Eddy Current Correction Research Articles

Impact of geometric correction on echo-planar imaging-based apparent diffusion coefficient maps for abdominal radiotherapy

Objective. The apparent diffusion coefficient (ADC) extracted from diffusion-weighted magnetic resonance imaging (DWI) is a potential biomarker in radiotherapy (RT). DWI is often implemented with an echo-planar imaging (EPI) read-out due to speed, but unfortunately low geometric accuracy follows. This study aimed to investigate the influence of geometric distortions on the ADCs extracted from the gross tumor volume (GTV) and on the shape of the GTV in abdominal EPI-DWI. Approach. Twenty-one patients had EPI-DWI scans on a 1.5 T MRI sim before treatment and on a 1.5 T MRI-Linac at one of the first treatment fractions. Off-resonance correction with and without eddy current correction were applied to ADC maps. The clinical GTVs were deformed based on the same (but inverted) corrections to assess the local-regional geometric influence of distortions. Mean surface distance (MSD), Hausdorff distance (HD), and Dice similarity coefficient (DSC) were calculated to compare the original and distorted GTVs, and ADC values were calculated based on a mono-exponential model. Phantom measurements were performed to validate the applied correction method. Main results. The median (range) ADC change within the GTV after full distortion correction was 1.3% (0.02%–6.9%) for MRI-Sim and 1.5% (0.1%–6.4%) for MRI-Linac. The additional effect of the eddy current correction was small in both systems. The median (range) MSD, HD, and DSC comparing the original and off-resonance distorted GTVs for all patients were 0.43 mm (0.11–0.94 mm), 4.00 mm (1.00–7.81 mm) and 0.93 (0.82–0.99), respectively. Significance. Overall effect of distortion correction was small in terms of derived ADC values, indicating that distortion correction is unimportant for prediction of outcomes based on ADC. However, large local geometric changes occurred after off-resonance distortion correction for some patients, suggesting that if the spatial information from ADC maps is to be used for dose painting strategies, corrections should be applied.

PyDesigner v1.0: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Magnetic Resonance Imaging.

PyDesigner is a Python-based software package based on the original Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline (Dv1) for dMRI preprocessing and tensor estimation. This software is openly provided for non-commercial research and may not be used for clinical care. PyDesigner combines tools from FSL and MRtrix3 to perform denoising, Gibbs ringing correction, eddy current motion correction, brain masking, image smoothing, and Rician bias correction to optimize the estimation of multiple diffusion measures. It can be used across platforms on Windows, Mac, and Linux to accurately derive commonly used metrics from DKI, DTI, WMTI, FBI, and FBWM datasets as well as tractography ODFs and .fib files. It is also file-format agnostic, accepting inputs in the form of .nii, .nii.gz, .mif, and dicom format. User-friendly and easy to install, this software also outputs quality control metrics illustrating signal-to-noise ratio graphs, outlier voxels, and head motion to evaluate data integrity. Additionally, this dMRI processing pipeline supports multiple echo-time dataset processing and features pipeline customization, allowing the user to specify which processes are employed and which outputs are produced to meet a variety of user needs.

Diabetes-related clinical and microstructural white matter changes in patients with Alzheimer's disease.

Although there exists substantial epidemiological evidence indicating an elevated risk of dementia in individuals with diabetes, our understanding of the neuropathological underpinnings of the association between Type-2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) remains unclear. This study aims to unveil the microstructural brain changes associated with T2DM in AD and identify the clinical variables contributing to these changes. In this retrospective study involving 64 patients with AD, 31 individuals had concurrent T2DM. The study involved a comparative analysis of diffusion tensor imaging (DTI) images and clinical features between patients with and without T2DM. The FSL FMRIB software library was used for comprehensive preprocessing and tractography analysis of DTI data. After eddy current correction, the "bedpost" model was utilized to model diffusion parameters. Linear regression analysis with a stepwise method was used to predict the clinical variables that could lead to microstructural white matter changes. We observed a significant impairment in the left superior longitudinal fasciculus (SLF) among patients with AD who also had T2DM. This impairment in patients with AD and T2DM was associated with an elevation in creatine levels. The white matter microstructure in the left SLF appears to be sensitive to the impairment of kidney function associated with T2DM in patients with AD. The emergence of AD in association with T2DM may be driven by mechanisms distinct from the typical AD pathology. Compromised renal function in AD could potentially contribute to impaired white matter integrity.

