Ultrashort echo time imaging for quantification of hepatic iron overload: Comparison of acquisition and fitting methods via simulations, phantoms, and in vivo data.
ABSTRACT: BACKGROUND:Current R2*-MRI techniques for measuring hepatic iron content (HIC) use various acquisition types and fitting models. PURPOSE:To evaluate the accuracy and precision of R2*-HIC acquisition and fitting methods. STUDY TYPE:Signal simulations, phantom study, and prospective in vivo cohort. POPULATION:In all, 132 patients (58/74 male/female, mean age 17.7 years). FIELD STRENGTH/SEQUENCE:2D-multiecho gradient-echo (GRE) and ultrashort echo time (UTE) acquisitions at 1.5T. ASSESSMENT:Synthetic MR signals were created to mimic published GRE and UTE methods, using different R2* values (25-2000 s-1 ) and signal-to-noise ratios (SNR). Phantoms with varying iron concentrations were scanned at 1.5T. In vivo data were analyzed from 132 patients acquired at 1.5T. R2* was estimated by fitting using three signal models. Accuracy and precision of R2* measurements for UTE acquisition parameters (SNR, echo spacing [?TE], maximum echo time [TEmax ]) and fitting methods were compared for simulated, phantom, and in vivo datasets. STATISTICAL TESTS:R2* accuracy was determined from the relative error and by linear regression analysis. Precision was evaluated using coefficient of variation (CoV) analysis. RESULTS:In simulations, all models had high R2* accuracy (error <5%) and precision (CoV <10%) for all SNRs, shorter ?TE (?0.5 msec), and longer TEmax (?10.1 msec); except the constant offset model overestimated R2* at the lowest SNR. In phantoms and in vivo, all models produced similar R2* values for different SNRs and shorter ?TEs (slopes: 0.99-1.06, R2 > 0.99, P < 0.001). In all experiments, R2* results degraded for high R2* values with longer ?TE (?1 msec). In vivo, shorter and longer TEmax gave similar R2* results (slopes: 1.02-1.06, R2 > 0.99, P < 0.001) for the noise subtraction model for 25?R2*?2000 s-1 . However, both quadratic and constant offset models, using shorter TEmax (?4.7 msec) overestimated R2* and yielded high CoVs up to ?170% for low R2* (<250 s-1 ). DATA CONCLUSION:UTE with TEmax ? 10.1 msec and ?TE ? 0.5 msec yields accurate R2* estimates over the entire clinical HIC range. Monoexponential fitting with noise subtraction is the most robust signal model to changes in UTE parameters and achieves the highest R2* accuracy and precision. LEVEL OF EVIDENCE:2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1475-1488.
Project description:PURPOSE:Recent studies have suggested the presence of short-T<sub>2</sub> * signals in the liver, which may confound chemical shift-encoded (CSE) fat quantification when using short echo times (TEs). The purpose of this study was to characterize the liver signal at short echo times and to determine its impact on liver fat quantification. METHODS:An ultrashort echo time (UTE) chemical shift-encoded MRI (CSE-MRI) technique and a multicomponent reconstruction were developed to characterize short-T<sub>2</sub> * liver signals. Subsequently, liver fat fraction was quantified using a short-TE (first TE = 0.7 ms) and UTE CSE-MRI acquisitions and compared with a standard CSE-MRI (first TE = 1.2 ms). RESULTS:Short-T<sub>2</sub> * signals were consistently observed in the liver of all healthy volunteers imaged at both 1.5T and 3.0T. At 3.0T, short-T<sub>2</sub> * signal fractions of 9.6 ± 1.5%, 7.0 ± 1.7%, and 7.4 ± 1.7% with T<sub>2</sub> * of 0.23 ± 0.05 ms, 0.20 ± 0.05 ms, and 0.10 ± 0.02 ms were measured in healthy volunteers, patients with liver cirrhotic disease, and patients with hepatic steatosis (but no cirrhosis), respectively. For proton density fat fraction (PDFF) estimation, 1.7% (P < .01) and 3.4% (P < .01) biases were observed in subjects imaged using short-TE CSE-MRI and using UTE CSE-MRI at 1.5T, respectively. The biases were reduced to 0.4% and -0.7%, respectively, by excluding short echoes less than 1 ms. A 3.2% bias (P < .01) was observed in subjects imaged using UTE CSE-MRI at 3.0T, which was reduced to 0.1% by excluding short echoes <1 ms. CONCLUSIONS:A liver short-T<sub>2</sub> * signal component was consistently observed and was shown to confound liver fat quantification when short echo times were used with CSE-MRI.
