Phase contrast MRI with minimized background phase errors.

PURPOSE: Phase contrast MRI (PC-MRI) is used clinically to measure velocities in the body, but systematic background phase errors caused by magnetic field imperfections corrupt the velocity measurements with offsets that limit clinical utility. This work aims to minimize systematic background phase errors in PC-MRI, thereby maximizing the accuracy of velocity measurements. METHODS: The MRI scanner's background phase errors from eddy currents and mechanical oscillations were modeled using the gradient impulse response function (GIRF). Gradient waveforms were then numerically optimized using the GIRF to create pulse sequences that minimize the background phase errors. The pulse sequences were tested in a static phantom where the predicted response could be compared directly to the measured background velocity. The optimized acquisitions were then tested in human subjects, where flow rates and background errors were compared to conventional PC-MRI. RESULTS: When using the GIRF-optimized gradient waveforms, the predicted background phase was within 0.6 [95% CI = -3.4, 5.4] mm/s of the measured background phase in a static phantom. Excellent agreement was seen for in vivo blood flow values (flow rate agreement r 2 $$ {r}^2 $$ = 0.96), and the background phase was reduced by 78.8 ± $$ \pm $$ 18.7%. CONCLUSION: This work shows that using a GIRF to model the effects of magnetic field imperfections combined with numerically optimized gradient waveforms enables PC-MRI waveforms to be designed to produce a minimal background phase in the most time-efficient manner. The methodology could be adapted for other MRI sequences where similar magnetic field errors affect measurements.

Fully automated background phase correction using M-estimate SAmple consensus (MSAC)-Application to 2D and 4D flow.

PURPOSE: Flow quantification by phase-contrast MRI is hampered by spatially varying background phase offsets. Correction performance by polynomial regression on stationary tissue may be affected by outliers such as wrap-around or constant flow. Therefore, we propose an alternative, M-estimate SAmple Consensus (MSAC) to reject outliers, and improve and fully automate background phase correction. METHODS: The MSAC technique fits polynomials to randomly drawn small samples from the image. Over several trials, it aims to find the best consensus set of valid pixels by rejecting outliers to the fit and minimizing the residuals of the remaining pixels. The robustness of MSAC to its few parameters was investigated and verified using third-order polynomial correction fits on a total of 118 2D flow (97 with wrap-around) and 18 4D flow data sets (14 with wrap-around), acquired at 1.5 T and 3 T. Background phase was compared with standard stationary correction and phantom correction. Pulmonary/systemic flow ratios in 2D flow were derived, and exemplary 4D flow analysis was performed. RESULTS: The MSAC technique is robust over a range of parameter choices, and a unique set of parameters is suitable for both 2D and 4D flow. In 2D flow, phase errors were significantly reduced by MSAC compared with stationary correction (p = 0.005), and stationary correction shows larger errors in pulmonary/systemic flow ratios compared with MSAC. In 4D flow, MSAC shows similar performance as stationary correction. CONCLUSIONS: The MSAC method provides fully automated background phase correction to 2D and 4D flow data and shows improved robustness over stationary correction, especially with outliers present.

Comparison of quantitative susceptibility mapping methods on evaluating radiation-induced cerebral microbleeds and basal ganglia at 3T and 7T.

Quantitative susceptibility mapping (QSM) has the potential for being a biomarker for various diseases because of its ability to measure tissue susceptibility related to iron deposition, myelin, and hemorrhage from the phase signal of a T2 *-weighted MRI. Despite its promise as a quantitative marker, QSM is faced with many challenges, including its dependence on preprocessing of the raw phase data, the relatively weak tissue signal, and the inherently ill posed relationship between the magnetic dipole and measured phase. The goal of this study was to evaluate the effects of background field removal and dipole inversion algorithms on noise characteristics, image uniformity, and structural contrast for cerebral microbleed (CMB) quantification at both 3T and 7T. We selected four widely used background phase removal and five dipole field inversion algorithms for QSM and applied them to volunteers and patients with CMBs, who were scanned at two different field strengths, with ground truth QSM reference calculated using multiple orientation scans. 7T MRI provided QSM images with lower noise than did 3T MRI. QSIP and VSHARP + iLSQR achieved the highest white matter homogeneity and vein contrast, with QSIP also providing the highest CMB contrast. Compared with ground truth COSMOS QSM images, overall good correlations between susceptibility values of dipole inversion algorithms and the COSMOS reference were observed in basal ganglia regions, with VSHARP + iLSQR achieving the susceptibility values most similar to COSMOS across all regions. This study can provide guidance for selecting the most appropriate QSM processing pipeline based on the application of interest and scanner field strength.

