Free-breathing, fat-corrected T1 mapping of the liver with stack-of-stars MRI, and joint estimation of T1, PDFF, R 2 * , and B 1 + .

PURPOSE: Quantitative T1 mapping has the potential to replace biopsy for noninvasive diagnosis and quantitative staging of chronic liver disease. Conventional T1 mapping methods are confounded by fat and B 1 + $$ {B}_1^{+} $$ inhomogeneities, resulting in unreliable T1 estimations. Furthermore, these methods trade off spatial resolution and volumetric coverage for shorter acquisitions with only a few images obtained within a breath-hold. This work proposes a novel, volumetric (3D), free-breathing T1 mapping method to account for multiple confounding factors in a single acquisition. THEORY AND METHODS: Free-breathing, confounder-corrected T1 mapping was achieved through the combination of non-Cartesian imaging, magnetization preparation, chemical shift encoding, and a variable flip angle acquisition. A subspace-constrained, locally low-rank image reconstruction algorithm was employed for image reconstruction. The accuracy of the proposed method was evaluated through numerical simulations and phantom experiments with a T1/proton density fat fraction phantom at 3.0 T. Further, the feasibility of the proposed method was investigated through contrast-enhanced imaging in healthy volunteers, also at 3.0 T. RESULTS: The method showed excellent agreement with reference measurements in phantoms across a wide range of T1 values (200 to 1000 ms, slope = 0.998 (95% confidence interval (CI) [0.963 to 1.035]), intercept = 27.1 ms (95% CI [0.4 54.6]), r2 = 0.996), and a high level of repeatability. In vivo imaging studies demonstrated moderate agreement (slope = 1.099 (95% CI [1.067 to 1.132]), intercept = -96.3 ms (95% CI [-82.1 to -110.5]), r2 = 0.981) compared to saturation recovery-based T1 maps. CONCLUSION: The proposed method produces whole-liver, confounder-corrected T1 maps through simultaneous estimation of T1, proton density fat fraction, and B 1 + $$ {B}_1^{+} $$ in a single, free-breathing acquisition and has excellent agreement with reference measurements in phantoms.

Confounder-corrected T1 mapping in the liver through simultaneous estimation of T1 , PDFF, R 2 * , and B 1 + in a single breath-hold acquisition.

PURPOSE: Quantitative volumetric T1 mapping in the liver has the potential to aid in the detection, diagnosis, and quantification of liver fibrosis, inflammation, and spatially resolved liver function. However, accurate measurement of hepatic T1 is confounded by the presence of fat and inhomogeneous B 1 + $$ {B}_1^{+} $$ excitation. Furthermore, scan time constraints related to respiratory motion require tradeoffs of reduced volumetric coverage and/or increased acquisition time. This work presents a novel 3D acquisition and estimation method for confounder-corrected T1 measurement over the entire liver within a single breath-hold through simultaneous estimation of T1 , fat and B 1 + $$ {B}_1^{+} $$ . THEORY AND METHODS: The proposed method combines chemical shift encoded MRI and variable flip angle MRI with a B 1 + $$ {B}_1^{+} $$ mapping technique to enable confounder-corrected T1 mapping. The method was evaluated theoretically and demonstrated in both phantom and in vivo acquisitions at 1.5 and 3.0T. At 1.5T, the method was evaluated both pre- and post- contrast enhancement in healthy volunteers. RESULTS: The proposed method demonstrated excellent linear agreement with reference inversion-recovery spin-echo based T1 in phantom acquisitions at both 1.5 and 3.0T, with minimal bias (5.2 and 45 ms, respectively) over T1 ranging from 200-1200 ms. In vivo results were in general agreement with reference saturation-recovery based 2D T1 maps (SMART1 Map, GE Healthcare). CONCLUSION: The proposed 3D T1 mapping method accounts for fat and B 1 + $$ {B}_1^{+} $$ confounders through simultaneous estimation of T1 , B 1 + $$ {B}_1^{+} $$ , PDFF and R 2 * $$ {R}_2^{\ast } $$ . It demonstrates strong linear agreement with reference T1 measurements, with low bias and high precision, and can achieve full liver coverage in a single breath-hold.

Addressing concomitant gradient phase errors in time-interleaved chemical shift-encoded MRI fat fraction and R2 * mapping with a pass-specific phase fitting method.

