Gradient Nonlinearity Correction Research Articles

Mapping the Impact of Approximate Gradient Nonlinearity Fields Correction on Tractography.

Nonlinear gradients impact diffusion weighted MRI by introducing spatial variation in estimated diffusion tensors. Recent studies have shown that increasing signal-to-noise ratios and the use of ultra-strong gradients may lead to clinically significant impacts on analyses due to these nonlinear gradients in microstructural measures. These effects can potentially bias tractography results and cause misinterpretation of data. Herein, we characterize the impact of an "approximate" gradient nonlinearity correction technique in tractography using empirically derived gradient nonlinear fields. This technique scales the diffusion signal by the change in magnitude due to the gradient nonlinearities, without concomitant correction of gradient direction errors. The impact of this correction on tractography is assessed through white matter bundle segmentation and connectomics via bundle-wise volume, fractional anisotropy, mean diffusivity, radial diffusivity, axial diffusivity, primary eigenvector, and length; as well as the modularity, global efficiency, and characteristic path length connectomics graph measures. We investigate the differences between (1) these measures directly and (2) the within session variability of these measures before and after approximate correction in 61 subjects from the MASiVar pediatric reproducibility dataset. We find approximate correction results is little to no differences on the population level, but large differences on the subject-specific level for both the measures directly and their within session variability. Thus, this study suggests though approximate correction of gradient nonlinearities may not change tractography findings on the population level, subject-specific interpretations may exhibit large fluctuations. A limitation is the lack of comparison with the empirical voxel-wise gradient table correction.

Mapping the impact of nonlinear gradient fields with noise on diffusion MRI

In diffusion MRI, gradient nonlinearities cause spatial variations in the magnitude and direction of diffusion gradients. Studies have shown artifacts from these distortions can results in biased diffusion tensor information and tractography. Here, we investigate the impact of gradient nonlinearity correction in the presence of noise. We introduced empirically derived gradient nonlinear fields at different signal-to-noise ratio (SNR) levels in two experiments: tensor field simulation and simulation of the brain. For each experiment, this work compares two techniques empirically: voxel-wise gradient table correction and approximate correction by scaling the signal directly. The impact was assessed through diffusion metrics including mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and principal eigen vector (V1). The study shows (1) the correction of gradient nonlinearities will not lead to substantively incorrect estimation of diffusion metrics in a linear system, (2) gradient nonlinearity correction does not interact adversely with noise, (3) nonlinearity correction suppresses the impact of nonlinearities in typical SNR data, (4) for SNR below 30, the performance of both the gradient nonlinearity correction techniques were similar, and (5) larger impacts are seen in regions where the gradient nonlinearities are distinct. Thus, this study suggests that there were greater beneficial effects than adverse effects due to the correction of nonlinearities. Additionally, correction of nonlinearities is recommended when region of interests are in areas with pronounced nonlinearities.

Diffusion-Weighted Imaging of the Abdomen: Correction for Gradient Nonlinearity Bias in Apparent Diffusion Coefficient.

Gradient nonlinearity (GNL) introduces spatial nonuniformity bias in apparent diffusion coefficient (ADC) measurements, especially at large offsets from the magnet isocenter. To investigate the effects of GNL in abdominal ADC measurements and to develop an ADC bias correction procedure. Retrospective. Two homemade ultrapure water phantoms/25 patients with histologically confirmed pancreatic ductal adenocarcinoma (PDAC). A 3.0 T/diffusion-weighted imaging (DWI) with single-shot echo-planar imaging sequence. ADC bias was computed in the three orthogonal directions at different offset locations. The spatial-dependent correctors of ADC bias were generated from the ADCs of phantom 1. The ADCs were estimated before and after corrections for the phantom 1 with both the proposed approach and the theoretical GNL correction method. For the patients, ADCs were measured in abdominal tissues including left and right liver lobes, PDAC, spleen, bilateral kidneys, and bilateral paraspinal muscles. Friedman tests and Wilcoxon tests. The ADC bias measured by phantom 1 was 9.7% and 12.6% higher in the right-left and anterior-posterior directions and 9.2% lower in the superior-inferior direction at the 150 mm offsets from the magnetic isocenter. The corrected vs. the uncorrected ADCs measurements (median: 2.20× 10-3 mm2 /sec for both the proposed method and the theoretical GNL method vs. 2.31× 10-3 mm2 /sec, respectively) and their relative ADC errors (0.014, 0.016, and 0.054, respectively) were lower in the phantom 1. The relative ADC errors substantially decreased after correction in the phantom 2 (median: 0.048 and -0.008, respectively). The ADCs of all the abdominal tissues were lower after correction except for the left liver lobes (P=0.13). GNL bias in abdominal ADC can be measured by a DWI phantom. The proposed correction procedure was successfully applied for the bias correction in abdominal ADC. 3. Stage 1.