Flow quantification dependency on background phase correction techniques in 4D-flow MRI.

PURPOSE: To analyze the dependence of flow volume measurements on 3D cine phase-contrast MRI (4D-flow MRI) background phase correction. METHODS: In 31 subjects scanned on a 1.5T MRI scanner, flow volume measurements at 4 vessels were made using phantom corrected 2D phase contrast and 4D flow with background phase correction performed by linear, second, third, and fourth-order polynomial fitting to static tissue. Variations in the amount and distribution of static tissue were made to investigate the influence on flow volume measurements. RESULTS: Bland Altman comparison of 2D phase-contrast and 4D-flow measurements showed low bias (2.3%-4.8%) and relatively large limits of agreement (13.5%-17.6%). Approximately half of this was attributable to sequence and physiological differences between the 2 scan sequences, demonstrated by smaller limits of agreement (5.3%-10.0%) when comparing 4D-flow measurements with differing background phase corrections. Using only 20% of available static tissue points for polynomial fitting resulted in only 1% difference in flow volume measurements. Using asymmetrically distributed static tissue or including nonstatic tissue for polynomial fitting yielded highly variable differences in flow volume measurements, which became more variable with increased polynomial order. Completely asymmetric static tissue selection resulted in high deviations in flow volume measurements (mean > 7%, max = 345%). CONCLUSION: Comparisons between 2D phase-contrast and 4D-flow volume measurements should consider influences from sequence and physiological differences. A subset of static tissue points may be used with low impact on flow measurements, but should avoid the inclusion of nonstatic tissue and avoid asymmetric distribution. Higher-order polynomial fits are more susceptible to inaccurate static tissue selection.

Data Quality and Optimal Background Correction Order of Respiratory-Gated k-Space Segmented Spoiled Gradient Echo (SGRE) and Echo Planar Imaging (EPI)-Based 4D Flow MRI.

BACKGROUND: A reduction in scan time of 4D Flow MRI would facilitate clinical application. A recent study indicates that echo-planar imaging (EPI) 4D Flow MRI allows for a reduction in scan time and better data quality than the recommended k-space segmented spoiled gradient echo (SGRE) sequence. It was argued that the poor data quality of SGRE was related to the nonrecommended absence of respiratory motion compensation. However, data quality can also be affected by the background offset compensation. PURPOSE: To compare the data quality of respiratory motion-compensated SGRE and EPI 4D Flow MRI and their dependence on background correction (BC) order. STUDY TYPE: Retrospective. SUBJECTS: Eighteen healthy subjects (eight female, mean age 32 ± 5 years). FIELD STRENGTH AND SEQUENCE: 1.5 T. [Correction added on July 26, 2019, after first online publication: The preceding field strength was corrected.] SGRE and EPI-based 4D Flow MRI. ASSESSMENT: Data quality was investigated visually and by comparing flows through the cardiac valves and aorta. Measurements were obtained from transvalvular flow and pathline analysis. STATISTICAL TESTS: Linear regression and Bland-Altman analysis were used. Wilcoxon test was used for comparison of visual scoring. Student's t-test was used for comparison of flow volumes. RESULTS: No significant difference was found by visual inspection (P = 0.08). Left ventricular (LV) flows were strongly and very strongly associated with SGRE and EPI, respectively (R2 = 0.86-0.94 SGRE; 0.71-0.79 EPI, BC0-4). LV and right ventricular (RV) outflows and LV pathline flows were very strongly associated (R2 = 0.93-0.95 SGRE; 0.88-0.91 EPI, R2 = 0.91-0.95 SGRE; 0.91-0.93 EPI, BC1-4). EPI LV outflow was lower than the short-axis-based stroke volume. EPI RV outflow and proximal descending aortic flow were lower than SGREs. DATA CONCLUSION: Both sequences yielded good internal data consistency when an adequate background correction was applied. Second and first BC order were considered sufficient for transvalvular flow analysis in SGRE and EPI, respectively. Higher BC orders were preferred for particle tracing. Level of Evidence 4 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2020;51:885-896.

