Field Inhomogeneity Map Research Articles

Free-Breathing Liver Fat, R₂* and B₀ Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-Based Reconstruction.

This work introduced a stack-of-radial multi-echo asymmetric-echo MRI sequence for free-breathing liver volumetric acquisition. Regularized model-based reconstruction was implemented in Berkeley Advanced Reconstruction Toolbox (BART) to jointly estimate all physical parameter maps (water, fat, R2∗ , and B0 field inhomogeneity maps) and coil sensitivity maps from self-gated k -space data. Specifically, locally low rank and temporal total variation regularization were employed directly on physical parameter maps. The proposed free-breathing radial technique was tested on a water/fat & iron phantom, a young volunteer, and obesity/diabetes/hepatic steatosis patients. Quantitative fat fraction and R2∗ accuracy were confirmed by comparing our technique with the reference breath-hold Cartesian scan. The multi-echo radial sampling sequence achieves fast k -space coverage and is robust to motion. Moreover, the proposed motion-resolved model-based reconstruction allows for free-breathing liver fat and R2∗ quantification in multiple motion states. Overall, our proposed technique offers a convenient tool for non-invasive liver assessment with no breath holding requirement.

Estimation of field inhomogeneity map following magnitude-based ambiguity-resolved water-fat separation

Magnitude-based PDFF (Proton Density Fat Fraction) and R2∗ mapping with resolved water-fat ambiguity is extended to calculate field inhomogeneity (field map) using the phase images. The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and R2∗ from magnitude fitting may be updated using the estimated field maps. The limits of quantification of our voxel-independent implementation were assessed. Bland-Altman was used to compare PDFF and field maps from our method against a reference complex-based method on 152 UK Biobank subjects (1.5 T Siemens). A separate acquisition (3 T Siemens) presenting field inhomogeneities was also used. The proposed field mapping was accurate beyond double the complex-based limit range. High agreement was obtained between the proposed method and the reference in UK. Robust field mapping was observed at 3 T, for inhomogeneities over 400 Hz including rapid variation across edges. Field mapping following unambiguous magnitude-based water-fat separation was demonstrated in-vivo and showed potential at 3 T.

Estimation of undistorted images in brain echo-planar images with distortions using the conjugate gradient method with anatomical regularization.

Although echo-planar imaging (EPI) is widely used for diffusion magnetic resonance (MR) imaging, EPI images suffer from susceptibility-induced geometric distortions. We herein propose a new estimation method for undistorted EPI images using anatomical T1 -weighted images (T1 WIs) based on the physics of MR imaging. Our proposed method estimates the undistorted EPI image in the image domain while estimating the magnetic field inhomogeneity map using the conjugate gradient method with anatomical regularization. Our method synthesizes the distorted image to match the measured EPI image containing geometric distortions by alternately updating the undistorted EPI image and the magnetic field inhomogeneity map. We evaluated our proposed method and compared it with a nonrigid registration-based distortion correction method using simulated data and using real data. In the evaluation of the estimation of the magnetic field inhomogeneity map, we used the normalized root-mean-squared error (NRMSE) between the estimated results and the ground truth. In the evaluation of the estimation of undistorted images, we used mutual information (MI) between the undistorted EPI image and the anatomical T1 WI. Using the simulated data, the means and standard deviations of the NRMSE values in the nonrigid registration-based method and proposed method were 1.29±0.63 and 0.64±0.30, respectively. The MI values in the proposed method were larger than those in the nonrigid registration-based method in all evaluated slices. For the real data, the proposed method improved the distortion, and the MI values in the proposed method were larger than those in the nonrigid registration-based method. In the estimation of the magnetic field inhomogeneity map, the NRMSE values in our method were smaller than those in the nonrigid registration-based method. We demonstrated that our proposed method can estimate the regions with compressed distortions that are not well represented by the nonrigid registration-based methods. The results suggest that the proposed method could be useful in analyses combining EPI images with T1 WIs.

Improved Balanced Steady-State Free Precession Based MR Fingerprinting with Deep Autoencoders.