Validating the accuracy of multispectral metal artifact suppressed diffusion-weighted imaging.

Diffusion-weighted imaging (DWI) provides quantitative measurement of random water displacement in tissue as calculated by the apparent diffusion coefficient (ADC). While heavily utilized in stroke and oncology applications, DWI is a promising tool to map microstructural changes in musculoskeletal applications including evaluation of synovial reactions resulting from total hip arthroplasty (THA). One major challenge facing the application of DWI in THA is the significant artifacts related to the conventional echo-planar imaging (EPI) readout used. Multispectral imaging (MSI) techniques, including the multiacquisition with variable resonance image combination (MAVRIC), have been shown to effectively reduce metallic susceptibility artifacts around total joint replacements to render clinically useful images. Recently, a 2D periodically rotated overlapping parallel line with enhanced reconstruction (PROPELLER) FSE acquisition that incorporates a diffusion preparation pulse with 2D-MAVRIC has been developed to mitigate both distortion and dropout artifacts. While there have been some preliminary assessments of DWI-MAVRIC, the repeatability of DWI-MAVRIC and the effects of key parameters, such as the number of spectral bins, are unknown. To evaluate the quantitative accuracy of DWI-MAVRIC as compared to conventional diffusion sequences. A diffusion phantom with different reference diffusivities (ADC = 113-1123 μm2 /s) was used. Scans were performed on two 1.5T MRI scanners. DWI-EPI and DWI-MAVRIC were acquired in both the axial and coronal planes. Three spatial offsets (0 cm, 10 cm left, and 10 cm right off iso-center) were used to evaluate effects of off-isocenter positioning. To assess intraday and interday repeatability, DWI-EPI and DWI-MAVRIC acquisitions were repeated on one scanner at same-day and 9-month intervals. To assess inter-scanner repeatability, DWI-EPI and DWI-MAVRIC acquisitions were compared between two scanners. ADC maps were generated with and without gradient nonlinearity correction (GNC). Linear regression, correlation, and error statistics were determined between calculated and reference ADC values. Bland-Altman plots were generated to evaluate intraday, interday, and interscanner repeatability. DWI-MAVRIC had excellent correlation to reference values but at reduced linearity (r = 1.00, slope = 0.91-0.94) as compared to DWI-EPI (r = 1.00, slope = 0.99-1.01). A greater than 5% ADC bias was observed at the lowest ADC values, predominantly in the DWI-MAVRIC scans. ADC values did not vary with DWI-MAVRIC parameters. DWI-EPI acquisitions had intraday, interday, and interscanner repeatability of 3.18 μm2 /s, 19.2 μm2 /s, and 20.2 μm2 /s, respectively. DWI-MAVRIC acquisitions had inferior intraday, interday, and interscanner repeatability of 13.3 μm2 /s, 44.7 μm2 /s, 110 μm2 /s, respectively. Lower ADC errors were found at isocenter, as compared to the left and right positions. GNC reduced the absolute error by 0.31% ± 0.89%, 3.6% ± 1.4%, 0.65% ± 2.4% for the center, left, and right positions, respectively. DWI-MAVRIC provides good linearity with respect to reference values and good intra- and interday repeatability.

Gradient nonlinearity correction in liver DWI using motion-compensated diffusion encoding waveforms