Relationship between gain rate during the growing phase and forage allowance in the finishing phase in Nellore cattle.

The study evaluated the effect of gain rates during the growing phase and the possible interaction with finishing phase forage allowance in beef cattle. In Exp. 1, eighty Nellore cattle (386 ± 7.90 kg; divided into 16 paddocks) were used in a randomized block design in a 2 × 2 factorial arrangement of treatments: (1) gain rate during the growing phase (low and high); (2) forage allowance during the finishing phase (low and high). In Exp. 2, twelve cannulated animals were used to evaluate ruminal parameters at the finishing phase. The animals were supplemented (2% of body weight [BW]) during 112 days. The average forage mass was 6507 and 2712 kg DM/ha, resulting in a forage allowance of 2.91 and 1.31 kg DM/kg BW, respectively, for high and low supply. In Exp. 1, there was interaction among factors (P = 0.01) for average daily gain (ADG): low gain rate animals that were finished on high forage allowance had an ADG 0.203 kg/day greater than average of other treatments (0.917 kg/day). Animals with a high gain rate in the growing phase started the finishing phase 51.5 kg heavier than low gain rate animals; this difference dropped to 35.5 kg in final BW (P < 0.01). In terms of the carcass, this difference was 27 kg at the beginning and dropped to 25 kg at the final carcass weight (P < 0.01). In Exp. 2, the acetate concentration at the end was higher for animals fed high forage allowance compared to low. However, propionate concentration was higher for animals fed low forage allowance compared to high (P = 0.01). Our results showed that the growing phase influences performance during the finishing phase; however, forage allowance with high supplementation at the finishing phase had negligible effects under these experimental conditions.

A method to correct background phase offset for phase-contrast MRI in the presence of steady flow and spatial wrap-around artifact.

PURPOSE: Background phase offsets in phase-contrast MRI are often corrected using polynomial regression; however, correction performance degrades when temporally invariant outliers such as steady flow or spatial wrap-around artifact are present. We describe and validate an iterative method called automatic rejection of temporally invariant outliers (ARTO), which excludes these outliers from the fitting process. METHODS: The ARTO method iteratively removes pixels with large polynomial regression errors analyzed by a Gaussian mixture model fitting of the residual distribution. A total of 150 trials of a simulated phantom (75 with wrap-around artifact) and 125 phase-contrast MRI cines from 22 healthy subjects (48 with wrap-around artifact) were used for validation. Background phase offsets were corrected using second-order weighted regularized least squares (WRLS) with and without ARTO. Flow volumes after WRLS and WRLS+ARTO corrections were compared with the known truth (phantom) and stationary phantom reference (in vivo) using Bland-Altman analysis. The ratio between the pulmonary flow and the systemic flow was also computed in a subset of 6 subjects. RESULTS: In the simulated phantom, compared with WRLS and no correction, correction with WRLS+ARTO produced superior agreement in volumetric flow quantification with the known truth. In vivo, WRLS+ARTO also produced superior agreement with stationary phantom-corrected volumetric flow compared with WRLS and no correction. In data sets with wrap-around artifact, WRLS produced significantly larger variance in the pulmonary flow and systemic flow ratio than stationary phantom correction (P = .0008). CONCLUSION: The proposed method provides automatic exclusion of temporally invariant outliers and produces flow quantification results comparable to stationary phantom correction.