Differential diagnosis of neurodegenerative diseases using an innovative deep learning approach

AbstractBackgroundNeurodegenerative diseases lead to memory disorders and cognitive impairments with major impacts on day‐to‐day function. Early detection of patients is essential for an efficient care approach, and differential diagnosis is essential for determining prognosis and tailoring management. Most research studies compare patients by pairwise comparison methods, but this does not correspond to daily clinical practice, in which clinicians need to distinguish many different possible types of dementia concomitantly. It has been shown that magnetic resonance imaging (MRI) can mark with good accuracy the different types of dementia, such as: Alzheimer’s disease (AD), Frontotemporal dementia (FTD), Parkinson’s disease (PD), dementia with Lewy bodies (DLB). Recently, Deep Learning approaches have started being used although differential diagnosis is still almost ignored. The aim of this study was to develop a Convolution Neural Network (CNN) to classify probable AD, FTD, DLB, PD and healthy elderly control (CN) subjects starting from Diffusion Tensor Imaging (DTI).MethodThe cohort used in this study came from six different initiatives: ADNI1, NIFD2, NACC3, PDBP4, PPMI5 and Newcastle University6,7,8,9. We analysed a total of 108 CN, 110 AD, 135 FTD, 150 DLB, 132 PD. Demographic and neuropsychological characterization is shown in Table 1. The DTI scans were pre‐processed using the FMRIB software library (FSL) with eddy current correction, skull‐stripping and diffusion tensor calculation. We post‐processed the Fractional Anisotropy (FA) map using 3D CNN. The proposed CNN structure consisted of an image input layer, convolutional filters, several batch normalization with max‐pooling layers and, finally, the classification layer. A data augmentation approach was adopted that ultimately resulted in a dataset of 26’450 brain scans. A hold‐out validation (70% training set 30% test set) was performed.ResultWe found excellent performance of the 3D CNN in discriminating the different neurodegenerative diseases. Figure 1 shows the confusion matrix with the following values for the area under the ROC curve in each class: 0.92 for CN, 0.90 for AD, 0.97 for DLB, 0.96 for FTD, 0.99 for PD.ConclusionThese results prove that Deep Learning and the 3D CCN method are suitable for clinical practice and provide additional clues that can help clinicians in diagnosis making.

Correction of B0 and linear eddy currents: Impact on morphological and quantitative ultrashort echo time double echo steady state (UTE-DESS) imaging.

The purpose of the current study was to investigate the effects of B0 and linear eddy currents on ultrashort echo time double echo steady state (UTE-DESS) imaging and to determine whether eddy current correction (ECC) effectively resolves imaging artifacts caused by eddy currents. 3D UTE-DESS sequences based on either projection radial or spiral cones trajectories were implemented on a 3-T clinical MR scanner. An off-isocentered thin-slice excitation approach was used to measure eddy currents. The measurements were repeated four times using two sets of tested gradient waveforms with opposite polarities and two different slice locations to measure B0 and linear eddy currents simultaneously. Computer simulation was performed to investigate the eddy current effect. Finally, a phantom experiment, an ex vivo experiment with human synovium and ankle samples, and an in vivo experiment with human knee joints, were performed to demonstrate the effects of eddy currents and ECC in UTE-DESS imaging. In a computer simulation, the two echoes (S+ and S-) in UTE-DESS imaging exhibited strong distortion at different orientations in the presence of B0 and linear eddy currents, resulting in both image degradation as well as misalignment of pixel location between the two echoes. The same phenomenon was observed in the phantom, ex vivo, and in vivo experiments, where the presence of eddy currents degraded S+, S-, echo subtraction images, and T2 maps. The implementation of ECC dramatically improved both the image quality and image registration between the S+ and S- echoes. It was concluded that ECC is crucial for reliable morphological and quantitative UTE-DESS imaging.

