Multi-echo Gradient Recalled Echo Sequence Research Articles

Improved MR temperature imaging at 0.5T using view-sharing accelerated multiecho thermometry for MR-guided laser interstitial thermal therapy.

The aim of the current study was to improve temperature-monitoring precision using multiecho proton resonance frequency shift-based thermometry with view-sharing acceleration for MR-guided laser interstitial thermal therapy (MRgLITT) on a 0.5-T low-field MR system. Both precision and speed of the temperature measurement for clinical MRgLITT treatments suffer at low field, due to reduced image signal-to-noise ratio (SNR), decreased temperature-induced phase changes, and limited RF receiver channels. In this work, a bipolar multiecho gradient-recalled echo sequence with a temperature-to-noise ratio optimal weighted echo combination is applied to improve the temperature precision. A view-sharing-based approach is utilized to accelerate signal acquisitions while preserving image SNRs. The method was evaluated using ex vivo (pork and pig brain) LITT heating experiments and in vivo (human brain) nonheating experiments on a high-performance 0.5-T scanner. In terms of results, (1) after echo combination, multiecho thermometry (i.e., ~7.5-40.5ms, 7 TEs) provides ~1.5-1.9 times higher temperature precision than the no echo combination case (i.e., TE7 = 40.5ms) within the same readout bandwidth. Additionally, echo registration is necessary for the bipolar multiecho sequence; (2) for a threefold acceleration, the view-sharing approach with variable-density subsampling shows around 1.8 times lower temperature errors than the GRAPPA method. Particularly for view-sharing, variable-density subsampling performs better than Interleave subsampling; and (3) ex vivo heating and in vivo nonheating experiments demonstrated that the temperature accuracy was less than 0.5 and that the temperature precision was less than 0.6 using the proposed 0.5-T thermometry. It was concluded that view-sharing accelerated multiecho thermometry is a practical temperature measurement approach for MRgLITT at 0.5T.

Fat-Corrected Pancreatic R2* Relaxometry from Multi-Echo Gradient-Recalled Echo Sequence Using Convolutional Neural Network

Fat-corrected R2* relaxometry from multi-echo gradient-recalled echo sequences (mGRE) could represent an efficient approach for iron overload evaluation, but its use is limited by computational constraints. A new method for the fast generation of R2* and fat fractions (FF) maps from mGRE using a convolutional neural network (U-Net) and deep learning (DL) is presented. A U-Net for the calculation of pancreatic R2* and FF maps was trained with 576 mGRE abdominal images and compared to conventional fat-corrected relaxometry. The U-Net was effectively trained and provided R2* and FF maps visually comparable to conventional methods. Predicted pancreatic R2* and FF values were well correlated with the conventional model. Estimated and ground truth mean R2* values were not significantly different (43.65 ± 21.89 vs. 43.77 ± 19.81 ms, p = 0.692, intraclass correlation coefficient-ICC = 0.9938, coefficient of variation-CoV = 5.3%), while estimated FF values were slightly higher in respect to ground truth values (27.8 ± 16.87 vs. 25.67 ± 15.43 %, p < 0.0001, ICC = 0.986, CoV = 10.1%). Deep learning utilizing the U-Net is a feasible method for pancreatic MR fat-corrected relaxometry. A trained U-Net can be efficiently used for MR fat-corrected relaxometry, providing results comparable to conventional model-based methods.

Pancreas MRI Segmentation Into Head, Body, and Tail Enables Regional Quantitative Analysis of Heterogeneous Disease.