Objective To experimentally characterize the effectiveness of a gradient nonlinearity correction method in removing ADC bias for different motion-compensated diffusion encoding waveforms.MethodsThe diffusion encoding waveforms used were the standard monopolar Stejskal–Tanner pulsed gradient spin echo (pgse) waveform, the symmetric bipolar velocity-compensated waveform (sym-vc), the asymmetric bipolar velocity-compensated waveform (asym-vc) and the asymmetric bipolar partial velocity-compensated waveform (asym-pvc). The effectiveness of the gradient nonlinearity correction method using the spherical harmonic expansion of the gradient coil field was tested with the aforementioned waveforms in a phantom and in four healthy subjects.ResultsThe gradient nonlinearity correction method reduced the ADC bias in the phantom experiments for all used waveforms. The range of the ADC values over a distance of ± 67.2 mm from isocenter reduced from 1.29 × 10–4 to 0.32 × 10–4 mm2/s for pgse, 1.04 × 10–4 to 0.22 × 10–4 mm2/s for sym-vc, 1.22 × 10–4 to 0.24 × 10–4 mm2/s for asym-vc and 1.07 × 10–4 to 0.11 × 10–4 mm2/s for asym-pvc. The in vivo results showed that ADC overestimation due to motion or bright vessels can be increased even further by the gradient nonlinearity correction.ConclusionThe investigated gradient nonlinearity correction method can be used effectively with various motion-compensated diffusion encoding waveforms. In coronal liver DWI, ADC errors caused by motion and residual vessel signal can be increased even further by the gradient nonlinearity correction.

Empirical validation of gradient field models for an accurate ADC measured on clinical 3T MR systems in body oncologic applications.

To empirically corroborate vendor-provided gradient nonlinearity (GNL) characteristics and demonstrate efficient GNL bias correction for human brain apparent diffusion coefficient (ADC) across 3T MR systems and spatial locations. Spatial distortion vector fields (DVF) were mapped in 3D using a surface fiducial array phantom for individual gradient channels on three 3T MR platforms from different vendors. Measured DVF were converted into empirical 3D GNL tensors and compared with their theoretical counterparts derived from vendor-provided spherical harmonic (SPH) coefficients. To illustrate spatial impact of GNL on ADC, diffusion weighted imaging using three orthogonal gradient directions was performed on a volunteer brain positioned at isocenter (as a reference) and offset superiorly by 10-17cm (>10% predicted GNL bias). The SPH tensor-based GNL correction was applied to individual DWI gradient directions, and derived ADC was compared with low-bias reference for human brain white matter (WM) ROIs. Empiric and predicted GNL errors were comparable for all three studied 3T MR systems, with <1.0% differences in the median and width of spatial histograms for individual GNL tensor elements. Median (±width) of ADC (10-3mm2/s) histograms measured at isocenter in WM reference ROIs from three MR systems were: 0.73±0.11, 0.71±0.14, 0.74±0.17, and at off-isocenters (before versus after GNL correction) were respectively 0.63±0.14 versus 0.72±0.11, 0.53±0.16 versus 0.74±0.18, and 0.65±0.16 versus 0.76±0.18. The phantom-based spatial distortion measurements validated vendor-provided gradient fields, and accurate WM ADC was recovered regardless of spatial locations and clinical MR platforms using system-specific tensor-based GNL correction for routine DWI.

Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI

BackgroundAchieving inter-site / inter-scanner reproducibility of diffusion weighted magnetic resonance imaging (DW-MRI) metrics has been challenging given differences in acquisition protocols, analysis models, and hardware factors. PurposeMagnetic field gradients impart scanner-dependent spatial variations in the applied diffusion weighting that can be corrected if the gradient nonlinearities are known. However, retrieving manufacturer nonlinearity specifications is not well supported and may introduce errors in interpretation of units or coordinate systems. We propose an empirical approach to mapping the gradient nonlinearities with sequences that are supported across the major scanner vendors. Study typeProspective observational study. SubjectsA spherical isotropic diffusion phantom, and a single human control volunteer. Field strength/sequence3 T (two scanners). Stejskal-Tanner spin echo sequence with b-values of 1000, 2000 s/mm2 with 12, 32, and 384 diffusion gradient directions per shell. AssessmentWe compare the proposed correction with the prior approach using manufacturer specifications against typical diffusion pre-processing pipelines (i.e., ignoring spatial gradient nonlinearities). In phantom data, we evaluate metrics against the ground truth. In human and phantom data, we evaluate reproducibility across scans, sessions, and hardware. Statistical testsWilcoxon rank-sum test between uncorrected and corrected data. ResultsIn phantom data, our correction method reduces variation in mean diffusivity across sessions over uncorrected data (p < 0.05). In human data, we show that this method can also reduce variation in mean diffusivity across scanners (p < 0.05). ConclusionOur method is relatively simple, fast, and can be applied retroactively. We advocate incorporating voxel-specific b-value and b-vector maps should be incorporated in DW-MRI harmonization preprocessing pipelines to improve quantitative accuracy of measured diffusion parameters.