Automatic bundle-specific white matter fiber tracking tool using diffusion tensor imaging data: A pilot trial in the application of language-related glioma resection.

Cerebral neoplasms like gliomas may cause intracranial pressure increasing, neural tract deviation, infiltration, or destruction in peritumoral areas, leading to neuro-functional deficits. Novel tracking technology, such as DTI, can objectively reveal and visualize three-dimensional white matter trajectories; in combination with intraoperative navigation, it can help achieve maximum resection whilst minimizing neurological deficit. Since the reconstruction of DTI raw data largely relies on the technical engineering and anatomical experience of the operator; it is time-consuming and prone to operator-induced bias. Here, we develop new user-friendly software to automatically segment and reconstruct functionally active areas to facilitate precise surgery. In this pilot trial, we used an in-house developed software (DiffusionGo) specially designed for neurosurgeons, which integrated a reliable diffusion-weighted image (DWI) preprocessing pipeline that embedded several functionalities from software packages of FSL, MRtrix3, and ANTs. The preprocessing pipeline is as follows: 1. DWI denoising, 2. Gibbs-ringing removing, 3. Susceptibility distortion correction (process if opposite polarity data were acquired), 4. Eddy current and motion correction, and 5. Bias correction. Then, this fully automatic multiple assigned criteria algorithms for fiber tracking were used to achieve easy modeling and assist precision surgery. We demonstrated the application with three language-related cases in three different centers, including a left frontal, a left temporal, and a left frontal-temporal glioma, to achieve a favorable surgical outcome with language function preservation or recovery. The DTI tracking result using DiffusionGo showed robust consistency with direct cortical stimulation (DCS) finding. We believe that this fully automatic processing pipeline provides the neurosurgeon with a solution that may reduce time costs and operating errors and improve care quality and surgical procedure quality across different neurosurgical centers.

Accelerated sequences of 4D flow MRI using GRAPPA and compressed sensing: A comparison against conventional MRI and computational fluid dynamics.

To evaluate hemodynamic markers obtained by accelerated GRAPPA (R= 2, 3, 4) and compressed sensing (R= 7.6) 4D flow MRI sequences under complex flow conditions. The accelerated 4D flow MRI scans were performed on a pulsatile flow phantom, along with a nonaccelerated fully sampled k-space acquisition. Computational fluid dynamics simulations based on the experimentally measured flow fields were conducted for additional comparison. Voxel-wise comparisons (Bland-Altman analysis, -norm metric), as well as nonderived quantities (velocity profiles, flow rates, and peak velocities), were used to compare the velocity fields obtained from the different modalities. 4D flow acquisitions and computational fluid dynamics depicted similar hemodynamic patterns. Voxel-wise comparisons between the MRI scans highlighted larger discrepancies at the voxels located near the phantom's boundary walls. A trend for all MR scans to overestimate velocity profiles and peak velocities as compared to computational fluid dynamics was noticed in regions associated with high velocity or acceleration. However, good agreement for the flow rates was observed, and eddy-current correction appeared essential for consistency of the flow rates measurements with respect to the principle of mass conservation. GRAPPA (R= 2, 3) and highly accelerated compressed sensing showed good agreement with the fully sampled acquisition. Yet, all 4D flow MRI scans were hampered by artifacts inherent to the phase-contrast acquisition procedure. Computational fluid dynamics simulations are an interesting tool to assess these differences but are sensitive to modeling parameters.

Characterization and correction of diffusion gradient-induced eddy currents in second-order motion-compensated echo-planar and spiral cardiac DTI.