Quantitative imaging studies of the pancreas have often targeted the three main anatomical segments, head, body, and tail, using manual region of interest strategies to assess geographic heterogeneity. Existing automated analyses have implemented whole-organ segmentation, providing overall quantification but failing to address spatial heterogeneity. To develop and validate an automated method for pancreas segmentation into head, body, and tail subregions in abdominal MRI. Retrospective. One hundred and fifty nominally healthy subjects from UK Biobank (100 subjects for method development and 50 subjects for validation). A separate 390 UK Biobank triples of subjects including type 2 diabetes mellitus (T2DM) subjects and matched nondiabetics. A 1.5 T, three-dimensional two-point Dixon sequence (for segmentation and volume assessment) and a two-dimensional axial multiecho gradient-recalled echo sequence. Pancreas segments were annotated by four raters on the validation cohort. Intrarater agreement and interrater agreement were reported using Dice overlap (Dice similarity coefficient [DSC]). A segmentation method based on template registration was developed and evaluated against annotations. Results on regional pancreatic fat assessment are also presented, by intersecting the three-dimensional parts segmentation with one available proton density fat fraction (PDFF) image. Wilcoxon signed rank test and Mann-Whitney U-test for comparisons. DSC and volume differences for evaluation. A P value < 0.05 was considered statistically significant. Good intrarater (DSC mean, head: 0.982, body: 0.940, tail: 0.961) agreement and interrater (DSC mean, head: 0.968, body: 0.905, tail: 0.943) agreement were observed. No differences (DSC, head: P=0.4358, body: P=0.0992, tail: P=0.1080) were observed between the manual annotations and our method's segmentations (DSC mean, head: 0.965, body: 0.893, tail: 0.934). Pancreatic body PDFF was different between T2DM and nondiabetics matched by body mass index. The developed segmentation's performance was no different from manual annotations. Application on type 2 diabetes subjects showed potential for assessing pancreatic disease heterogeneity. 4 TECHNICAL EFFICACY STAGE: 3.

Robust water-fat separation for multi-echo gradient-recalled echo sequence using convolutional neural network.

To accurately separate water and fat signals for bipolar multi-echo gradient-recalled echo sequence using a convolutional neural network (CNN). A CNN architecture was designed and trained using the relationship between multi-echo images from the bipolar multi-echo gradient-recalled echo sequence and artifact-free water-fat-separated images. The artifact-free water-fat-separated images for training the CNN were obtained from multiple signals with different TEs by using iterative decomposition of water and fat with echo asymmetry and the least-squares estimation method, in which multiple signals at different TEs were acquired using a single-echo gradient-recalled echo sequence. We also proposed a data augmentation method using a synthetic field inhomogeneity to generate multi-echo signals, including various bipolar multi-echo gradient-recalled echo artifacts so that the CNN could prevent overfitting and increase the separation accuracy. We trained the CNN using in vivo knee images and tested it using in vivo knee, head, and ankle images. In vivo imaging results showed that the proposed CNN could separate water-fat images accurately. Although the proposed CNN was trained using only in vivo knee images, the proposed CNN could also separate water-fat images of different imaging regions. The proposed data augmentation method could prevent overfitting even with a limited number of training data sets and make the method robust to magnetic field inhomogeneities. The proposed CNN could obtain water-fat-separated images from the multi-echo images acquired from the bipolar multi-echo gradient-recalled echo sequence, which included artifacts from the bipolar gradients.

Placental baseline conditions modulate the hyperoxic BOLD-MRI response

ObjectivesHuman pregnancies complicated by placental dysfunction may be characterized by a high hyperoxic Blood oxygen level-dependent (BOLD) MRI response. The pathophysiology behind this phenomenon remains to be established. The aim of this study was to evaluate whether it is associated with altered placental baseline conditions, including a lower oxygenation and altered tissue morphology, as estimated by the placental transverse relaxation time (T2*). MethodWe included 49 normal pregnancies (controls) and 13 pregnancies complicated by placental dysfunction (cases), defined by a birth weight < 10th percentile in combination with placental pathological signs of vascular malperfusion. During maternal oxygen inhalation, we measured the relative ΔBOLD response ((hyperoxic BOLD – baseline BOLD)/baseline BOLD) from a dynamic single-echo gradient-recalled echo (GRE) MRI sequence and the absolute ΔT2* (hyperoxic T2*– baseline T2*) from breath-hold multi-echo GRE sequences. ResultsIn the control group, the relative ΔBOLD response increased during gestation from 5% in gestational week 20 to 20% in week 40. In the case group, the relative ΔBOLD response was significantly higher (mean Z-score 4.94; 95% CI 2.41, 7.47). The absolute ΔT2*, however, did not differ between controls and cases (p = 0.37), whereas the baseline T2* was lower among cases (mean Z-score −3.13; 95% CI -3.94, −2.32). Furthermore, we demonstrated a strong negative linear correlation between the Log10 ΔBOLD response and the baseline T2* (r = −0.88, p < 0.0001). ConclusionThe high hyperoxic ΔBOLD response demonstrated in pregnancies complicated by placental dysfunction may simply reflect altered baseline conditions, as the absolute increase in placental oxygenation (ΔT2*) does not differ between groups.

