2* image.

BACKGROUND AND PURPOSE: Recent research has shown that a T2* image (either magnitude or phase) is not identical to the internal spatial distribution of a magnetic susceptibility (χ) source. In this paper, we examine the reasons behind these differences by looking into the insights of T2*-weighted magnetic resonance imaging (T2*MRI) and provide numerical characterizations of the source/image mismatches by numerical simulations. METHODS: For numerical simulations of T2*MRI, we predefine a 3D χ source and calculate the complex-valued T2* image by intravoxel dephasing in presence and absence of diffusion. We propose an empirical α-power model to describe the overall source/image transformation. For a Gaussian-shaped χ source, we numerically characterize the source/image morphological mismatch in terms of spatial correlation and FWHM (full width at half maximum). RESULTS: In theory, we show that the χ-induced fieldmap is morphologically different from the χ source due to dipole effect, and the T2* magnitude image is related to the fieldmap by a quadratic transformation in the small phase angle regime, which imposes an additional morphological change. The numerical simulations with a Gaussian-shaped χ source show that a T2* magnitude image may suffer an overall source/image morphological shrinkage of 20% to 25% and that the T2* phase image is almost identical to the fieldmap thus maintaining a morphological mismatch from the χ source due to dipole effect. CONCLUSION: The morphological mismatch between a bulk χ source and its T2* image is caused by the 3D convolution during tissue magnetization (dipole effect), the nonlinearity of the T2* magnitude and phase calculation, and the spin diffusion effect. In the small phase angle regime, the T2* magnitude exhibits an overall morphological shrinkage, and the T2* phase image suffers a dipole effect but maintains the χ-induced fieldmap morphology.