PURPOSE: Concomitant gradients induce phase errors that increase quadratically with distance from isocenter. This work proposes a complex-based fitting method that addresses concomitant gradient phase errors in chemical shift encoded (CSE) MRI estimation of proton density fat fraction (PDFF) and R2 * through joint estimation of pass-specific phase terms. This method is applicable to time-interleaved multi-echo gradient-echo acquisitions (i.e., multi-pass acquisitions) and does not require prior knowledge of gradient waveforms typically needed to address concomitant gradient phase errors. THEORY AND METHODS: A CSE-MRI spoiled gradient echo signal model, with pass-specific phase terms, is introduced for non-linear least squares estimation of PDFF and R2 * in the presence of concomitant gradient phase errors. Cramér-Rao lower bound analysis was used to determine noise performance tradeoffs of the proposed fitting method, which was then validated in both phantom and in vivo experiments. RESULTS: The proposed fitting method removed PDFF and R2 * estimation errors up to 12% and 10 s-1 , respectively, at ±12 cm off isocenter (S/I) in a water phantom. In healthy volunteers, PDFF and R2 * bias was reduced by ~10% (12 cm off-isocenter) and ~30 s-1 (16 cm off-isocenter), respectively. An evaluation in 29 clinical liver datasets demonstrated reduced PDFF bias and variability (8.4% improvement in the coefficient of variation), even with the imaging volume centered at isocenter. CONCLUSION: Concomitant gradient induced phase errors in multi-pass CSE-MRI acquisitions can result in PDFF and R2 * estimation biases away from isocenter. The proposed fitting method enables accurate PDFF and R2 * quantification in the presence of concomitant gradient phase errors without knowledge of imaging gradient waveforms.

B0 and B1 inhomogeneities in the liver at 1.5 T and 3.0 T.

PURPOSE: The purpose of this work is to characterize the magnitude and variability of B0 and B1 inhomogeneities in the liver in large cohorts of patients at both 1.5 T and 3.0 T. METHODS: Volumetric B0 and B1 maps were acquired over the liver of patients presenting for routine abdominal MRI. Regions of interest were drawn in the nine Couinaud segments of the liver, and the average value was recorded. Magnitude and variation of measured averages in each segment were reported across all patients. RESULTS: A total of 316 B0 maps and 314 B1 maps, acquired at 1.5 T and 3.0 T on a variety of GE Healthcare MRI systems in 630 unique exams, were identified, analyzed, and, in the interest of reproducible research, de-identified and made public. Measured B0 inhomogeneities ranged (5th-95th percentiles) from -31.7 Hz to 164.0 Hz for 3.0 T (-14.5 Hz to 81.3 Hz at 1.5 T), while measured B1 inhomogeneities (ratio of actual over prescribed flip angle) ranged from 0.59 to 1.13 for 3.0 T (0.83 to 1.11 at 1.5 T). CONCLUSION: This study provides robust characterization of B0 and B1 inhomogeneities in the liver to guide the development of imaging applications and protocols. Field strength, bore diameter, and sex were determined to be statistically significant effects for both B0 and B1 uniformity. Typical clinical liver imaging at 3.0 T should expect B0 inhomogeneities ranging from approximately -100 Hz to 250 Hz (-50 Hz to 150 Hz at 1.5 T) and B1 inhomogeneities ranging from approximately 0.4 to 1.3 (0.7 to 1.2 at 1.5 T).

Limits of Fat Quantification in the Presence of Iron Overload.