Long-term sequelae of hippocampal lesions in patients with transient global amnesia: A multiparametric MRI study.

MRI signal diffusion-weighted imaging (DWI) hyperintensity in the hippocampus in patients with transient global amnesia (TGA) are resolved within several days after the onset of TGA. To use multiparametric MRI to unravel the sequelae of TGA. A prospective longitudinal study. Eight TGA patients. A 3.0T Siemens Tim Trio MRI scanner with T1 -weighted MPRAGE, diffusion-weighted echo planar imaging, and multiecho gradient-recalled echo sequences. Brain MRI scanned within 72 hours, 2 weeks, and 3 months after onset of TGA, respectively. T1 image hippocampus was first segmented into 12 subregions using FreeSurfer and registered to DWI to locate DWI lesion. Then a T1 image with segmented hippocampus was registered to its corresponding apparent diffusion coefficient (ADC) map, fractional anisotropy (FA) map, and quantitative susceptibility map, respectively. Finally, the volume, water diffusion and anisotropy, and magnetic susceptibility of DWI lesion were analyzed. A paired samples t-test was performed to detect measurement differences between three tests. Pearson correlation was used to assess the correlations between all measurements. Hyperintensity was detected in the head, body, and caudate of CA1 and hippocampal tail. No significant changes existed in CA1/unit volume across the three timepoint measurements (all, P > 0.480). In Test 1, ratio ADC (DWI lesion / adjacent healthy, rADC) and ratio FA (rFA) decreased below, while ratio susceptibility increased above 1.0. In Test 2, all the episodes normalized around 1.0. In Test 3, rADC remained normalization, but rFA decreased and ratio susceptibility increased again. In Test 1 and Test 3 (vs. Test 2), decreased FA values were accompanied with lower axial diffusivity and/or higher radial diffusivity (all, P < 0.001). Moreover, rFA significantly correlated with ratio susceptibility in Test 3 (r = -0.665, P = 0.039). Microstructural sequelae in hippocampus were demonstrated, which indicates that ischemic lesions may be associated with TGA. 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1350-1358.

Can transverse relaxation rates in deep gray matter be approximated from functional and T2-weighted FLAIR scans for relative brain iron quantification?

Alterations in iron concentration in certain deep gray matter regions are known to occur in aging and several clinical conditions. In vivo measurements of R2∗ transverse relaxation rates and quantitative susceptibility mapping (QSM) have been shown to be strongly correlated with iron concentration in tissue, but their calculation requires the acquisition of a multi-echo gradient recalled echo sequence (MGRE). In the current study, we examined the feasibility of approximating R2∗ rates using metrics derived from fMRI-EPI and T2-weighted FLAIR images, which are widely available. In a sample of 40 healthy subjects, we obtained these metrics (vEPI and vFLAIR), as well as R2∗ rates and QSM estimates, and found significant correlations between vEPI and vFLAIR and R2∗ rates in several subcortical gray matter regions known to accumulate iron, but not in a control corticospinal white matter region. These relationships were preserved after referencing vEPI and vFLAIR with respect to the values in the control region. Effect sizes (above 0.5 for some of the regions, particularly the largest ones) were calculated and put in relation to those of the correlation between QSM and R2∗ rates. We propose that the metrics described here may be applied, possibly in a retrospective fashion, to analyze datasets with available EPI or T2-weighted FLAIR scans (and lacking a MGRE sequence), to devise new hypotheses regarding links between iron concentration in brain tissue and other variables of interest.

T2* mapping of subtalar cartilage: Precision and association between anatomical variants and cartilage composition.

