Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI

Evaluation of Six Phase Encoding Based Susceptibility Distortion Correction Methods for Diffusion MRI

Purpose: Susceptibility distortions impact diffusion MRI data analysis and is typically corrected during preprocessing. Correction strategies involve three classes of methods: registration to a structural image, the use of a fieldmap, or the use of images acquired with opposing phase encoding directions. It has been demonstrated that phase encoding based methods outperform the other two classes, but unfortunately, the choice of which phase encoding based method to use is still an open question due to the absence of any systematic comparisons.

Methods: In this paper we quantitatively evaluated six popular phase encoding based methods for correcting susceptibility distortions in diffusion MRI data. We employed a framework that allows for the simulation of realistic diffusion MRI data with susceptibility distortions. We evaluated the ability for methods to correct distortions by comparing the corrected data with the ground truth. Four diffusion tensor metrics (FA, MD, eigenvalues and eigenvectors) were calculated from the corrected data and compared with the ground truth. We also validated two popular indirect metrics using both simulated data and real data. The two indirect metrics are the difference between the corrected LR and AP data, and the FA standard deviation over the corrected LR, RL, AP, and PA data.

Results: We found that DR-BUDDI and TOPUP offered the most accurate and robust correction compared to the other four methods using both direct and indirect evaluation metrics. EPIC and HySCO performed well in correcting b₀ images but produced poor corrections for diffusion weighted volumes, and also they produced large errors for the four diffusion tensor metrics. We also demonstrate that the indirect metric (the difference between corrected LR and AP data) gives a different ordering of correction quality than the direct metric.

Conclusion: We suggest researchers to use DR-BUDDI or TOPUP for susceptibility distortion correction. The two indirect metrics (the difference between corrected LR and AP data, and the FA standard deviation) should be interpreted together as a measure of distortion correction quality. The performance ranking of the various tools inferred from direct and indirect metrics differs slightly. However, across all tools, the results of direct and indirect metrics are highly correlated indicating that the analysis of indirect metrics may provide a good proxy of the performance of a correction tool if assessment using direct metrics is not feasible.

Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines

The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

A Web-Based Educational Magnetic Resonance Simulator: Design, Implementation and Testing

A new web-based education-oriented magnetic resonance (MR) simulator is presented. We have identified the main requirements that this simulator should comply with, so that trainees can face useful practical tasks such as setting the exact slice position and its properties, selecting the correct protocol or fitting the parameters to acquire an image. The tool follows the client-server model. The client contains the interface that mimics the console of a real machine and several of its features. The server stores anatomical models and executes the bulk of the simulation. This cross-platform simulator has been used in two real educational scenarios. The acceptance of the tool has been measured using two criteria, namely, the System Usability Scale and the Likelihood to Recommend, both with satisfactory results. Therefore, we conclude that given the potential of the tool, it may play a relevant role for the training of MRI operators and other involved personnel.

FOD-based registration for susceptibility distortion correction in brainstem connectome imaging

The high resolution, multi-shell diffusion MRI (dMRI) data from the Human Connectome Project (HCP) provides a great opportunity to map fine-grained fiber pathways in human brainstem, but the severe susceptibility-induced distortion around the brainstem poses a significant challenge. While the correction tools used in the HCP Pipeline greatly reduce the distortion artifacts in the preprocessed data, significant residual distortions are still widely present, especially in the brainstem region. One fundamental reason is that the topup tool used in the HCP Pipeline only relies on the B0 images, which lack sufficient contrast about white matter pathways, to estimate the distortion displacement between opposite phase encodings (PEs). To fully utilize the rich information of HCP data that includes dMRI data from two opposite PEs, we compute the fiber orientation distributions (FODs) from the data of each PE and propose a novel method to estimate and correct the residual distortion using FOD-based registration. Using the dMRI data of 94 HCP subjects, we show quantitatively that our method can reduce the misalignment of main fiber direction in the brainstem by 21% as compared to the topup tool used in the HCP Pipeline. Our method is fully compatible with the HCP Pipeline and thus can be readily integrated with it to enhance distortion correction in connectome imaging research.

SEGUE: A Speedy rEgion-Growing Algorithm for Unwrapping Estimated Phase

Recent magnetic resonance imaging (MRI) techniques, such as quantitative magnetic susceptibility mapping, employ the signal phase to reveal disease-related changes in tissue composition, including iron or calcium content. The MRI phase is also routinely used in functional and diffusion MRI for distortion correction. However, phase images are wrapped into a range of 2π radians. Phase region expanding labeller for unwrapping discrete estimates (PRELUDE) is the gold standard method for robust, spatial, 3-D, MRI phase unwrapping. Unfortunately, PRELUDE's computation time can reach 15 min for a severely wrapped brain image and nearly 10 h to unwrap a full head-and-neck image on a standard PC. In this paper, we develop a Speedy rEgion-Growing algorithm for Unwrapping Estimated phase (SEGUE) based on similar principles to PRELUDE, implemented with additional methods for acceleration. We compared PRELUDE and SEGUE in numerical phantoms, and using in vivo images of the brain, head and neck, and pelvis acquired in 4-5 healthy volunteers and at 4-6 echo times. To overcome chemical-shift-induced errors within the head and neck, and pelvic images, we also investigated applying both techniques within fat and water masks separately. SEGUE provided almost identical unwrapped phase maps to the gold standard PRELUDE. SEGUE was (1.5 to 70 times) faster than PRELUDE, especially in severely wrapped images at later echoes and in the head and neck, and pelvic images. Applying these techniques within fat and water masks separately removed chemical-shift-induced errors successfully. SEGUE's MATLAB implementation is available for download. SEGUE is a general unwrapping algorithm not specific to MRI, and therefore could be used in images acquired with other modalities.