Magnetic Resonance (MR) Fingerprinting is an emerging transient-state imaging paradigm, which enables the quantization of multiple MR tissue parameters in a single experiment. Balanced steady-state free precession (bSSFP)-based MR Fingerprinting has excellent signal-to-noise characteristics and also allows for acquiring both tissue parameter maps and field inhomogeneity maps. However, field inhomogeneity often results in complex magnetization evolutions in bSSFP-based MR Fingerprinting, which creates significant challenges in image reconstruction. In this paper, we introduce a new method to address the image reconstruction problem. The proposed method incorporates a low-dimensional nonlinear manifold learned from an ensemble of magnetization evolutions using a deep autoencoder. It provides much better representation power than a low-dimensional linear subspace in capturing complex magnetization evolutions. We formulate the image reconstruction problem with this nonlinear model and solve the resulting optimization problem using an algorithm based on variable splitting and the alternating direction method of multipliers. We evaluate the performance of the proposed method using numerical experiments and demonstrate that it significantly outperforms the state-of-art method using a linear subspace model.

An improved homogeneity design method for fast field-cycling coils in molecular MRI.

Delta relaxation-enhanced MR (dreMR) is a field-cycling quantitative method for molecular imaging. The dreMR method uses a B0 insert coil to shift the magnitude of the main magnetic field as a magnetization preparation phase of the pulse sequence. Here, an improved coil design method is presented that minimizes field inhomogeneities and allows for explicit control of the ROI. A solenoid produces the bulk field shift, and a boundary element method is employed to design in-series shim and shield layers. A design is presented and compared to the current generation dreMR coil design on field inhomogeneity maps, shield performance, and simulated dreMR image. A proof-of-concept design is also presented with an ROI shifted from isocenter. The new design is able to image a sphere of 8.5cm in diameter with field inhomogeneity of < 1% versus the previous design's 5cm. The new design presented an increase in shielding capabilities, whereas inductance and resistance increased. For a simulated dreMR image, the new design presented errors < 10% compared to an ideal field simulation, whereas the previous design had errors > 18%. The shifted ROI design produced a region of < 1% inhomogeneity much larger than a design with no shim layer. The new design method was found to greatly improve the insert coil field homogeneity and reduce errors in dreMR imaging in simulation without detriment to shielding. This method's capability to increase ROI and control its location will be used to design human dreMR coils going forward.

Simultaneous MR quantification of hepatic fat content, fatty acid composition, transverse relaxation time and magnetic susceptibility for the diagnosis of non-alcoholic steatohepatitis.

Non-alcoholic steatohepatitis (NASH) is characterized at histology by steatosis, hepatocyte ballooning and inflammatory infiltrates, with or without fibrosis. Although diamagnetic material in fibrosis and inflammation can be detected with quantitative susceptibility imaging, fatty acid composition changes in NASH relative to simple steatosis have also been reported. Therefore, our aim was to develop a single magnetic resonance (MR) acquisition and post-processing scheme for the diagnosis of steatohepatitis by the simultaneous quantification of hepatic fat content, fatty acid composition, T2 * transverse relaxation time and magnetic susceptibility in patients with non-alcoholic fatty liver disease. MR acquisition was performed at 3.0T using a three-dimensional, multi-echo, spoiled gradient echo sequence. Phase images were unwrapped to compute the B0 field inhomogeneity (ΔB0 ) map. The ΔB0 -demodulated real part images were used for fat-water separation, T2 * and fatty acid composition quantification. The external and internal fields were separated with the projection onto dipole field method. Susceptibility maps were obtained after dipole inversion from the internal field map with single-orientation Bayesian regularization including spatial priors. Method validation was performed in 32 patients with biopsy-proven, non-alcoholic fatty liver disease from which 12 had simple steatosis and 20 NASH. Liver fat fraction and T2 * did not change significantly between patients with simple steatosis and NASH. In contrast, the saturated fatty acid fraction increased in patients with NASH relative to patients with simple steatosis (48±2% versus 44±4%; p<0.05) and the magnetic susceptibility decreased (-0.30±0.27ppm versus 0.10±0.14ppm; p<0.001). The area under the receiver operating characteristic curve for magnetic susceptibility as NASH marker was 0.91 (95% CI: 0.79-1.0). Simultaneous MR quantification of fat content, fatty acid composition, T2 * and magnetic susceptibility is feasible in the liver. Our preliminary results suggest that quantitative susceptibility imaging has a high diagnostic performance for the diagnosis of NASH.