Caval to pulmonary 3D flow distribution in patients with Fontan circulation and impact of potential 4D flow MRI error sources.

PURPOSE: Uneven flow distribution in patients with Fontan circulation is suspected to lead to complications. 4D flow MRI offers evaluation using time-resolved pathlines; however, the potential error is not well understood. The aim of this study was to systematically assess variability in flow distribution caused by well-known sources of error. METHODS: 4D flow MRI was acquired in 14 patients with Fontan circulation. Flow distribution was quantified by the % of caval venous flow pathlines reaching the left and right pulmonary arteries. Impact of data acquisition and data processing uncertainties were investigated by (1) probabilistic 4D blood flow tracking at varying noise levels, (2) down-sampling to mimic acquisition at different spatial resolutions, (3) pathline calculation with and without eddy current correction, and (4) varied segmentation of the Fontan geometry to mimic analysis errors. RESULTS: Averaged among the cohort, uncertainties accounted for flow distribution errors from noise ≤3.2%, low spatial resolution ≤2.3% to 3.8%, eddy currents ≤6.4%, and inaccurate segmentation ≤3.9% to 9.1% (dilation and erosion, respectively). In a worst-case scenario (maximum additive errors for all 4 sources), flow distribution errors were as high as 22.5%. CONCLUSION: Inaccuracies related to postprocessing (segmentation, eddy currents) resulted in the largest potential error (≤15.5% combined) whereas errors related to data acquisition (noise, low spatial resolution) had a lower impact (≤5.5%-7.0% combined). Whereas it is unlikely that these errors will be additive or affect the identification of severe asymmetry, these results illustrate the importance of eddy current correction and accurate segmentation to minimize Fontan flow distribution errors.

Joint 2D and 3D phase processing for quantitative susceptibility mapping: application to 2D echo-planar imaging.

Quantitative susceptibility mapping (QSM) measures tissue magnetic susceptibility and typically relies on time-consuming three-dimensional (3D) gradient-echo (GRE) MRI. Recent studies have shown that two-dimensional (2D) multi-slice gradient-echo echo-planar imaging (GRE-EPI), which is commonly used in functional MRI (fMRI) and other dynamic imaging techniques, can also be used to produce data suitable for QSM with much shorter scan times. However, the production of high-quality QSM maps is difficult because data obtained by 2D multi-slice scans often have phase inconsistencies across adjacent slices and strong susceptibility field gradients near air-tissue interfaces. To address these challenges in 2D EPI-based QSM studies, we present a new data processing procedure that integrates 2D and 3D phase processing. First, 2D Laplacian-based phase unwrapping and 2D background phase removal are performed to reduce phase inconsistencies between slices and remove in-plane harmonic components of the background phase. This is followed by 3D background phase removal for the through-plane harmonic components. The proposed phase processing was evaluated with 2D EPI data obtained from healthy volunteers, and compared against conventional 3D phase processing using the same 2D EPI datasets. Our QSM results were also compared with QSM values from time-consuming 3D GRE data, which were taken as ground truth. The experimental results show that this new 2D EPI-based QSM technique can produce quantitative susceptibility measures that are comparable with those of 3D GRE-based QSM across different brain regions (e.g. subcortical iron-rich gray matter, cortical gray and white matter). This new 2D EPI QSM reconstruction method is implemented within STI Suite, which is a comprehensive shareware for susceptibility imaging and quantification. Copyright © 2016 John Wiley & Sons, Ltd.

Image-based background phase error correction in 4D flow MRI revisited.