Very high gradient amplitudes played out over extended time intervals as required for second-order motion-compensated cardiac DTI may violate the assumption of a linear time-invariant gradient system model. The aim of this work was to characterize diffusion gradient-related system nonlinearity and propose a correction approach for echo-planar and spiral spin-echo motion-compensated cardiac DTI. Diffusion gradient-induced eddy currents of 9 diffusion directions were characterized at b values of 150 s/mm2 and 450 s/mm2 for a 1.5Tesla system and used to correct phantom, ex vivo, and in vivo motion-compensated cardiac DTI data acquired with echo-planar and spiral trajectories. Predicted trajectories were calculated using gradient impulse response function and diffusion gradient strength- and direction-dependent zeroth- and first-order eddy current responses. A reconstruction method was implemented using the predicted -space trajectories to additionally include off-resonances and concomitant fields. Resulting images were compared to a reference reconstruction omitting diffusion gradient-induced eddy current correction. Diffusion gradient-induced eddy currents exhibited nonlinear effects when scaling up the gradient amplitude and could not be described by a 3D basis alone. This indicates that a gradient impulse response function does not suffice to describe diffusion gradient-induced eddy currents. Zeroth- and first-order diffusion gradient-induced eddy current effects of up to -1.7rad and -16 to +12 rad/m, respectively, were identified. Zeroth- and first-order diffusion gradient-induced eddy current correction yielded improved image quality upon image reconstruction. The proposed approach offers correction of diffusion gradient-induced zeroth- and first-order eddy currents, reducing image distortions to promote improvements of second-order motion-compensated spin-echo cardiac DTI.

Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus.

There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.

409 Diffusion MRI to investigate atypical corticospinal tract microstructure and motor impairments in hemiplegic cerebral palsy

OBJECTIVES/GOALS: Hemiplegic cerebral palsy (HCP) limits the functional ability of one side of the body, but motor impairments are very heterogeneous among children with this diagnosis. The purpose of this study was to evaluate the CST using DTI and tractography analyses as it relates to quantitative measures of the severity of weakness and mirror movements in HCP. METHODS/STUDY POPULATION: Preliminary results include five participants with HCP (2M, 16±7.8 years) and six controls (2M, 12±3.5 years). DTI data were collected using a spin-echo echo-planar imaging sequence with diffusion weighting of b=1000 s/mm2 in 60 different directions and 8 scans without diffusion weighting (b=0 s/mm2). Images were processed with steps of brain extraction, denoising, motion and eddy current correction, and fit with tensors to generate maps of diffusivity metrics. Anatomical landmarks were used to guide probabilistic tractography of the CST for analyses in both the lesioned and non-lesioned hemispheres. To quantify grasp weakness and mirroring severity, participants completed a bilateral assessment of grip strength using handheld force measurement devices and custom MATLAB data acquisition software. RESULTS/ANTICIPATED RESULTS: DTI is a feasible method to evaluate CST microstructure in HCP and typically developing pediatric participants. Spearman correlation analyses, using age and sex as covariates, revealed that for the lesioned hemisphere CST, there were significant positive correlations between grasp weakness severity and mean diffusivity (MD) (Ï=0.66, p=0.038) and between grasp weakness severity and axial diffusivity (AD) (Ï =0.68, p=0.030). There was not a significant correlation between grasp weakness severity and fractional anisotropy (FA) (Ï =-0.47, p=0.166). For the non-lesioned hemisphere CST, there was a significant positive correlation between mirroring severity and radial diffusivity (RD) (Ï =0.70, p=0.023). There was not a significant correlation between mirror movement severity and FA (Ï =-0.41, p=0.2361). DISCUSSION/SIGNIFICANCE: The correlations demonstrated here show a potential relationship between CST microstructure and the severity of hand impairments in HCP. While these relationships between CST diffusivity properties and hand function are preliminary, they provide the first steps to better understand underlying neural mechanisms for motor impairments in HCP.