Hindfoot arthritis is an important contributor to foot pain and physical disability. While the subtalar joint (STJ) is most frequently affected, anatomical variants such as facet configuration were suggested to further STJ cartilage deterioration. T2* mapping enables detection of ultra-structural cartilage change, particularly in thin cartilage layers, but its feasibility in the STJ has not yet been evaluated. The purpose of this study was to evaluate segmentation consistency and inter-scan short-term precision error of T2* mapping of talocalcaneal cartilage and to investigate the relationship between facet configuration and STJ T2* values. Using 3Tesla morphological magnetic resonance imaging, STJ configuration was categorized according to the degree of fusion between anterior, medial, or posterior facets. Subsequently, two repeats of multi-echo gradient recalled echo sequences were performed to obtain T2* maps with repositioning. Segmentation consistency of T2* values attained an ICC of 0.90 (95%CI 0.69-0.99). Precision errors comprised a coefficient of variation (CV) ranging 0.01-0.05, corresponding to a root mean square CV of 0.03-0.04. A 2-joint configuration type (i.e., fused anterior-medial facets) was significantly associated with a decrease in posterior facet T2* values (β = -0.6, p = 0.046). STJ T2* mapping is a reliable method requiring at least a 4% difference within people to enable detection of significant change. Anatomical variants in STJ configuration were associated with T2* values with the more stable 3-joint types exhibiting more favorable cartilage outcomes. Longer-term larger-scaled studies focusing on arthritis pathology are needed to further support the use of T2* mapping in hindfoot disease monitoring. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 34:1969-1976, 2016.

Renal Blood Oxygenation Level-Dependent Magnetic Resonance Imaging: A Sensitive and Objective Analysis.

The aim of this study was to determine a robust (sensitive and objective) method for analyzing renal blood oxygenation level-dependent magnetic resonance imaging data. Forty-seven subjects (30 with chronic kidney disease [CKD] and 17 controls) were imaged at baseline and after furosemide with a multiecho gradient recalled echo sequence. Conventional analysis consisted of regional segmentation (small cortex, large cortex, and medulla), followed by computing the mean of each region. In addition, we segmented the entire parenchyma and computed the mean (μ1) plus higher moments (μ2, μ3, and μ4). Two raters performed each of the segmentation steps, and agreement was assessed with intraclass correlation coefficients (ICCs). We used a measure of effect size (Cohen's d value), in addition to the usual measure of statistical significance, P values, for determining significant results. The mean of the renal parenchyma showed the highest agreement between raters (ICC, 0.99), and the higher parenchyma moments were on par with large cortical region of interest (ROI) ICC. The renal parenchymal mean also exhibited significant sensitivity to changes after furosemide administration in healthy subjects (P = 0.002, d = 0.84), in agreement with medullary ROIs (P = 0.002, d = 1.59). When comparing controls and subjects with CKD at baseline, cortical ROI showed a significant difference (P = 0.015, d = -0.69), whereas the parenchyma ROI did not (P = 0.152, d = 0.39). Post-furosemide data in all regions resulted in a significant difference (large cortex: P = 0.026, d = -0.51; medulla: P = 0.019, d = -0.61) with the renal parenchyma ROI resulting in the largest effect size (P = 0.003, d = -0.75). Higher moments of the renal parenchyma showed similar significant differences as well. Overall, our data support the use of the entire parenchyma to evaluate changes in the medulla after administration of furosemide, a widely used pharmacological maneuver. Changes in higher moments indicate that there is more than just a shift in the mean renal R2* and may provide clinically relevant information without the need for subjective regional segmentation. For evaluating differences between controls and subjects with CKD at baseline; large cortical ROI provided the highest sensitivity and objectivity. A combination of renal parenchyma assessment and large cortical ROI may provide the most robust method of evaluating renal blood oxygenation level-dependent magnetic resonance imaging data.

In vivo deformation of thin cartilage layers: Feasibility and applicability of T2* mapping.