Retrospective Correction of ADC for Gradient Nonlinearity Errors in Multicenter Breast DWI Trials: ACRIN6698 Multiplatform Feasibility Study.

The presented analysis of multisite, multiplatform clinical oncology trial data sought to enhance quantitative utility of the apparent diffusion coefficient (ADC) metric, derived from diffusion-weighted magnetic resonance imaging, by reducing technical interplatform variability owing to systematic gradient nonlinearity (GNL). This study tested the feasibility and effectiveness of a retrospective GNL correction (GNC) implementation for quantitative quality control phantom data, as well as in a representative subset of 60 subjects from the ACRIN 6698 breast cancer therapy response trial who were scanned on 6 different gradient systems. The GNL ADC correction based on a previously developed formalism was applied to trace-DWI using system-specific gradient-channel fields derived from vendor-provided spherical harmonic tables. For quantitative DWI phantom images acquired in typical breast imaging positions, the GNC improved interplatform accuracy from a median of 6% down to 0.5% and reproducibility of 11% down to 2.5%. Across studied trial subjects, GNC increased low ADC (<1 µm2/ms) tumor volume by 16% and histogram percentiles by 5%–8%, uniformly shifting percentile-dependent ADC thresholds by ∼0.06 µm2/ms. This feasibility study lays the grounds for retrospective GNC implementation in multiplatform clinical imaging trials to improve accuracy and reproducibility of ADC metrics used for breast cancer treatment response prediction.

The adverse effect of gradient nonlinearities on diffusion MRI: From voxels to group studies

Nonlinearities of gradient magnetic fields in diffusion MRI (dMRI) can introduce systematic errors in estimates of diffusion measures. While there are correction methods that can compensate for these errors, as presented in the Human Connectome Project, such nonlinear effects are often assumed to be negligible for typical applications, and as a result, gradient nonlinearities are mostly left uncorrected. In this work, we perform a systematic analysis to investigate the effect of gradient nonlinearities on dMRI studies, from voxel-wise estimates to group study outcomes. We present a novel framework to quantify and visualize these effects by decomposing them into their magnitude and angle components. Mean magnitude deviation and fractional gradient anisotropy are introduced to quantify the distortions in the size and shape of gradient vector distributions. By means of Monte-Carlo simulations and real data from the Human Connectome Project, the errors on dMRI measures derived from the diffusion tensor imaging and diffusional kurtosis imaging are highlighted. We perform a group study to showcase the alteration in the significance and effect size due to ignoring gradient nonlinearity correction. Our results indicate that the effect of gradient field nonlinearities on dMRI studies can be significant and may complicate the interpretation of the results and conclusions.

Dosimetric Impact of MRI Distortions: A Study on Head and Neck Cancers

To evaluate the effect of magnetic resonance (MR) imaging (MRI) geometric distortions on head and neck radiation therapy treatment planning (RTP) for an MRI-only RTP. We also assessed the potential benefits of patient-specific shimming to reduce the magnitude of MR distortions for a 3-T scanner. Using an in-house Matlab algorithm, shimming within entire imaging volumes and user-defined regions of interest were simulated. We deformed 21 patient computed tomography (CT) images with MR distortion fields (gradient nonlinearity and patient-induced susceptibility effects) to create distorted CT (dCT) images using bandwidths of 122 and 488Hz/mm at 3T. Field parameters from volumetric modulated arc therapy plans initially optimized on dCT data sets were transferred to CT data to compute a new plan. Both plans were compared to determine the impact of distortions on dose distributions. Shimming across entire patient volumes decreased the percentage of voxels with distortions of more than 2mm from 15.4% to 2.0%. Using the user-defined region of interest (ROI) shimming strategy, (here the Planning target volume (PTV) was the chosen ROI volume) led to increased geometric for volumes outside the PTV, as such voxels within the spinal cord with geometric shifts above 2 mm increased from 11.5% to 32.3%. The worst phantom-measured residual system distortions after 3-dimensional gradient nonlinearity correction within a radial distance of 200mm from the isocenter was 2.17mm. For all patients, voxels with distortion shifts of more than 2mm resulting from patient-induced susceptibility effects were 15.4% and 0.0% using bandwidths of 122Hz/mm and 488Hz/mm at 3T. Dose differences between dCT and CT treatment plans in D50 at the planning target volume were 0.4%±0.6% and 0.3%±0.5% at 122 and 488Hz/mm, respectively. The overall effect of MRI geometric distortions on data used for RTP was minimal. Shimming over entire imaging volumes decreased distortions, but user-defined subvolume shimming introduced significant errors in nearby organs and should probably be avoided.

