Proton Density Fat Fraction Maps Research Articles

Enhanced accuracy and stability in automated intra-pancreatic fat deposition monitoring of type 2 diabetes mellitus using Dixon MRI and deep learning.

Intra-pancreatic fat deposition (IPFD) is closely associated with the onset and progression of type 2 diabetes mellitus (T2DM). We aimed to develop an accurate and automated method for assessing IPFD on multi-echo Dixon MRI. In this retrospective study, 534 patients from two centers who underwent upper abdomen MRI and completed multi-echo and double-echo Dixon MRI were included. A pancreatic segmentation model was trained on double-echo Dixon water images using nnU-Net. Predicted masks were registered to the proton density fat fraction (PDFF) maps of the multi-echo Dixon sequence. Deep semantic segmentation feature-based radiomics (DSFR) and radiomics features were separately extracted on the PDFF maps and modeled using the support vector machine method with 5-fold cross-validation. The first deep learning radiomics (DLR) model was constructed to distinguish T2DM from non-diabetes and pre-diabetes by averaging the output scores of the DSFR and radiomics models. The second DLR model was then developed to distinguish pre-diabetes from non-diabetes. Two radiologist models were constructed based on the mean PDFF of three pancreatic regions of interest. The mean Dice similarity coefficient for pancreas segmentation was 0.958 in the total test cohort. The AUCs of the DLR and two radiologist models in distinguishing T2DM from non-diabetes and pre-diabetes were 0.868, 0.760, and 0.782 in the training cohort, and 0.741, 0.724, and 0.653 in the external test cohort, respectively. For distinguishing pre-diabetes from non-diabetes, the AUCs were 0.881, 0.688, and 0.688 in the training cohort, which included data combined from both centers. Testing was not conducted due to limited pre-diabetic patients. Intraclass correlation coefficients between radiologists' pancreatic PDFF measurements were 0.800 and 0.699 at two centers, suggesting good and moderate reproducibility, respectively. The DLR model demonstrated superior performance over radiologists, providing a more efficient, accurate and stable method for monitoring IPFD and predicting the risk of T2DM and pre-diabetes. This enables IPFD assessment to potentially serve as an early biomarker for T2DM, providing richer clinical information for disease progression and management.

Dixon-based B0 self-navigation in radial stack-of-stars multi-echo gradient echo imaging.

To develop a Dixon-based self-navigation approach to estimate and correct temporal variations in radial stack-of-stars gradient echo imaging for quantitative body MRI. The proposed method estimates temporal variations using a self-navigator estimated by a graph-cut-based water-fat separation algorithm on the oversampled k-space center. The self-navigator was employed to correct for phase differences between radial spokes (one-dimensional [1D] correction) and to perform a motion-resolved reconstruction to correct spatiotemporal pseudo-periodic variations (three-dimensional [3D] correction). Numerical simulations, phantom experiments and in vivo neck scans were performed to evaluate the effects of temporal variations on the field-map, proton density fat fraction (PDFF) and map, and to validate the proposed method. Temporal variations were found to cause signal loss and phase shifts on the multi-echo images that lead to an underestimation of , while PDFF mapping was less affected. The self-navigator captured slowly varying temporal drifts and temporal variations caused by respiratory motion. While the 1D correction effectively corrected drifts in phantom studies, it was insufficient in vivo due to 3D spatially varying temporal variations with amplitudes of up to 25 Hz at 3 T near the lungs. The proposed 3D correction locally improved the correction of field-map and and reduced image artifacts. Temporal variations particularly affect mapping in radial stack-of-stars imaging. The self-navigation approach can be applied without modifying the MR acquisition to correct for drift and physiological motion-induced variations, especially in the presence of fat.

7 Tesla MRI Liver Fat Quantification in Mice: Data Quality Assessment.

The objective of this study was to evaluate the robustness of proton density fat fraction (PDFF) data determined by magnetic resonance imaging (MRI) and spectroscopy (MRS) via spatially resolved error estimation. Using standard T2* relaxation time measurement protocols, in-vivo and ex-vivo MRI data with water and fat nominally in phase or out of phase relative to each other were acquired on a 7 T small animal scanner. Based on a total of 24 different echo times, PDFF maps were calculated in a magnitude-based approach. After identification of the decisive error-prone variables, pixel-wise error estimation was performed by simple propagation of uncertainty. The method was then used to evaluate PDFF data acquired for an explanted mouse liver and an in vivo mouse liver measurement. The determined error maps helped excluding measurement errors as cause of unexpected local PDFF variations in the explanted liver. For in vivo measurements, severe error maps gave rise to doubts in the acquired PDFF maps and triggered an in-depth analysis of possible causes, yielding abdominal movement or bladder filling as in vivo occurring reasons for the increased errors. The combination of pixel-wise acquisition of PDFF data and the corresponding error maps allows for a more specific, spatially resolved evaluation of the PDFF value reliability.