BACKGROUND: Chemical shift encoded magnetic resonance imaging (CSE-MRI)-based tissue fat quantification is confounded by increased R2* signal decay rate caused by the presence of excess iron deposition. PURPOSE: To determine the upper limit of R2* above which it is no longer feasible to quantify proton density fat fraction (PDFF) reliably, using CSE-MRI. STUDY TYPE: Prospective. POPULATION: Cramér-Rao lower bound (CRLB) calculations, Monte Carlo simulations, phantom experiments, and a prospective study in 26 patients with known or suspected liver iron overload. FIELD STRENGTH/SEQUENCE: Multiecho gradient echo at 1.5 T and 3.0 T. ASSESSMENT: CRLB calculations were used to develop an empirical relationship between the maximum R2* value above which PDFF estimation will achieve a desired number of effective signal averages. A single voxel multi-TR, multi-TE stimulated echo acquisition mode magnetic resonance spectroscopy acquisition was used as a reference standard to estimate PDFF. Reconstructed PDFF and R2* maps were analyzed by one analyst using multiple regions of interest drawn in all nine Couinaud segments. STATISTICAL TESTS: None. RESULTS: Simulations, phantom experiments, and in vivo measurements demonstrated unreliable PDFF estimates with increased R2*, with PDFF errors as large as 20% at an R2* of 1000 s-1 . For typical optimized Cartesian acquisitions (TE1 = 0.75 msec, ΔTE = 0.67 msec at 1.5 T, TE1 = 0.65 msec, ΔTE = 0.58 msec at 3.0 T), an empirical relationship between PDFF estimation errors and acquisition parameters was developed that suggests PDFF estimates are unreliable above an R2* of ~538 s-1 and ~779 s-1 at 1.5 T and 3 T, respectively. This empirical relationship was further investigated with phantom experiments and in vivo measurements, with PDFF errors at an R2* of 1000 s-1 at 3.0 T as large as 10% with TE1 = 1.24 msec, ΔTE = 1.01 msec compared to 3% with TE1 = 0.65 msec, ΔTE = 0.58 msec. DATA CONCLUSION: We successfully developed a theoretically-based empirical formula that may provide an easily calculable guideline to identify R2* values above which PDFF is not reliable in research and clinical applications using CSE-MRI to quantify PDFF in the presence of iron overload. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

T1 -corrected quantitative chemical shift-encoded MRI.

PURPOSE: To develop and validate a T1 -corrected chemical-shift encoded MRI (CSE-MRI) method to improve noise performance and reduce bias for quantification of tissue proton density fat-fraction (PDFF). METHODS: A variable flip angle (VFA)-CSE-MRI method using joint-fit reconstruction was developed and implemented. In computer simulations and phantom experiments, sources of bias measured using VFA-CSE-MRI were investigated. The effect of tissue T1 on bias using low flip angle (LFA)-CSE-MRI was also evaluated. The noise performance of VFA-CSE-MRI was compared to LFA-CSE-MRI for liver fat quantification. Finally, a prospective pilot study in patients undergoing gadoxetic acid-enhanced MRI of the liver to evaluate the ability of the proposed method to quantify liver PDFF before and after contrast. RESULTS: VFA-CSE-MRI was accurate and insensitive to transmit B1 inhomogeneities in phantom experiments and computer simulations. With high flip angles, phase errors because of RF spoiling required modification of the CSE signal model. For relaxation parameters commonly observed in liver, the joint-fit reconstruction improved the noise performance marginally, compared to LFA-CSE-MRI, but eliminated T1 -related bias. A total of 25 patients were successfully recruited and analyzed for the pilot study. Strong correlation and good agreement between PDFF measured with VFA-CSE-MRI and LFA-CSE-MRI (pre-contrast) was observed before (R2 = 0.97; slope = 0.88, 0.81-0.94 95% confidence interval [CI]; intercept = 1.34, -0.77-1.92 95% CI) and after (R2 = 0.93; slope = 0.88, 0.78-0.98 95% CI; intercept = 1.90, 1.01-2.79 95% CI) contrast. CONCLUSION: Joint-fit VFA-CSE-MRI is feasible for T1 -corrected PDFF quantification in liver, is insensitive to B1 inhomogeneities, and can eliminate T1 bias, but with only marginal SNR advantage for T1 values observed in the liver.

A chemical shift encoding (CSE) approach for spectral selection in fluorine-19 MRI.

PURPOSE: To develop a chemical shift encoding (CSE) approach for fluorine-19 MRI of perfluorocarbons in the presence of multiple known fluorinated chemical species. THEORY AND METHODS: A multi-echo CSE technique is applied for spectral separation of the perfluorocarbon perfluoro-15-crown-5-ether (PFCE) and isoflurane (ISO) based on their chemical shifts at 4.7 T. Cramér-Rao lower bound analysis is used to identify echo combinations with optimal signal-to-noise performance. Signal contributions are fit with a multispectral fluorine signal model using a non-linear least squares estimation reconstruction directly from k-space data. This CSE approach is tested in fluorine-19 phantoms and in a mouse with a 2D and 3D spoiled gradient-echo acquisition using multiple echo times determined from Cramér-Rao lower bound analysis. RESULTS: Cramér-Rao lower bound analysis for PFCE and ISO separation shows signal-to-noise performance is maximized with a 0.33 ms echo separation. A linear behavior (R2 = 0.987) between PFCE signal and known relative PFCE volume is observed in CSE reconstructed images using a mixed PFCE/ISO phantom. Effective spatial and spectral separation of PFCE and ISO is shown in phantoms and in vivo. CONCLUSION: Feasibility of a gradient-echo CSE acquisition and image reconstruction approach with optimized noise performance is demonstrated through fluorine-19 MRI of PFCE with effective removal of ISO signal contributions. Magn Reson Med 79:2183-2189, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Noise properties of proton density fat fraction estimated using chemical shift-encoded MRI.

