Robust selective weighted field mapping using multi-echo gradient echo-based MRI

In 3D gradient echo (GRE) and echo planar imaging (EPI), strong macroscopic field gradients are observed at air/tissue interfaces. The respective field gradients lead to an apparent increase in intravoxel dephasing, and, subsequently, to signal loss or image distortion. We propose an analytical approximation and a consequent method to compute low and high resolution field maps over all field map regimes (small and large echo spacing). A number of approaches which compute field maps from reconstructed phase data rely upon optimized linear least square fit and complex division approaches owing to the simplicity of their implementation. Most of these techniques, however, have historically considered only the phase signal when computing off-resonance maps while ignoring magnitude data. This latter may be of notable interest since the presence of noise is well depicted and interpreted. The presence of noise and phase aliasing that increase with increasing echo time (TE) and echo spacing (ΔTE) may seriously challenge the off-resonance map accuracy. These techniques still remain subject to the trade-off during the choice of GRE sequences, TE and ΔTE. In this work, we explore a novel model that considers any type of TE and ΔTE regime (small or large) and high phase wraps complexity. The field offset is weighted by the magnitude signal decay quality, to make the field mapping procedure as noise independent as possible. The performance of the proposed method was tested using simulated, experimental phantoms and in vivo human studies. The proposed approach markedly outperforms conventional techniques. It provides a correction equivalent to that of the conventional techniques in regions with high SNR (20), yielding a mean error of about 0.1 Hz, but appearing more robust in regions with low SNR (10), such as near the sinus cavity and at the very edge of the brain (mean error less than 1 Hz), where phase wraps and noise are highly present. The proposed technique shows promise to enhance field map generation over any acquisition regime and in regions of both high and low SNR and it can be easily implemented for rapid computation and used in a clinical setting

Fast and accurate pseudo multispectral technique for whole-brain MRI tissue classification

Numerous strategies have been proposed to classify brain tissues into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). However, many of them fail when classifying specific regions with low contrast between tissues. In this work, we propose an alternative pseudo multispectral classification (PMC) technique using CIE LAB spaces instead of gray scale T1-weighted MPRAGE images, combined with a new preprocessing technique for contrast enhancement and an optimized iterative K-means clustering. To improve the accuracy of the classification process, gray scale images were converted to multispectral CIE LAB data by applying several transformation matrices. Thus, the amount of information associated with each image voxel was increased. The image contrast was then enhanced by applying a real time function that separates brain tissue distributions and improve image contrast in certain brain regions. The data were then classified using an optimized iterative and convergent K-means classifier. The performance of the proposed approach was assessed using simulation and in vivo human studies through comparison with three common software packages used for brain MR image segmentation, namely FSL, SPM8 and K-means clustering. In the presence of high SNR, the results showed that the four algorithms achieve a good classification. Conversely, in the presence of low SNR, PMC was shown to outperform the other methods by accurately recovering all tissue volumes. The quantitative assessment of brain tissue classification for simulated studies showed that the PMC algorithm resulted in a mean Jaccard index (JI) of 0.74 compared to 0.75 for FSL, 0.7 for SPM and 0.8 for K-means. The in vivo human studies showed that the PMC algorithm resulted in a mean JI of 0.92, which reflects a good spatial overlap between segmented and actual volumes, compared to 0.84 for FSL, 0.78 for SPM and 0.66 for K-means. The proposed algorithm presents a high potential for improving the accuracy of automatic brain tissues classification and was found to be accurate even in the presence of high noise level

Mixed model phase evolution for correction of magnetic field inhomogeneity effects in 3D quantitative gradient echo-based MRI

Purpose: In 3D gradient echo magnetic resonance imaging (MRI), strong field gradients B0(macro) are visually observed at air/tissue interfaces. At low spatial resolution in particular, the respective field gradients lead to an apparent increase in intravoxel dephasing, and subsequently, to signal loss or inaccurate R-2* estimates. If the strong field gradients are measured, their influence can be removed by postprocessing. Methods: Conventional corrections usually assume a linear phase evolution with time. For high macroscopic gradient inhomogeneities near the edge of the brain and at the paranasal sinuses, however, this assumption is often broken. Herein, we explored a novel model that considers both linear and stochastic dependences of the phase evolution with echo time in the presence of weak and strong macroscopic field inhomogeneities. We tested the performance of the model at large field gradients using simulation, phantom, and human in vivo studies. Results: The performance of the proposed approach was markedly better than the standard correction method, providing a correction equivalent to that of the conventional approach in regions with high signal to noise ratio (SNR > 10), but appearing more robust in regions with low SNR (SNR <4). Conclusion: The proposed technique shows promise to improve R-2* measurements in regions of large susceptibilities. The clinical and research applications still require further investigation

Non Linear Phase model for correction of magnetic field inhomogeneity Effects in Quantitative gradient Echo based MRI

Non Linear Phase model for correction of magnetic eld inhomogeneity E ects in Quantitative gradient Echo based MRIR2* plays an important role in the quantitative evaluation of the brain function and tissue iron content. Unfortunately, susceptibility induced macroscopic eld inho- mogeneities B0macro across an image voxel act to increase the R2 * in a gradient echo image. If these B0macro are measurable their in uence can be removed in post processing. Conventionally, these algorithms assume the phase evolves linearly with time ; however, in the presence of a large B0macro, e.g. near the edge of the brain, this is assumption is broken , .The phase evolution appears random. In this work, we hypothesize that the phase evolution in the presence of large B0macro, can be modeled and corrected using 1D-random-walk theory

Non Linear Phase model for correction of magnetic field inhomogeneity Effects in Quantitative gradient Echo based MRI

Non Linear Phase model for correction of magnetic eld inhomogeneity E ects in Quantitative gradient Echo based MRIR2* plays an important role in the quantitative evaluation of the brain function and tissue iron content. Unfortunately, susceptibility induced macroscopic eld inho- mogeneities B0macro across an image voxel act to increase the R2 * in a gradient echo image. If these B0macro are measurable their in uence can be removed in post processing. Conventionally, these algorithms assume the phase evolves linearly with time ; however, in the presence of a large B0macro, e.g. near the edge of the brain, this is assumption is broken , .The phase evolution appears random. In this work, we hypothesize that the phase evolution in the presence of large B0macro, can be modeled and corrected using 1D-random-walk theory