Early detection of mild cognitive impairment with convolutional neural network based on brain structural connectivity

AbstractBackgroundStructural connectivity derived from diffusion tensor imaging (DTI) reflects white matter network changes during Alzheimer’s Disease (AD) progression, which can potentially serve as a biomarker for prediction. In this study we explored the ability of structural connectivity to predict mild cognitive impairment (MCI) by implementing a convolutional neural network (CNN).Method613 DTI scans (194 controls, 291 MCI and 128 AD) from 206 patients (113 male, 93 female; age range: 55‐91 years) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Phase GO and 2 were included in this study. The clinical diagnosis at last ADNI follow‐up visit served as reference for prediction (average: 44.4 ± 26.7 months). DTI scans were acquired with 5 b0 and 41 diffusion weighted volumes with b=1000 s/mm2 at 2.7×2.7×2.7mm3 resolution. Preprocessing included eddy current correction and co‐registration to normalized T1‐weighted scans.Whole brain white matter fiber was tracked by deterministic tractography using DSI Studio under Q‐space diffeomorphic reconstruction scheme, which calculates the quantitative anisotropy mapping prior to normalization to standard space. An Automated Anatomical Labeling atlas 2 with 120 region‐of‐interests (ROI) was used to parcellate the brain, where each ROI represented a node of the network. The weight of the edge was calculated by number of tracts connecting two ROIs and normalized by medial length. Therefore a 120×120 structural connectivity matrix was generated for each scan. 80% of data was used for training and 10% of data for validation. The parameters that achieved highest accuracy on validation set was applied to the remaining 10% (hold‐out test set) for final evaluation. A seven‐layer CNN model was constructed for differentiating three groups. The performance was evaluated by accuracy and sensitivity.ResultOn an average of 3.7 years before the diagnosis, the prediction accuracy was 86.9% in validation set and 85.7% in hold‐out test set. The sensitivity to predict controls and MCI were 95.0% and 93.3%, respectively.ConclusionUsing CNN, structural connectivity allowed good ability to predict MCI from cohorts at an early stage before diagnosis.

Late Breaking Abstracts

Late Breaking Abstracts

Accurate Quantification of Choline-to-Creatine Ratio as a Biomarker to Distinguish Osteosarcoma Patients from Normal Subjects Employing Proton Magnetic Resonance Spectroscopy Imaging at 3 Tesla

Purpose: This study focused on accurate quantification of a maximum of Choline-to-Creatine ratio (Max (Cho/Cr)) in 10 Osteosarcoma patients, in comparison with 5 healthy volunteers as our control group using proton Magnetic Resonance Spectroscopy Imaging (1H-MRSI).
 Materials and Methods: Max (Cho/Cr) were obtained in 10 patients with Osteosarcoma over their corresponding ratio maps containing diseased tissue, to be compared with Cho/Cr in 5 healthy volunteers at 3T, employing MRSI (Performed Employing Pointed-resolved Spectroscopy (PRESS), TR/TE: 2500s /135 ms) with water-suppression. An extra unsuppressed water Single-Voxel Spectroscopy (SVS) was acquired to provide phase information for further Eddy Current Correction (ECC). Multi-stage preprocessing was applied. Subtract QUEST MRSI as a time-domain technique was employed to accurately quantify the metabolites’ ratios and to estimate the baseline.
 Results: An optimal database for Subtract QUEST was achieved based on multiple trials evaluated by acceptable peak-fitting and Cramer-Rao-Bound (CRB). Lipids at frequencies of 0.94 and 1.33ppm were combined to increase the accuracy of the Lipid estimation.
 Conclusion: Estimation of Max (Cho/Cr) evaluated over Cho/Cr spatial maps to distinguish Osteosarcoma patients from normal subjects suggested that the proposed quantification method leads to high power and linear classifier with a high degree of reproducibility, considering 1H-MRSI at 3T machine as a high efficacy diagnostic tool for musculoskeletal radiology.