Improving apparent diffusion coefficient accuracy on a compact 3T MRI scanner using gradient nonlinearity correction.

Gradient nonlinearity (GNL) leads to biased apparent diffusion coefficients (ADCs) in diffusion-weighted imaging. A gradient nonlinearity correction (GNLC) method has been developed for whole body systems, but is yet to be tested for the new compact 3T (C3T) scanner, which exhibits more complex GNL due to its asymmetrical design. To assess the improvement of ADC quantification with GNLC for the C3T scanner. Phantom measurements and retrospective analysis of patient data. A diffusion quality control phantom with vials containing 0-30% polyvinylpyrrolidone in water was used. For in vivo data, 12 patient exams were analyzed (median age, 33). Imaging was performed on the C3T and two commercial 3T scanners. A clinical DWI (repetition time [TR] = 10,000 msec, echo time [TE] = minimum, b = 1000 s/mm2 ) sequence was used for phantom imaging and 10 patient cases and a clinical DTI (TR = 6000-10,000 msec, TE = minimum, b = 1000 s/mm2 ) sequence was used for two patient cases. The 0% vial was measured along three orthogonal axes, and at two different temperatures. The ADC for each concentration was compared between the C3T and two whole-body scanners. Cerebrospinal fluid and white matter ADCs were quantified for each patient and compared to values in literature. Paired t-test and two-way analysis of variance (ANOVA). For all PVP concentrations, the corrected ADC was within 2.5% of the reference ADC. On average, the ADC of cerebrospinal fluid and white matter post-GNLC were within 1% and 6%, respectively, of values reported in the literature and were significantly different from the uncorrected data (P < 0.05). This study demonstrated that GNL effects were more severe for the C3T due to the asymmetric gradient design, but our implementation of a GNLC compensated for these effects, resulting in ADC values that are in good agreement with values from the literature. 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1498-1507.

Phantom-based field maps for gradient nonlinearity correction in diffusion imaging.

Gradient coils in magnetic resonance imaging do not produce perfectly linear gradient fields. For diffusion imaging, the field nonlinearities cause the amplitude and direction of the applied diffusion gradients to vary over the field of view. This leads to site- and scan-specific systematic errors in estimated diffusion parameters such as diffusivity and anisotropy, reducing reliability especially in studies that take place over multiple sites. These errors can be substantially reduced if the actual scanner-specific gradient coil magnetic fields are known. The nonlinearity of the coil fields is measured by scanner manufacturers and used internally for geometric corrections, but obtaining and using the information for a specific scanner may be impractical for many sites that operate without special-purpose local engineering and research support. We have implemented an empirical field-mapping procedure using a large phantom combined with a solid harmonic approximation to the coil fields that is simple to perform and apply. Here we describe the accuracy and precision of the approach in reproducing manufacturer gold standard field maps and in reducing spatially varying errors in quantitative diffusion imaging for a specific scanner. Before correction, median B value error ranged from 33 - 41 relative to manufacturer specification at 100 mm from isocenter; correction reduced this to 0 - 4. On-axis spatial variation in the estimated mean diffusivity of an isotropic phantom was 2.2% - 4.1% within 60 mm of isocenter before correction, 0.5% - 1.6% after. Expected fractional anisotropy in the phantom was 0; highest estimated fractional anisotropy within 60 mm of isocenter was reduced from 0.024 to 0.012 in the phase encoding direction (48% reduction) and from 0.020 to 0.006 in the frequency encoding direction (72% reduction).

Gradient nonlinearity calibration and correction for a compact, asymmetric magnetic resonance imaging gradient system