Project description:To image cartilage-bone interfaces in naturally occurring and experimentally prepared human cartilage-bone specimens at 3 T by using ultrashort echo time (TE) (UTE) and conventional pulse sequences to (a) determine the appearance of the signal intensity patterns and (b) identify the structures contributing to signal intensity on the UTE MR images.This study was exempted by the institutional review board, and informed consent was not required. Five cadaveric (mean age, 86 years +/- 4) patellae were imaged by using proton density-weighted fat-suppressed (repetition time msec/TE msec, 2300/34), T1-weighted (700/10), and UTE (300/0.008, 6.6, with or without dual-inversion preparations at inversion time 1 = 135 msec and inversion time 2 = 95 msec) sequences. The UTE images were compared with proton density-weighted fat-suppressed and T1-weighted images and were evaluated by two radiologists. To identify the sources of signal on the UTE images, samples including specific combinations of tissues (uncalcified cartilage [UCC] only, calcified cartilage [CC] and subchondral bone [bone] [CC/bone], bone only; and UCC, CC, and bone [UCC/CC/bone]) were prepared and imaged by using the UTE sequence.On the UTE MR images, all patellar sections exhibited a high-intensity linear signal near the osteochondral junction, which was not visible on protein density-weighted fat-suppressed or T1-weighted images. In some sections, focal regions of thickened or diminished signal intensity were also found. In the prepared samples, UCC only, CC/bone, and UCC/CC/bone samples exhibited high signal intensity on the UTE images, whereas bone-only samples did not.These results show that the high signal intensity on UTE images of human articular joints originates from the CC and the deepest layer of the UCC, without a definite contribution from subchondral bone. UTE sequences may provide a way of evaluating abnormalities at or near the osteochondral junction. (c) RSNA, 2010.
Project description:Larmor-frequency shift or image phase measured by gradient-echo sequences has provided a new source of MRI contrast. This contrast is being used to study both the structure and function of the brain. So far, phase images of the brain have been largely obtained at long echo times as maximum phase signal-to-noise ratio (SNR) is achieved at TE = T2* (?40 ms at 3T). The structures of the brain, however, are compartmentalized and complex with a wide range of signal relaxation times. At such long TE, the short-T2 components are largely attenuated and contribute minimally to phase contrast. The purpose of this study was to determine whether proton gradient-echo images of the brain exhibit phase contrast at ultra-short TE (UTE). Our data showed that UTE images acquired at 7 T without off-resonance saturation do not contain significant phase contrast between gray and white matter. However, UTE images of the brain can attain strong phase contrast even at a nominal TE of 106 ?s by using off-resonance RF saturation pulses, which provide direct saturation of ultra-short-T2 components and indirect saturation of longer-T2 components via magnetization transfer. In addition, phase contrast between gray and white matter acquired at UTE with off-resonance saturation is reversed compared to that of the long-T2 signals acquired at long TEs. This finding opens up a potential new way to manipulate image phase contrast of the brain. By accessing short and ultra-short-T2 species, MRI phase images may further improve the characterization of tissue microstructure in the brain.
Project description:BACKGROUND:Quantitative MRI of patellar tendinopathy (PT) can be challenging due to spatial variation of T2 * relaxation times. PURPOSE:1) To compare T2 * quantification using a standard approach with analysis in specific tissue compartments of the patellar tendon. 2) To evaluate test-retest reliability of different methods for fitting ultrashort echo time (UTE)-relaxometry data. STUDY TYPE:Prospective. SUBJECTS:Sixty-five athletes with PT. FIELD STRENGTH/SEQUENCE:3D UTE scans covering the patellar tendon were acquired using a 3.0T scanner and a 16-channel surface coil. ASSESSMENT:Voxelwise median T2 * was quantified with monoexponential, fractional-order, and biexponential fitting. We applied two methods for T2 * analysis: first, a standard approach by analyzing all voxels covering the proximal patellar tendon. Second, within subregions of the patellar tendon, by using thresholds on biexponential fitting parameter percentage short T2 * (0-30% for mostly long T2 *, 30-60% for mixed T2 *, and 60-100% for mostly short T2 *). STATISTICAL TESTS:Average test-retest reliability was assessed in three athletes using coefficients-of-variation (CV) and coefficients-of-repeatability (CR). RESULTS:With standard image analysis, we found a median [interquartile range, IQR] monoexponential T2 * of 6.43 msec [4.32-8.55] and fractional order T2 * 4.39 msec [3.06-5.78]. The percentage of short T2 * components was 52.9% [35.5-69.6]. Subregional monoexponential T2 * was 13.78 msec [12.11-16.46], 7.65 msec [6.49-8.61], and 3.05 msec [2.52-3.60] and fractional order T2 * 11.82 msec [10.09-14.44], 5.14 msec [4.25-5.96], and 2.19 msec [1.82-2.64] for 0-30%, 30-60%, and 60-100% short T2 *, respectively. Biexponential component short T2 * was 1.693 msec [1.417-2.003] for tissue with mostly short T2 * and long T2 * of 15.79 msec [13.47-18.61] for mostly long T2 *. The average CR (CV) was 2 msec (15%), 2 msec (19%) and 10% (22%) for monoexponential, fractional order and percentage short T2 *, respectively. DATA CONCLUSION:Patellar tendinopathy is characterized by regional variability in binding states of water. Quantitative multicompartment T2 * analysis in PT can be facilitated using a voxel selection method based on using biexponential fitting parameters. LEVEL OF EVIDENCE:1 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:420-430.