Evaluation of eddy current distortion and field inhomogeneity distortion corrections in MR diffusion imaging using log-demons DIR method

To investigate the feasibility of the log-demons deformable image registration (DIR) method to correct eddy current and field inhomogeneity distortions while preserving diffusion tensor information. Diffusion-weighted images (DWIs) are susceptible to distortions caused by eddy current and echo-planar imaging (EPI) gradients. We propose a post-acquisition correction algorithm using the log-demons DIR technique for eddy current and field inhomogeneity distortions of DWI. The new correction technique was applied to DWI acquired using a diffusion phantom and the multiple acquisitions for standardization of structural imaging validation and evaluation (MASSIVE) brain database. This method is compared to previous methods using cross-correlation, mutual information (MI). In the phantom study, the log-demons algorithm reduced eddy current and field inhomogeneity distortions while preserving diffusion tensor information when compared to affine and demon’s registration techniques. Analysis of the tensor metrics using percent difference and the root mean square of the apparent diffusion coefficient and fractional anisotropy found that the log-demons algorithm outperforms the other algorithms in terms of preserving diffusion information. In the MASSIVE study, the average MI of all slices increased for both eddy current and field inhomogeneity distortion correction. The average absolute differences of all slices between corrected images with opposing gradients were also on average decreased. This work indicates that the log-demons DIR algorithm is feasible to reduce eddy current and field inhomogeneity distortions while preserving quantitative diffusion information.

Performance gains with Compute Unified Device Architecture-enabled eddy current correction for diffusion MRI.

Correcting for eddy currents, movement-induced distortion and gradient inhomogeneities is imperative when processing diffusion MRI (dMRI) data, but is highly computing resource-intensive. Recently, Compute Unified Device Architecture (CUDA) was implemented for the widely-used eddy-correction software, 'eddy', which reduces processing time and allows more comprehensive correction. We investigated processing speed, performance and compatibility of CUDA-enabled eddy-current correction processing compared to commonly-used non-CUDA implementations. Four representative dMRI datasets from the Human Connectome Project, Alzheimer's Disease Neuroimaging Initiative and Chronic Diseases Connectome Project were processed on high-specification and regular workstations through three different configurations of 'eddy'. Processing times and graphics processing unit (GPU) resources used were monitored and compared. Using CUDA reduced the 'eddy' processing time by a factor of up to five. The CUDA slice-to-volume correction method was also faster than non-CUDA eddy except when datasets were large. We make a series of recommendations for eddy configuration and hardware. We suggest that users of eddy-correction software for dMRI processing utilise CUDA and take advantage of the slice-to-volume correction option. We recommend that users run eddy on computers with at least 32GB motherboard random access memory (RAM), and a graphics card with at least 4.5GB RAM and 3750 cores to optimise processing time.

A field-monitoring-based approach for correcting eddy-current-induced artifacts of up to the 2nd spatial order in human-connectome-project-style multiband diffusion MRI experiment at 7T: A pilot study

Over the recent years, significant advances in Spin-Echo (SE) Echo-Planar (EP) Diffusion MRI (dMRI) have enabled improved fiber tracking conspicuity in the human brain. At the same time, pushing the spatial resolution and using higher b-values inherently expose the acquired images to further eddy-current-induced distortion and blurring. Recently developed data-driven correction techniques, capable of significantly mitigating these defects, are included in the reconstruction pipelines developed for the Human Connectome Project (HCP) driven by the NIH BRAIN initiative. In this case, however, corrections are derived from the original diffusion-weighted (DW) magnitude images affected by distortion and blurring. Considering the complexity of k-space deviations in the presence of time varying high spatial order eddy currents, distortion and blurring may not be fully reversed when relying on magnitude DW images only. An alternative approach, consisting of iteratively reconstructing DW images based on the actual magnetic field spatiotemporal evolution measured with a magnetic field monitoring camera, has been successfully implemented at 3T in single band dMRI (Wilm et al., 2017, 2015). In this study, we aim to demonstrate the efficacy of this eddy current correction method in the challenging context of HCP-style multiband (MB ​= ​2) dMRI protocol.The magnetic field evolution was measured during the EP-dMRI readout echo train with a field monitoring camera equipped with 16 19F NMR probes. The time variation of 0th, 1st and 2nd order spherical field harmonics were used to reconstruct DW images. Individual DW images reconstructed with and without field correction were compared. The impact of eddy current correction was evaluated by comparing the corresponding direction-averaged DW images and fractional anisotropy (FA) maps.19F field monitoring data confirmed the existence of significant field deviations induced by the diffusion-encoding gradients, with variations depending on diffusion gradient amplitude and direction. In DW images reconstructed with the field correction, residual aliasing artifacts were reduced or eliminated, and when high b-values were applied, better gray/white matter delineation and sharper gyri contours were observed, indicating reduced signal blurring. The improvement in image quality further contributed to sharper contours and better gray/white matter delineation in mean DW images and FA maps.In conclusion, we demonstrate that up-to-2nd-order-eddy-current-induced field perturbation in multiband, in-plane accelerated HCP-style dMRI acquisition at 7T can be corrected by integrating the measured field evolution in image reconstruction.