Due to engineering limitations, the spatial encoding gradient fields in conventional magnetic resonance imaging cannot be perfectly linear and always contain higher-order, nonlinear components. If ignored during image reconstruction, gradient nonlinearity (GNL) manifests as image geometric distortion. Given an estimate of the GNL field, this distortion can be corrected to a degree proportional to the accuracy of the field estimate. The GNL of a gradient system is typically characterized using a spherical harmonic polynomial model with model coefficients obtained from electromagnetic simulation. Conventional whole-body gradient systems are symmetric in design; typically, only odd-order terms up to the 5th-order are required for GNL modeling. Recently, a high-performance, asymmetric gradient system was developed, which exhibits more complex GNL that requires higher-order terms including both odd- and even-orders for accurate modeling. This work characterizes the GNL of this system using an iterative calibration method and a fiducial phantom used in ADNI (Alzheimer’s Disease Neuroimaging Initiative). The phantom was scanned at different locations inside the 26 cm diameter-spherical-volume of this gradient, and the positions of fiducials in the phantom were estimated. An iterative calibration procedure was utilized to identify the model coefficients that minimize the mean-squared-error between the true fiducial positions and the positions estimated from images corrected using these coefficients. To examine the effect of higher-order and even-order terms, this calibration was performed using spherical harmonic polynomial of different orders up to the 10th-order including even- and odd-order terms, or odd-order only. The results showed that the model coefficients of this gradient can be successfully estimated. The residual root-mean-squared-error after correction using up to the 10th-order coefficients was reduced to 0.36 mm, yielding spatial accuracy comparable to conventional whole-body gradients. The even-order terms were necessary for accurate GNL modeling. In addition, the calibrated coefficients improved image geometric accuracy compared with the simulation-based coefficients.

MO-FG-CAMPUS-IeP3-02: Gradient Nonlinearity Calibration and Correction for Full-Volume Imaging of a Compact Asymmetric MRI Gradient System

Purpose: Due to engineering limitations, the spatial encoding gradient fields generated by an MRI scanner are inherently nonlinear and images must undergo geometric correction prior to display. The gradient nonlinearity (GNL) of a conventional symmetric whole-body gradient is typically characterized using a spherical harmonic polynomial (SHP) expansion of magnetic gradient fields with up to fifth-order terms. Only odd-order terms are required due to design symmetry (i.e., 3rd/5th-order). Recently, a high-performance asymmetric gradient system was developed which exhibits more complex GNL. Here, the GNL across the 26cm diameter-spherical-volume (DSV) of this system is characterized using a phantom-based iterative calibration procedure. Methods: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) phantom was scanned with a 3D IR-FSPGR sequence (sagittal acquisition, Nx=Ny=256, Nz=196, Δx=Δy=1.05mm, Δz=1.3mm, BW=±125kHz) on the asymmetric gradient system. The phantom (20cm diameter with 160 spherical fiducials of 1.0/1.5cm diameter) was imaged at isocenter and at spatial offsets of ±3cm along the superior/inferior, right/left, anterior/posterior axes to cover the 26cm DSV. The spatial positions of fiducials were measured from MR images and compared with the design positions to extract distortion field, which is then used to fit the SHP model using an iterative calibration procedure. The effect of higher-order terms was tested by incrementally increasing the model order from 5 to 10. The calibrated coefficients were then used to geometrically correct the images, and for each case the residual root-mean-square-error (RMSE) was determined. For comparison, RMSE was also determined for images corrected using coefficients obtained from electromagnetic simulation. Results: The calibrated coefficients (10th-order) reduce RMSE from 0.96mm (using a 10th-order correction from electromagnetic simulation) to 0.36mm. Conclusion: The model parameters for GNL correction over the 26cm DSV of an asymmetric gradient system were successfully determined. The use of calibrated coefficients up to 10thorder coefficients including even-order terms yielded improved geometric accuracy (RMSE) comparable to conventional symmetric gradients. Funding support: NIH R01EB010065

NonCartesian MR image reconstruction with integrated gradient nonlinearity correction.

To derive a noniterative gridding-type reconstruction framework for nonCartesian magnetic resonance imaging (MRI) that prospectively accounts for gradient nonlinearity (GNL)-induced image geometrical distortion during MR image reconstruction, as opposed to the standard, image-domain based GNL correction that is applied after reconstruction; to demonstrate that such framework is able to reduce the image blurring introduced by the conventional GNL correction, while still offering effective correction of GNL-induced geometrical distortion and compatibility with off-resonance correction. After introducing the nonCartesian MRI signal model that explicitly accounts for the effects of GNL and off-resonance, a noniterative gridding-type reconstruction framework with integrated GNL correction based on the type-III nonuniform fast Fourier transform (NUFFT) is derived. A novel type-III NUFFT implementation is then proposed as a numerically efficient solution to the proposed framework. The incorporation of simultaneous B0 off-resonance correction to the proposed framework is then discussed. Several phantom and in vivo data acquired via various 2D and 3D nonCartesian acquisitions, including 2D Archimedean spiral, 3D shells with integrated radial and spiral, and 3D radial sampling, are used to compare the results of the proposed and the standard GNL correction methods. Various phantom and in vivo data demonstrate that both the proposed and the standard GNL correction methods are able to correct the coarse-scale geometric distortion and blurring induced by GNL and off-resonance. However, the standard GNL correction method also introduces blurring effects to corrected images, causing blurring of resolution inserts in the phantom images and loss of small vessel clarity in the angiography examples. On the other hand, the results after the proposed GNL correction show better depiction of resolution inserts and higher clarity of small vessel. The proposed GNL-integrated nonCartesian reconstruction method can mitigate the resolution loss that occurs during standard image-domain GNL correction, while still providing effective correction of coarse-scale geometric distortion and blurring induced by GNL and off-resonance.