The objectives of this study were as follows: (i) to assess segmentation consistency and scan precision of T2* mapping of human tibio-talar cartilage, and (ii) to monitor changes in T2* relaxation times of ankle cartilage immediately following a clinically relevant in vivo exercise and during recovery. Using multi-echo gradient recalled echo sequences, averaged T2* values were calculated for tibio-talar cartilage layers in 10 healthy volunteers. Segmentation consistency and scan precision were determined from two repeated segmentations and two repeated acquisitions with repositioning, respectively. Subsequently, acute in vivo cartilage loading responses were monitored by calculating averaged tibio-talar T2* values at rest, immediately after (i.e., deformation) and at 15 min (i.e., recovery) following a 30-repetition knee bending exercise. Precision errors attained 4-6% with excellent segmentation consistency point estimates (i.e., intra-rater ICC of 0.95) and acceptable limits of confidence. At deformation, T2* values were increased in both layers [+16.1 (10.7)%, p = 0.004 and +17.3 (15.3)%, p = 0.023, for the talus and tibia, respectively] whereas during recovery no significant changes could be established when comparing to baseline [talar cartilage: +5.2 (8.2)%, p = 0.26 and tibial cartilage: +6.6 (10.4)%, p = 0.23]. T2* mapping is a viable method to monitor deformational behavior in thin cartilage layers such as ankle cartilage. Longitudinal changes in T2* can be reliably appraised and require at least 4-6% differences to ascertain statistical significance. The ability to detect considerable change even after non-strenuous loading events, endorses T2* mapping as an innovative method to evaluate the effects of therapeutic exercise on thin cartilage layers. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 34:771-778, 2016.

A new validated T2* analysis method with certainty estimates for cardiac and liver iron load determination

Background Accurate quantification of iron load is of importance in tailoring the therapy for patients with iron load disease. Magnetic resonance (MR) imaging is commonly used to assess iron load in different organs by calculating the T2* value. Estimation of precision and uncertainty of the obtained T2* value may be useful for determining changes in iron load between follow-up scans and for titration of treatment. Therefore, the purpose of this study was to develop and validate a new T2* analysis method, providing certainty estimates for quantification of iron load in heart and liver. Methods The proposed method is ar egion of interest (ROI)based algorithm which changes curve-fitting procedure based on the results from an initial T2* estimate using a 3-parameter, noise-corrected model. The certainty estimate, presented as the T2* value confidence interval, is derived from multiple sub-regions within the selected ROI. Except for ROI delineation, the proposed method does not require any user interaction. Phantom imaging was performed on a 1.5T system, with two clinically available multi-echo gradient-recalled echo sequences for cardiac and liver imaging. Phantoms were constructed to cover the clinically important range of T2*. A T2* single-echo gradient-echo sequence with TR set to 6 × T1 and a 50° flip angle was used as reference standard. Computer simulations were performed to assess accuracy and precision from 2 000 repetitions at SNR=15. Inter- and intra-observer variability was obtained in patients (n=22) by one experienced and one inexperienced observer.

Acetabular Cartilage Assessment in Patients with Femoroacetabular Impingement by Using T2* Mapping with Arthroscopic Verification

To evaluate the ability of T2* mapping to help differentiate damaged from normal acetabular cartilage in patients with femoroacetabular impingement (FAI). The institutional review board approved this retrospective study, and the requirement to obtain informed consent was waived. The study complied with HIPAA guidelines. The authors reviewed T2* relaxation time maps of 28 hips from 26 consecutive patients (mean patient age, 28.2 years; range, 12-53 years; eight male patients (nine hips) with a mean age of 26.7 years [range, 16-53 years]; 18 female patients (19 hips) with a mean age of 28.9 years [range, 12-46 years]). Conventional diagnostic 3.0-T magnetic resonance (MR) arthrography was augmented by including a multiecho gradient-recalled echo sequence for T2* mapping. After imaging, acetabular and femoral data were separated and acetabular regions of interest were identified. Arthroscopic cartilage assessment with use of a modified Beck scale for acetabular cartilage damage was performed by an orthopedic surgeon who was blinded to the results of T2* mapping. A patient-specific acetabular projection with a T2* overlay was developed to anatomically correlate imaging data with those from surgery (the standard of reference). Results were analyzed by using receiver operating characteristic (ROC) curves. The patient-specific acetabular projection enabled co-localization between the MR imaging and arthroscopic findings. T2* relaxation times for normal cartilage (Beck score 1, 35.3 msec ± 7.0) were significantly higher than those for cartilage with early changes (Beck score 2, 20.7 msec ± 6.0) and cartilage with more advanced degeneration (Beck scores 3-6, ≤19.8 msec ± 5.6) (P < .001). At ROC curve analysis, a T2* value of 28 msec was identified as the threshold for damaged cartilage, with a 91% true-positive and 13% false-positive rate for differentiating Beck score 1 cartilage (normal) from all other cartilages. The patient-specific acetabular projection with a T2* mapping overlay enabled good anatomic localization of cartilage damage defined with a T2* threshold of 28 msec and less.