Advanced single voxel 1 H magnetic resonance spectroscopy techniques in humans: Experts' consensus recommendations.

Conventional proton MRS has been successfully utilized to noninvasively assess tissue biochemistry in conditions that result in large changes in metabolite levels. For more challenging applications, namely, in conditions which result in subtle metabolite changes, the limitations of vendor-provided MRS protocols are increasingly recognized, especially when used at high fields (≥3 T) where chemical shift displacement errors, B0 and B1 inhomogeneities and limitations in the transmit B1 field become prominent. To overcome the limitations of conventional MRS protocols at 3 and 7 T, the use of advanced MRS methodology, including pulse sequences and adjustment procedures, is recommended. Specifically, the semiadiabatic LASER sequence is recommended when TE values of 25-30 ms are acceptable, and the semiadiabatic SPECIAL sequence is suggested as an alternative when shorter TE values are critical. The magnetic field B0 homogeneity should be optimized and RF pulses should be calibrated for each voxel. Unsuppressed water signal should be acquired for eddy current correction and preferably also for metabolite quantification. Metabolite and water data should be saved in single shots to facilitate phase and frequency alignment and to exclude motion-corrupted shots. Final averaged spectra should be evaluated for SNR, linewidth, water suppression efficiency and the presence of unwanted coherences. Spectra that do not fit predefined quality criteria should be excluded from further analysis. Commercially available tools to acquire all data in consistent anatomical locations are recommended for voxel prescriptions, in particular in longitudinal studies. To enable the larger MRS community to take advantage of these advanced methods, a list of resources for these advanced protocols on the major clinical platforms is provided. Finally, a set of recommendations are provided for vendors to enable development of advanced MRS on standard platforms, including implementation of advanced localization sequences, tools for quality assurance on the scanner, and tools for prospective volume tracking and dynamic linear shim corrections.