Partial fourier and parallel MR image reconstruction with integrated gradient nonlinearity correction.

To describe how integrated gradient nonlinearity (GNL) correction can be used within noniterative partial Fourier (homodyne) and parallel (SENSE and GRAPPA) MR image reconstruction strategies, and demonstrate that performing GNL correction during, rather than after, these routines mitigates the image blurring and resolution loss caused by postreconstruction image domain based GNL correction. Starting from partial Fourier and parallel magnetic resonance imaging signal models that explicitly account for GNL, noniterative image reconstruction strategies for each accelerated acquisition technique are derived under the same core mathematical assumptions as their standard counterparts. A series of phantom and in vivo experiments on retrospectively undersampled data were performed to investigate the spatial resolution benefit of integrated GNL correction over conventional postreconstruction correction. Phantom and in vivo results demonstrate that the integrated GNL correction reduces the image blurring introduced by the conventional GNL correction, while still correcting GNL-induced coarse-scale geometrical distortion. Images generated from undersampled data using the proposed integrated GNL strategies offer superior depiction of fine image detail, for example, phantom resolution inserts and anatomical tissue boundaries. Noniterative partial Fourier and parallel imaging reconstruction methods with integrated GNL correction reduce the resolution loss that occurs during conventional postreconstruction GNL correction while preserving the computational efficiency of standard reconstruction techniques. Magn Reson Med 75:2534-2544, 2016. © 2015 Wiley Periodicals, Inc.

TU-F-CAMPUS-I-04: Head-Only Asymmetric Gradient System Evaluation: ACR Image Quality and Acoustic Noise

Purpose: A high-performance head-only magnetic resonance imaging gradient system with an acquisition volume of 26 cm employing an asymmetric design for the transverse coils has been developed. It is able to reach a magnitude of 85 mT/m at a slew rate of 700 T/m/s, but operated at 80 mT/m and 500 T/m/s for this test. A challenge resulting from this asymmetric design is that the gradient nonlinearly exhibits both odd- and even-ordered terms, and as the full imaging field of view is often used, the nonlinearity is pronounced. The purpose of this work is to show the system can produce clinically useful images after an on-site gradient nonlinearity calibration and correction, and show that acoustic noise levels fall within non-significant risk (NSR) limits for standard clinical pulse sequences. Methods: The head-only gradient system was inserted into a standard 3T wide-bore scanner without acoustic damping. The ACR phantom was scanned in an 8-channel receive-only head coil and the standard American College of Radiology (ACR) MRI quality control (QC) test was performed. Acoustic noise levels were measured for several standard pulse sequences. Results: Images acquired with the head-only gradient system passed all ACR MR image quality tests; Both even and odd-order gradient distortion correction terms were required for the asymmetric gradients to pass. Acoustic noise measurements were within FDA NSR guidelines of 99 dBA (with assumed 20 dBA hearing protection) A-weighted and 140 dB for peak for all but one sequence. Note the gradient system was installed without any shroud or acoustic batting. We expect final system integration to greatly reduce noise experienced by the patient. Conclusion: A high-performance head-only asymmetric gradient system operating at 80 mT/m and 500 T/m/s conforms to FDA acoustic noise limits in all but one case, and passes all the ACR MR image quality control tests. This work was supported in part by the NIH grant 5R01EB010065

Quantitative characterizations of ultrashort echo (UTE) images for supporting air–bone separation in the head