Characterization of viscosupplementation formulations using chemical exchange saturation transfer (ViscoCEST).

BackgroundOsteoarthritis (OA) is characterized by progressive loss of cartilage in joints, and is a major cause of pain and disability, and imposes significant health care expense. New therapies are being developed to treat the symptomatic effect of OA, one of which is intra-articular injection of viscosupplementations of different forms of hyaluronic acid (HA). The current study evaluates the chemical exchange saturation transfer (CEST) effect from two popular viscosupplementations [Hylan gf-20 (Synvisc) and hyaluronan (Orthovisc)] by targeting the exchangeable hydroxyl protons present on these molecules (ViscoCEST).MethodsViscoCEST imaging from two viscosupplementations (Synvisc and Orthovisc) was performed on a 7T Siemens whole body MRI scanner. ViscoCEST images were collected with different combination of saturation pulse power and saturation duration. Z spectra were acquired at B1rms of 3.6 μT and 1 s saturation duration by varying the frequency from −4 to +4 ppm in step size of 0.1 ppm. Field inhomogeneity (B0) and radiofrequency (B1) maps were also acquired to correct ViscoCEST contrast map for any inhomogeneity.ResultsBoth viscosupplementations showed broad CEST effect (ViscoCEST), which peaked ~0.8 ppm from down field of water resonance. Orthovisc showed 20 % higher ViscoCEST contrast than Synvisc suggestive of more HA component in Orthovisc. Increased ViscoCEST contrast was observed from both viscosupplementations with increase in B1rms and saturation pulse duration.ConclusionViscoCEST has a potential to image the spatial distribution of viscosupplements in vivo in patients’ intra-articular space as well as temporal variation in their spatial distribution.

Image‐based estimation method for field inhomogeneity in brain echo‐planar images with geometric distortion using k‐space textures

ABSTRACTEcho‐planar imaging (EPI) can suffer from geometrical distortion due to magnetic field inhomogeneity. To correct the geometric distortions in EPI, a magnetic field map is used. Our purpose was to develop a novel image‐based method for estimating the field inhomogeneity map from the distorted EPI image and T1‐weighted image of the brain using k‐space textures. Based on magnetic resonance imaging physics, our method synthesizes the distorted image to match the measured EPI image through the generating process of EPI image by updating the estimated field inhomogeneity map. The estimation process was performed to minimize the cost function, which was defined by the synthesized EPI image and the measured EPI image with geometric distortion, using an iterative conjugate gradient algorithm. The proposed method was applied to simulation and human data. To evaluate the performance of the proposed method quantitatively, we used the normalized root mean square error (NRMSE) between the ground truth and the results estimated by our proposed method. In simulation data, the values of the NRMSE between the ground truth and the estimated field inhomogeneity map were &lt;0.08. In both simulation and human data, the estimated EPI images were very similar to input EPI images, and the NRMSE values between them were &lt;0.09. The results of the simulated and human data demonstrated that our method produced a reasonable estimation of the field inhomogeneity map. The estimated map could be used for distortion correction in EPI images. © 2015 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 45B: 142–152, 2015

Volume of interest-based fourier transform method for calculation of static magnetic field maps from susceptibility distributions.

Calculation of static magnetic field (B0 ) inhomogeneity maps of high-resolution susceptibility models by means of convolution with dipole kernels often encounters limitations in computer memory (RAM) for large input data matrices. In many applications, only a small portion of the full volume of the computer model is a volume of interest (VOI) or a susceptibility perturbation source. This work presents a VOI-based method to significantly reduce the computer memory usage for such applications. The VOI-based method is presented and compared with the conventional method for calculation of the B0 field in the brain and heart of a human body model in terms of calculation speed, memory requirement, and calculation error relative to the full model results. Use of the VOI-based method significantly reduced memory usage in the human body model calculations over the conventional method without loss of accuracy and with comparable calculation speed. The proposed method can be valuable for rapid calculation of B0 distributions on standard computer hardware for applications such as subject-specific B0 field calculations derived from anatomic scans. Magn Reson Med 75:2473-2480, 2016. © 2015 Wiley Periodicals, Inc.

Susceptibility artefact correction using dynamic graph cuts: application to neurosurgery.