PURPOSE: The purpose of this work is to characterize the noise distribution of proton density fat fraction (PDFF) measured using chemical shift-encoded MRI, and to provide alternative strategies to reduce bias in PDFF estimation. THEORY: We derived the probability density function for PDFF estimated using chemical shift-encoded MRI, and found it to exhibit an asymmetric noise distribution that contributes to signal-to-noise-ratio dependent bias. METHODS: To study PDFF noise bias, we performed (at 1.5 T) numerical simulations, phantom acquisitions, and a retrospective in vivo experiment. In each experiment, we compared the performance of three statistics (mean, median, and maximum likelihood estimator) in estimating the PDFF in a region of interest. RESULTS: We demonstrated the presence of the asymmetric noise distribution in simulations, phantoms, and in vivo. In each experiment we demonstrated that both the median and proposed maximum likelihood estimator statistics outperformed the mean statistic in mitigating noise-related bias for low signal-to-noise-ratio acquisitions. CONCLUSIONS: Characterization of the noise distribution of PDFF estimated using chemical shift-encoded MRI enabled new strategies based on median and maximum likelihood estimator statistics to mitigate noise-related bias for accurate PDFF measurement from a region of interest. Such strategies are important for quantitative chemical shift-encoded MRI applications that typically operate in low signal-to-noise-ratio regimes. Magn Reson Med 80:685-695, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Circadian Rhythm Disruption Increases Tumor Growth Rate and Accumulation of Myeloid-Derived Suppressor Cells.

Circadian rhythm disruption is implicated in the initiation and progression of many diseases, including cancer. External stimuli, such as sunlight, serve to synchronize physiological processes and cellular functions to a 24-h cycle. The immune system is controlled by circadian rhythms, and perturbation of these rhythms can potentially alter the immune response to infections and tumors. The effect of circadian rhythm disruption on the immune response to tumors remains unclear. Specifically, the effects of circadian disruption (CD) on immunosuppressive cell types within the tumor, such as myeloid-derived suppressor cells (MDSCs), are unknown. In this study, a shifting lighting schedule is used to disrupt the circadian rhythm of mice. After acclimation to lighting schedules, mice are inoculated with 4T1 or B16-F10 tumors. Tumor growth is increased in mice housed under circadian disrupting lighting conditions compared to standard lighting conditions. Analysis of immune populations within the spleen and tumor shows an increased accumulation of MDSCs within these tissues, suggesting that MDSC mediated immunosuppression plays a role in the enhanced tumor growth caused by circadian disruption. This paves the way for future studies of the effects of CD on immunosuppression in cancer.

Effect of noise and estimator type on bias for analysis of liver proton density fat fraction.

PURPOSE: Proton-density fat-fraction (PDFF) is typically measured from PDFF maps by calculating the mean PDFF value within a region of interest (ROI). However, the mean estimator has been shown to result in bias when signal-to-noise ratio (SNR) is low, resulting from a skewed distribution of PDFF noise statistics. Thus, the purpose of this work was to determine the relative performance of three estimation methods (mean, median, maximum likelihood estimators (MLE)) for analysis of liver PDFF maps. METHODS: Observational study of adult patients (n = 56) undergoing abdominal MRI. Both 2D-sequential CSE-MRI ('low-SNR') and 3D CSE-MRI ('high-SNR') acquisitions were obtained. Single-voxel MRS formed the independent reference measurement of hepatic PDFF. Intra-class correlation was tested on a subset of 'low-SNR' acquisitions. ROIs were semi-automatically co-registered across all acquisitions. Bland-Altman analysis and intra-class correlation coefficients were used for statistical analysis. A p-value of <0.05 was considered significant. RESULTS: For in vivo low-SNR acquisitions, the mean estimator had a larger error than either the median or MLE values (bias ~ -1% absolute PDFF). The intra-class correlation coefficient was significantly greater for median and maximum likelihood estimators (0.992 and 0.993, respectively) compared to the mean estimator (0.973). CONCLUSION: Alternative ROI analysis strategies, such as MLE or median estimators, are useful to avoid SNR-related PDFF bias. Median may be the most clinically practical strategy given its ease of calculation.