PURPOSE: To correct background phase errors in phase-contrast magnetic resonance imaging (MRI), image-based correction by referencing through stationary tissue is widely used. The aim of the present study was a detailed assessment of background phase errors in 4D Flow MRI and limitations of image-based correction. MATERIALS AND METHODS: In a phantom study, 4D Flow MRI data were acquired for typical settings on two clinical 3T MR systems. Background errors were analyzed with respect to their spatial order and minimum requirements regarding the signal-to-noise ratio (SNR) and the amount of stationary tissue for image-based correction were assessed. For in vivo evaluation, data of the aorta were acquired on one 3T MR system in five healthy subjects including subsequent scans on the stationary phantom as reference. RESULTS: Background errors were found to exhibit spatial variation of first- to third-order. For correction, a minimum SNR of 20 was needed to achieve an error of less than 0.4% of the encoding velocity. The minimum amount of stationary tissue was strongly dependent on the spatial order requiring at least 25%, 60%, and 75% of stationary tissue for first-, second-, and third-order correction. In vivo evaluation showed that with 35-41% of stationary tissue available only first-order correction yielded a significant reduction (P < 0.01). CONCLUSION: Background phase errors can range from first to third spatial order in 4D Flow MRI requiring correction with appropriate polynomials. At the same time, the limited amount of stationary tissue available in vivo limits image-based background phase correction to first spatial order. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1516-1525.

Integrated Laplacian-based phase unwrapping and background phase removal for quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) is a recently developed MRI technique that provides a quantitative measure of tissue magnetic susceptibility. To compute tissue magnetic susceptibilities based on gradient echoes, QSM requires reliable unwrapping of the measured phase images and removal of contributions caused by background susceptibilities. Typically, the two steps are performed separately. Here, we present a method that simultaneously performs phase unwrapping and HARmonic (background) PhasE REmovaL using the LAplacian operator (HARPERELLA). Both numerical simulations and in vivo human brain images show that HARPERELLA effectively removes both phase wraps and background phase, whilst preserving all low spatial frequency components originating from brain tissues. When compared with other QSM phase preprocessing techniques, such as path-based phase unwrapping followed by background phase removal, HARPERELLA preserves the tissue phase signal in gray matter, white matter and cerebrospinal fluid with excellent robustness, providing a convenient and accurate solution for QSM. The proposed algorithm is provided, together with QSM and susceptibility tensor imaging (STI) tools, in a shared software package named 'STI Suite'.

Accuracy of four-dimensional phase-contrast velocity mapping for blood flow visualizations: a phantom study.

BACKGROUND: Time-resolved three-dimensional, three-directional phase-contrast magnetic resonance velocity mapping (4D PC-MRI) is a powerful technique to depict dynamic blood flow patterns in the human body. However, the impact of phase background effects on flow visualizations has not been thoroughly studied previously, and it has not yet been experimentally demonstrated to what degree phase offsets affect flow visualizations and create errors such as inaccurate particle traces. PURPOSE: To quantify background phase offsets and their subsequent impact on particle trace visualizations in a 4D PC-MRI sequence. Additionally, we sought to investigate to what degree visualization errors are reduced by background phase correction. MATERIAL AND METHODS: A rotating phantom with a known velocity field was used to quantify background phase of 4D PC-MRI sequences accelerated with SENSE as well as different k-t BLAST speed-up factors. The deviation in end positions between particle traces in the measured velocity fields were compared before and after the application of two different phase correction methods. RESULTS: Phantom measurements revealed background velocity offsets up to 7 cm/s (7% of velocity encoding sensitivity) in the central slice, increasing with distance from the center. Background offsets remained constant with increasing k-t BLAST speed-up factors. End deviations of up to 5.3 mm (1.8 voxels) in the direction perpendicular to the rotating disc were found between particle traces and the seeding plane of the traces. Phase correction by subtraction of the data from the stationary phantom reduced the average deviation by up to 56%, while correcting the data-set with a first-order polynomial fit to stationary regions decreased average deviation up to 78%. CONCLUSION: Pathline visualizations can be significantly affected by background phase errors, highlighting the importance of dedicated and robust phase correction methods. Our results show that pathline deviation can be substantial if adequate phase background errors are not minimized.