Echo Planar Imaging (EPI) is routinely used in diffusion and functional MR imaging due to its rapid acquisition time. However, the long readout period makes it prone to susceptibility artefacts which results in geometric and intensity distortions of the acquired image. The use of these distorted images for neuronavigation hampers the effectiveness of image-guided surgery systems as critical white matter tracts and functionally eloquent brain areas cannot be accurately localised. In this paper, we present a novel method for correction of distortions arising from susceptibility artefacts in EPI images. The proposed method combines fieldmap and image registration based correction techniques in a unified framework. A phase unwrapping algorithm is presented that can efficiently compute the B0 magnetic field inhomogeneity map as well as the uncertainty associated with the estimated solution through the use of dynamic graph cuts. This information is fed to a subsequent image registration step to further refine the results in areas with high uncertainty. This work has been integrated into the surgical workflow at the National Hospital for Neurology and Neurosurgery and its effectiveness in correcting for geometric distortions due to susceptibility artefacts is demonstrated on EPI images acquired with an interventional MRI scanner during neurosurgery.

Radiofrequency field inhomogeneity compensation in high spatial resolution magnetic resonance spectroscopic imaging

Clinical magnetic resonance spectroscopy imaging (MRSI) is a non-invasive functional technique, whose mathematical framework falls into the category of linear inverse problems. However, its use in medical diagnostics is hampered by two main problems, both linked to the Fourier-based technique usually implemented for spectra reconstruction: poor spatial resolution and severe blurring in the spatial localization of the reconstructed spectra. Moreover, the intrinsic ill-posedness of the MRSI problem might be worsened by (i) spatially dependent distortions of the static magnetic field (B0) distribution, as well as by (ii) inhomogeneity in the power deposition distribution of the radiofrequency magnetic field (B1). Among several alternative methods, slim (Spectral Localization by IMaging) and bslim (B0 compensated slim) are reconstruction algorithms in which a priori information concerning the spectroscopic target is introduced into the reconstruction kernel. Nonetheless, the influence of the B1 field, particularly when its operating wavelength is close to the size of the human organs being studied, continues to be disregarded. starslim (STAtic and Radiofrequency-compensated slim), an evolution of the slim and bslim methods, is therefore proposed, in which the transformation kernel also includes the B1 field inhomogeneity map, thus allowing almost complete 3D modelling of the MRSI problem. Moreover, an original method for the experimental determination of the B1 field inhomogeneity map specific to the target under evaluation is also included. The compensation capabilities of the proposed method have been tested and illustrated using synthetic raw data reproducing the human brain.

Data-Driven MRSI Spectral Localization Via Low-Rank Component Analysis

Magnetic resonance spectroscopic imaging (MRSI) is a powerful tool capable of providing spatially localized maps of metabolite concentrations. Its utility, however, is often depreciated by spectral leakage artifacts resulting from low spatial resolution measurements through an effort to reduce acquisition times. Though model-based techniques can help circumvent these drawbacks, they require strong prior knowledge, and can introduce additional artifacts when the underlying models are inaccurate. We introduce a novel scheme in which a generative model is estimated from the raw MRSI data via a regularized variational framework that minimizes the model approximation error within a measurement-prescribed subspace. As additional a priori information, our approach relies only upon a measured field inhomogeneity map at high spatial resolution. We demonstrate the feasibility of our approach on both synthetic and experimental data.

Patient-induced susceptibility effect on geometric distortion of clinical brain MRI for radiation treatment planning on a 3T scanner

Concerns about the geometric accuracy of MRI in radiation therapy (RT) have been present since its invention. Although modern scanners typically have system levels of geometric accuracy that meet requirements of RT, subject-specific distortion is variable, and methods to in vivo assess and control patient-induced geometric distortion are not yet resolved. This study investigated the nature and magnitude of the subject-induced susceptibility effect on geometric distortions in clinical brain MRI, and tested the feasibility of in vivo quality control using field inhomogeneity mapping. For 19 consecutive patients scanned on a dedicated 3T MR scanner, B0 field inhomogeneity maps were acquired and analyzed to determine subject-induced distortions. For 3D T1 weighted images frequency-encoded with a bandwidth of 180 Hz/pixel, 86.9% of the estimated displacements were <0.5 mm, 97.4% <1 mm, and only 0.1% of displacements > 2 mm. The maximum displacement was <4 mm. The greatest distortions were observed at the interfaces with air at the sinuses. Displacements decayed to less than 1 mm over a distance of 8 mm. Metal surgical wires generated smaller distortions, with an averaged maximum displacement of 0.76 mm. Repeat acquisition of the field maps in 17 patients revealed a within-subject standard deviation of 0.25 ppm, equivalent to 0.22 mm displacement in the frequency-encoding direction in the 3D T1 weighted images. Susceptibility-induced voxel displacements in the brain are generally small, but should be monitored for precision RT. These effects are manageable at 3T and lower fields, and the methods applied can be used to monitor for potential local errors in individual patients, as well as to correct for local distortions as needed.