Test-retest reliability of Diffusion Tensor Imaging metrics in neonates

Diffusion tensor imaging (DTI) has been widely used in children and adults to study the microstructural features of the brain. Its use in neonate brains has been limited. Neonate brains are almost completely unmyelinated, and this together with the tendency for babies to move during a scanning session may affect the reliability of the measurements. Here we divided a 96 direction acquisition into three segments, and analysed the intra scan test-retest reliability for pairs of segments. Each segment was subjected to a rigorous quality control, and from the surviving data we chose 25 diffusion encoding directions from each segment, and assessed the pairwise reliability of the most common DTI metrics. This pairwise reliability was assessed for data from 86 infants. We used tract-based spatial statistics (TBSS), voxelwise and ROI analysis schemes, to see potential differential effects of analysis strategy and post processing on the obtained DTI metrics. We found that intra class correlation coefficient (ICC) values were generally high (ICC > 0.80). Residual motion in the data, after quality control, was not found to associate with the diffusion metrics. The results indicate that DTI metrics from neonate data can be reliable, even at relatively low angular resolution that are common for neonate scans. The results lend confidence to the use of neonate DTI data in cross sectional and longitudinal analyses in brain white matter skeleton. Future studies should assess the reliability of fiber tracking techniques in neonate data.

Incorporating non-linear alignment and multi-compartmental modeling for improved human optic nerve diffusion imaging

In vivo human optic nerve diffusion magnetic resonance imaging (dMRI) is technically challenging with two outstanding issues not yet well addressed: (i) non-linear optic nerve movement, independent of head motion, and (ii) effect from partial-volumed cerebrospinal fluid or interstitial fluid such as in edema. In this work, we developed a non-linear optic nerve registration algorithm for improved volume alignment in axial high resolution optic nerve dMRI. During eyes-closed dMRI data acquisition, optic nerve dMRI measurements by diffusion tensor imaging (DTI) with and without free water elimination (FWE), and by diffusion basis spectrum imaging (DBSI), as well as optic nerve motion, were characterized in healthy adults at various locations along the posterior-to-anterior dimension. Optic nerve DTI results showed consistent trends in microstructural parametric measurements along the posterior-to-anterior direction of the entire intraorbital optic nerve, while the anterior portion of the intraorbital optic nerve exhibited the largest spatial displacement. Multi-compartmental dMRI modeling, such as DTI with FWE or DBSI, was less subject to spatially dependent biases in diffusivity and anisotropy measurements in the optic nerve which corresponded to similar spatial distributions of the estimated fraction of isotropic diffusion components. DBSI results derived from our clinically feasible (∼10 min) optic nerve dMRI protocol in this study are consistent with those from small animal studies, which provides the basis for evaluating the utility of multi-compartmental dMRI modeling in characterizing coexisting pathophysiology in human optic neuropathies.

Diffusion-Weighted Imaging Shows a False-Negative Finding for Bone Marrow Involvement on 18F-FDG PET/MRI in a Patient With Malignant Lymphoma After Blood Transfusion

Iron overload is a major complication in blood transfusion procedures. This report presents a case of malignant lymphoma, in which the findings of bone marrow involvement were discordant between F-FDG PET imaging studies and MRI with diffusion-weighted imaging studies. In this case, the PET imaging studies were positive for malignant lymphoma, whereas the MRI studies were negative. Iron deposition in the reticuloendothelial system due to posttransfusion iron overload was considered to be the cause of the decrease in signal intensity on diffusion-weighted imaging, resulting in a false-negative finding on MRI.

Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data

Because of their low bandwidth in the phase-encode (PE) direction, the susceptibility-induced off-resonance field causes distortions in echo planar imaging (EPI) images. It is therefore crucial to correct for susceptibility-induced distortions when performing diffusion studies using EPI. The susceptibility-induced field is caused by the object (head) disrupting the field and it is typically assumed that it remains constant within a framework defined by the object, (i.e. it follows the object as it moves in the scanner).

However, this is only approximately true. When a non-spherical object rotates around an axis other than that parallel with the magnetic flux (the z-axis) it changes the way it disrupts the field, leading to different distortions. Hence, if using a single field to correct for distortions there will be residual distortions in the volumes where the object orientation is substantially different to that when the field was measured.

In this paper we present a post-processing method for estimating the field as it changes with motion during the course of an experiment. It only requires a single measured field and knowledge of the orientation of the subject when that field was acquired. The volume-to-volume changes of the field as a consequence of subject movement are estimated directly from the diffusion data without the need for any additional or special acquisitions. It uses a generative model that predicts how each volume would look predicated on field change and inverts that model to yield an estimate of the field changes. It has been validated on both simulations and experimental data. The results show that we are able to track the field with high accuracy and that we are able to correct the data for the adverse effects of the changing field.