Prediction of General Intelligence Using DTI Data

Background: Intelligence can be defined as the capability of a person to purposeful performance, logical thinking, and effective interaction with the environment. What we study as intelligence is a collection of talents, such as memory, accuracy, learning, and perception, which are different in different individuals. Objectives: Using diffusion tensor imaging (DTI) data, we studied relationships between mean fractional anisotropy (FA) in brain structural pathways and full-scale general intelligence (FSIQ) scores. Methods: The structural connectivity matrix for the whole brain was estimated using 116 regions defined by the AAL atlas. The superiority of this study to others was that the ROIs covered the whole brain and each node represented a small region of the brain; thus, the related connections would be more specific. We identified connections and features that played key roles in FSIQ. Besides, FSIQ was predicted using four regression techniques and the results were compared. The dataset consisted of the data of 29 subjects (15 males and 14 females), with the age range of 18 - 28 years, and the mean age of 25.10 years, recorded in our national brain mapping lab (NBML), Tehran, Iran. All the subjects were healthy, without any brain damage. In addition, none of them had a history of mental illness, brain surgery, or brain-damaging disease. All DTI data were processed using Explore-DTI software to perform eddy current correction and motion correction. Then, all images were registered to the AAL atlas template with 116 nodes using the Explore-DTI software (version 4.8.6). For each subject, DTI-based tractography was done for the whole brain by a deterministic approach based on streamline algorithms. FSIQ was measured using the WAIS-III test. This score was calculated by assessing cognitive abilities such as verbal reasoning, attention to verbal principles, fluid reasoning, spatial processing, and visual stimulations. The dataset mean (standard deviation) of the FSIQ scores was 96.93 (± 10.24). To identify the most important connections for FSIQ prediction, we performed a linear regression between each variable and the FSIQ scores. The significant variables were selected as the connections related to the FSIQ scores. To remove collinearity among variables, a correlation analysis was performed and variables that had a high correlation with another variable (r > 0.95) were deleted. Moreover, to reduce the number of features, PCA was applied to the remaining connections. Then, four regression models including linear regression, a support vector regression, a convolutional neural network, and a multilayer perceptron were applied to the data to predict the intelligence scores. In this case, 48 connections were selected after applying correlation analysis and 14 features were the outcome of the PCA step. Highly connected nodes were in the prefrontal, cerebellum, parietal, and limbic regions. These regions may play a key role in human intelligence. Results: After applying PCA, the SVR model with a linear kernel and a constant parameter of 10 was optimal for our dataset. It was utilized to predict FSIQ using leave-one-out cross-validation. The correlation between the predicted and measured intelligence scores was r = 0.72 (P = 9.03 × 10-6). It is noteworthy that the proposed model for predicting intelligence had a higher determination coefficient (R2 = 0.52) than previous methods. Moreover, previous methods widely used the functional MRI technique to extract features for predicting intelligence. In contrast, we used DTI data and mean FA of connections to predict intelligence and the results indicated that these data conveyed more information about intelligence.

Iron Loss Estimation for IPM Motors based on Eddy Current Loss Correction Factor Considering Plastic Deformation by Punching

Iron Loss Estimation for IPM Motors based on Eddy Current Loss Correction Factor Considering Plastic Deformation by Punching

A method for cranial target delineation in radiotherapy treatment planning aided by single-voxel magnetic resonance spectroscopy: evaluation using a custom-designed gel-based phantom and simulations.

Magnetic resonance spectroscopy (MRS) has been useful in radiotherapy treatment planning (RTP) especially in tumor delineation. Routinely, 2D/3D MRSI data are used for this application. However, not all centers have access to 2D/3D MRSI. The objective of this study was to introduce a method of using single-voxel spectroscopy (SVS) data in target delineation and assess its reliability. A gel-based phantom containing Creatine (Cr), N-acetyl-l-aspartic-acid (NAA), and Choline (Cho) was designed and built. The metabolite ratios simulate the normal and tumoral part of the brain. The jMRUI software (v. 6.0) was used to simulate a 1.5 T GE MRI scanner. The metabolite spectra provided by different time of echos (TE)s of the Point-RESolved Spectroscopy pulse-sequence (PRESS), different data-points, and post-processings were quantized by jMRUI. PseudoMRSI maps of Cho/Cr, NAA/Cr, and Cho + Cr/NAA were created. A conformity index (CI) was used to determine which metabolite-ratio isolines are more appropriate for tumor delineation. The simulation accuracy was verified. There were no differences > 4% between the measured and simulated spectra in peak regions. The pseudoMRSI map of Cho + Cr/NAA smoothly followed the complicated geometry of the tumor inside the gel-based phantom. The results showed that the single-voxel spectra produced by the PRESS pulse sequence with the TE of 144 ms, 512 data-points, and minimum post-processings of water suppression, eddy current correction, and baseline correction can be used for target delineation. This study suggests that SVS data can be used to aid target delineation by using a mathematical approach. This can enable a wider use of MR-derived information in radiotherapy. To the best of our knowledge, until now, 2D or 3D MRSI data provided from 3T MRI scanners have been used for MRS-based radiotherapy treatment planning. However, there are a lot of centers that are equipped to 1.5 T MRI scanners and some of them just equipped to SVS. This study introduces a mathematical approach to help these centers to take the benefits of MRS-based treatment planning.