Diffusion-tensor MRI reveals the complex muscle architecture of the human forearm

Purpose: To design a time-efficient patient-friendly clinical diffusion tensor MRI protocol and postprocessing tool to study the complex muscle architecture of the human forearm. Materials and Methods: The 15-minute examination was done using a 3 T system and consisted of: T1-weighted imaging, dual echo gradient echo imaging, single-shot spin-echo echo-planar imaging (EPI) diffusion tensor MRI. Postprocessing comprised of signal-to-noise improvement by a Rician noise suppression algorithm, image registration to correct for motion and eddy currents, and correction of susceptibility-induced deformations using magnetic field inhomogeneity maps. Per muscle one to five regions of interest were used for fiber tractography seeding. To validate our approach, the reconstructions of individual muscles from the in vivo scans were compared to photographs of those dissected from a human cadaver forearm. Results: Postprocessing proved essential to allow muscle segmentation based on combined T1-weighted and diffusion tensor data. The protocol can be applied more generally to study human muscle architecture in other parts of the body. Conclusion: The proposed protocol was able to visualize the muscle architecture of the human forearm in great detail and showed excellent agreement with the dissected cadaver muscles. J. Magn. Reson. Imaging 2012. © 2012 Wiley Periodicals, Inc.

Diffusion‐tensor MRI reveals the complex muscle architecture of the human forearm

To design a time-efficient patient-friendly clinical diffusion tensor MRI protocol and postprocessing tool to study the complex muscle architecture of the human forearm. The 15-minute examination was done using a 3 T system and consisted of: T(1) -weighted imaging, dual echo gradient echo imaging, single-shot spin-echo echo-planar imaging (EPI) diffusion tensor MRI. Postprocessing comprised of signal-to-noise improvement by a Rician noise suppression algorithm, image registration to correct for motion and eddy currents, and correction of susceptibility-induced deformations using magnetic field inhomogeneity maps. Per muscle one to five regions of interest were used for fiber tractography seeding. To validate our approach, the reconstructions of individual muscles from the in vivo scans were compared to photographs of those dissected from a human cadaver forearm. Postprocessing proved essential to allow muscle segmentation based on combined T(1) -weighted and diffusion tensor data. The protocol can be applied more generally to study human muscle architecture in other parts of the body. The proposed protocol was able to visualize the muscle architecture of the human forearm in great detail and showed excellent agreement with the dissected cadaver muscles.

Robust Reconstruction of MRSI Data Using a Sparse Spectral Model and High Resolution MRI Priors

We introduce a novel algorithm to address the challenges in magnetic resonance (MR) spectroscopic imaging. In contrast to classical sequential data processing schemes, the proposed method combines the reconstruction and postprocessing steps into a unified algorithm. This integrated approach enables us to inject a range of prior information into the data processing scheme, thus constraining the reconstructions. We use high resolution, 3-D estimate of the magnetic field inhomogeneity map to generate an accurate forward model, while a high resolution estimate of the fat/water boundary is used to minimize spectral leakage artifacts. We parameterize the spectrum at each voxel as a sparse linear combination of spikes and polynomials to capture the metabolite and baseline components, respectively. The constrained model makes the problem better conditioned in regions with significant field inhomogeneity, thus enabling the recovery even in regions with high field map variations. To exploit the high resolution MR information, we formulate the problem as an anatomically constrained total variation optimization scheme on a grid with the same spacing as the magnetic resonance imaging data. We analyze the performance of the proposed scheme using phantom and human subjects. Quantitative and qualitative comparisons indicate a significant improvement in spectral quality and lower leakage artifacts.