Project description:BACKGROUND:Ultrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development of rapid biomarker extraction and regional quantification has lagged behind due to labor-intensive lung segmentation. PURPOSE:To evaluate a deep learning (DL) approach for automated lung segmentation to extract image-based biomarkers from functional lung imaging using 3D radial UTE oxygen-enhanced (OE) MRI. STUDY TYPE:Retrospective study aimed to evaluate a technical development. POPULATION:Forty-five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis. FIELD STRENGTH/SEQUENCE:1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence. ASSESSMENT:Two 3D radial UTE volumes were acquired sequentially under normoxic (21% O2 ) and hyperoxic (100% O2 ) conditions. Automated segmentation of the lungs using 2D convolutional encoder-decoder based DL method, and the subsequent functional quantification via adaptive K-means were compared with the results obtained from the reference method, supervised region growing. STATISTICAL TESTS:Relative to the reference method, the performance of DL on volumetric quantification was assessed using Dice coefficient with 95% confidence interval (CI) for accuracy, two-sided Wilcoxon signed-rank test for computation time, and Bland-Altman analysis on the functional measure derived from the OE images. RESULTS:The DL method produced strong agreement with supervised region growing for the right (Dice: 0.97; 95% CI = [0.96, 0.97]; P < 0.001) and left lungs (Dice: 0.96; 95% CI = [0.96, 0.97]; P < 0.001). The DL method averaged 46 seconds to generate the automatic segmentations in contrast to 1.93 hours using the reference method (P < 0.001). Bland-Altman analysis showed nonsignificant intermethod differences of volumetric (P ? 0.12) and functional measurements (P ? 0.34) in the left and right lungs. DATA CONCLUSION:DL provides rapid, automated, and robust lung segmentation for quantification of regional lung function using UTE proton MRI. LEVEL OF EVIDENCE:2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1169-1181.
Project description:Cardiac T1 mapping allows non-invasive imaging of interstitial diffuse fibrosis. Myocardial T1 is commonly calculated by voxel-wise fitting of the images acquired using balanced steady-state free precession (SSFP) after an inversion pulse. However, SSFP imaging is sensitive to B1 and B0 imperfection, which may result in additional artifacts. A gradient echo (GRE) imaging sequence has been used for myocardial T1 mapping; however, its use has been limited to higher magnetic field to compensate for the lower signal-to-noise ratio (SNR) of GRE versus SSFP imaging. A slice-interleaved T1 mapping (STONE) sequence with SSFP readout (STONE-SSFP) has been recently proposed for native myocardial T1 mapping, which allows longer recovery of magnetization (>8 R-R) after each inversion pulse. In this study, we hypothesize that a longer recovery allows higher SNR and enables native myocardial T1 mapping using STONE with GRE imaging readout (STONE-GRE) at 1.5T. Numerical simulations and phantom and in vivo imaging were performed to compare the performance of STONE-GRE and STONE-SSFP for native myocardial T1 mapping at 1.5T. In numerical simulations, STONE-SSFP shows sensitivity to both T2 and off resonance. Despite the insensitivity of GRE imaging to T2 , STONE-GRE remains sensitive to T2 due to the dependence of the inversion pulse performance on T2 . In the phantom study, STONE-GRE had inferior accuracy and precision and similar repeatability as compared with STONE-SSFP. In in vivo studies, STONE-GRE and STONE-SSFP had similar myocardial native T1 times, precisions, repeatabilities and subjective T1 map qualities. Despite the lower SNR of the GRE imaging readout compared with SSFP, STONE-GRE provides similar native myocardial T1 measurements, precision, repeatability, and subjective image quality when compared with STONE-SSFP at 1.5T.