Accurate separation of air and bone is critical for creating synthetic CT from MRI to support Radiation Oncology workflow. This study compares two different ultrashort echo-time sequences in the separation of air from bone, and evaluates post-processing methods that correct intensity nonuniformity of images and account for intensity gradients at tissue boundaries to improve this discriminatory power. CT and MRI scans were acquired on 12 patients under an institution review board-approved prospective protocol. The two MRI sequences tested were ultra-short TE imaging using 3D radial acquisition (UTE), and using pointwise encoding time reduction with radial acquisition (PETRA). Gradient nonlinearity correction was applied to both MR image volumes after acquisition. MRI intensity nonuniformity was corrected by vendor-provided normalization methods, and then further corrected using the N4itk algorithm. To overcome the intensity-gradient at air–tissue boundaries, spatial dilations, from 0 to 4 mm, were applied to threshold-defined air regions from MR images. Receiver operating characteristic (ROC) analyses, by comparing predicted (defined by MR images) versus ‘true’ regions of air and bone (defined by CT images), were performed with and without residual bias field correction and local spatial expansion. The post-processing corrections increased the areas under the ROC curves (AUC) from 0.944 ± 0.012 to 0.976 ± 0.003 for UTE images, and from 0.850 ± 0.022 to 0.887 ± 0.012 for PETRA images, compared to without corrections. When expanding the threshold-defined air volumes, as expected, sensitivity of air identification decreased with an increase in specificity of bone discrimination, but in a non-linear fashion. A 1 mm air mask expansion yielded AUC increases of 1 and 4% for UTE and PETRA images, respectively. UTE images had significantly greater discriminatory power in separating air from bone than PETRA images. Post-processing strategies improved the discriminatory power of air from bone for both UTE and PETRA images, and reduced the difference between the two imaging sequences. Both post-processed UTE and PETRA images demonstrated sufficient power to discriminate air from bone to support synthetic CT generation from MRI data.

Integrated image reconstruction and gradient nonlinearity correction.

To describe a model-based reconstruction strategy for routine magnetic resonance imaging that accounts for gradient nonlinearity (GNL) during rather than after transformation to the image domain, and demonstrate that this approach reduces the spatial resolution loss that occurs during strictly image-domain GNL-correction. After reviewing conventional GNL-correction methods, we propose a generic signal model for GNL-affected magnetic resonance imaging acquisitions, discuss how it incorporates into contemporary image reconstruction platforms, and describe efficient nonuniform fast Fourier transform-based computational routines for these. The impact of GNL-correction on spatial resolution by the conventional and proposed approaches is investigated on phantom data acquired at varying offsets from gradient isocenter, as well as on fully sampled and (retrospectively) undersampled in vivo acquisitions. Phantom results demonstrate that resolution loss that occurs during GNL-correction is significantly less for the proposed strategy than for the standard approach at distances >10 cm from isocenter with a 35 cm field-of-view gradient coil. The in vivo results suggest that the proposed strategy better preserves fine anatomical detail than retrospective GNL-correction while offering comparable geometric correction. Accounting for GNL during image reconstruction allows geometric distortion to be corrected with less spatial resolution loss than is typically observed with the conventional image domain correction strategy.

Improved correction for gradient nonlinearity effects in diffusion‐weighted imaging

To provide an improved correction for gradient nonlinearity (GN) effects in diffusion-weighted imaging (DWI). These effects produce spatially varying apparent diffusion coefficient (ADC), a result that will be significant in large field-of-view imaging, and may be confounded by distortion and concomitant fields related to the DWI acquisition. The effect of more accurate gradient field maps on GN correction (GNC) of ADC was evaluated. A simulation compared GN effects in commonly imaged anatomy. A temperature-controlled phantom was imaged at positions 0 cm and 11 cm from isocenter and in two whole-body MRI systems at 1.5T with different patient bore diameters (55 cm and 60 cm). Varying correction methods were applied to determine the errors from spatial variance and interscanner reproducibility. As compared to conventional fifth-order spherical harmonics, a seventh-order GNC improved ADC accuracy by 1%. The combination of GNC with a dual-spin-echo pulse sequence and a retrospective concomitant field correction reduced ADC error due to spatial variance from 9.5% to 1.8% (55 cm bore) and from 4.2% to 1.8% (60 cm bore). The error in ADC attributed to interscanner reproducibility was reduced from 5.8% to 0.15% (at isocenter) and from 10% to 0.63% (11 cm from isocenter). GNC in DWI improved spatial accuracy and interscanner reproducibility of ADC.