Toward a practical protocol for human optic nerve DTI with EPI geometric distortion correction

To develop a practical protocol for diffusion tensor imaging (DTI) of the human optic nerve with echo planar imaging (EPI) geometric distortion correction. A conventional DTI protocol was modified to acquire images with fat and cerebrospinal fluid (CSF) suppression and field inhomogeneity maps of contiguous coronal slices covering the whole brain. The technique was applied to healthy volunteers and multiple sclerosis patients with and without a history of unilateral optic neuritis. DTI measures and optic nerve tractography before and after geometric distortion correction were compared. Diffusion measures from left and right or from affected and unaffected eyes in different subject cohorts were reported. The image geometry after correction closely resembled reference anatomical images. Optic nerve tractography became feasible after distortion correction. The diffusion measures from the healthy volunteers were in good agreement with the literature. Statistically significant differences were found in the fractional anisotropy and orthogonal eigenvalues between affected and unaffected eyes in optic neuritis patients with poor recovery. The diffusion measures before and after geometric distortion correction were not significantly different. For cohorts without optic neuritis, the difference between diffusion measures from left and right eyes was not statistically significant. The proposed technique could provide a practical DTI protocol to study the human optic nerve.

Two‐point dixon fat–water separation: Improving reliability and accuracy in phase correction algorithms

To propose an advanced phase-correction region-growing algorithm for two-point fat-water separation suitable for parotid assessment, and to evaluate the general performance of phase-correction algorithms. Two region-growing algorithms were evaluated in test objects and in head images: the original phase-correction algorithm (OPC) and the advanced phase-correction algorithm with voxel size manipulation (VSM) which includes: 1) starting the region-growing process from images of lower resolution and subsequently stepping toward the original matrix size, and 2) limiting the use of low-pass filters to fat-water interfaces with partial volume effects Fundamental problems relate to biological tissue spectrum being poorly approximated by two discrete peaks for fat and water. The VSM algorithm was shown to be less noise-sensitive, faster, and to produce a better approximation for the field inhomogeneity map. In head images (6 volunteers, 10 slices each) 43 errors were found with the OPC algorithm and only 6 errors with the VSM algorithm. Only the OPC algorithm produced errors surrounding the parotids (10 errors). The VSM algorithm provides a more accurate and less noise-sensitive fat-water separation. This highly significant performance improvement allows the application of phase-correction algorithms to a wider range of clinical applications.

Joint estimation of water/fat images and field inhomogeneity map

Water/fat separation in the presence of B 0 field inhomogeneity is a problem of considerable practical importance in MRI. This article describes two complementary methods for estimating the water/fat images and the field inhomogeneity map from Dixon-type acquisitions. One is based on variable projection (VARPRO) and the other on linear prediction (LP). The VARPRO method is very robust and can be used in low signal-to-noise ratio conditions because of its ability to achieve the maximum-likelihood solution. The LP method is computationally more efficient, and is shown to perform well under moderate levels of noise and field inhomogeneity. These methods have been extended to handle multicoil acquisitions by jointly solving the estimation problem for all the coils. Both methods are analyzed and compared and results from several experiments are included to demonstrate their performance.

BSLIM: Spectral Localization by Imaging With Explicit $B_{0}$ Field Inhomogeneity Compensation

Magnetic resonance spectroscopy imaging (MRSI) is an attractive tool for medical imaging. However, its practical use is often limited by the intrinsic low spatial resolution and long acquisition time. Spectral localization by imaging (SLIM) has been proposed as a non-Fourier reconstruction algorithm that incorporates spatial a priori information about spectroscopically uniform compartments. Unfortunately, the influence of the magnetic field inhomogeneity--in particular, the susceptibility effects at tissues' boundaries--undermines the validity of the compartmental model. Therefore, we propose BSLIM as an extension of SLIM with field inhomogeneity compensation. A B0-field inhomogeneity map, which can be acquired rapidly and at high resolution, is used by the new algorithm as additional a priori information. We show that the proposed method is distinct from the generalized SLIM (GSLIM) framework. Experimental results of a two-compartment phantom demonstrate the feasibility of the method and the importance of inhomogeneity compensation.