Associations of gestational diabetes and proton density fat fraction of vertebral bone marrow and paraspinal musculature in premenopausal women.

Fat content in bones and muscles, quantified by magnetic resonance imaging (MRI) as a proton density fat fraction (PDFF) value, is an emerging non-invasive biomarker. PDFF has been proposed to indicate bone and metabolic health among postmenopausal women. Premenopausal women with a history of gestational diabetes (GDM) carry an increased risk of developing type 2 diabetes and an increased risk of fractures. However, no studies have investigated the associations between a history of GDM and PDFF of bone or of paraspinal musculature (PSM), composed of autochthonous muscle (AM) and psoas muscle, which are responsible for moving and stabilizing the spine. This study aims to investigate whether PDFF of vertebral bone marrow and of PSM are associated with a history of GDM in premenopausal women. A total of 37 women (mean age 36.3 ± 3.8 years) who were 6 to 15 months postpartum with (n=19) and without (n=18) a history of GDM underwent whole-body 3T MRI, including a chemical shift encoding-based water-fat separation. The PDFF maps were calculated for the vertebral bodies and PSM. The cross-sectional area (CSA) of PSM was obtained. Associations between a history of GDM and PDFF were assessed using multivariable linear and logistic regression models. The PDFF of the vertebral bodies was significantly higher in women with a history of GDM (GDM group) than in women without (thoracic: median 41.55 (interquartile range 32.21-49.48)% vs. 31.75 (30.03-34.97)%; p=0.02, lumbar: 47.84 (39.19-57.58)% vs. 36.93 (33.36-41.31)%; p=0.02). The results remained significant after adjustment for age and body mass index (BMI) (p=0.01-0.02). The receiver operating characteristic curves showed optimal thoracic andlumbar vertebral PDFF cutoffs at 38.10% and 44.18%, respectively, todifferentiate GDM (AUC 0.72 and 0.73, respectively, sensitivity 0.58, specificity 0.89). The PDFF of the AM was significantly higher in the GDM group (12.99 (12.18-15.90)% vs. 10.83 (9.39-14.71)%; p=0.04) without adjustments, while the CSA was similar between the groups (p=0.34). A history of GDM is significantly associated with a higher PDFF of the vertebral bone marrow, independent of age and BMI. This statistical association between GDM and increased PDFF highlights vertebral bone marrow PDFF as a potential biomarker for the assessment of bone health in premenopausal women at risk of diabetes.

Trajectory correction enables free-running chemical shift encoded imaging for accurate cardiac proton-density fat fraction quantification at 3T

BackgroundMetabolic diseases can negatively alter epicardial fat accumulation and composition, which can be probed using quantitative cardiac chemical shift encoded(CSE) MRI by mapping proton-density fat fraction (PDFF). To obtain motion-resolved high-resolution PDFF maps, we proposed a free-running cardiac CSE-MRI framework at 3T. To employ faster bipolar readout gradients, a correction for gradients imperfections was added using the gradient impulse response function (GIRF) and evaluated on intermediate images and PDFF quantification. MethodsTen minutes free-running cardiac 3D radial CSE-MRI acquisitions were compared in vitro and in vivo at 3T. Monopolar and bipolar readout gradients schemes provided 8 echoes (TE1/ΔTE = 1.16/1.96ms) and 13 echoes (TE1/ΔTE = 1.12/1.07ms), respectively. Bipolar-gradients free-running cardiac fat and water images and PDFF maps were reconstructed with or without GIRF-correction. PDFF values were evaluated in silico, in vitro on a fat/water phantom, and in vivo in 10 healthy volunteers and three diabetic patients. ResultsIn monopolar mode, fat-water swaps were demonstrated in silico and confirmed in vitro. Using bipolar readout gradients, PDFF quantification was reliable and accurate with GIRF correction with a mean bias of 0.03% in silico and 0.36% in vitro while it suffered from artifacts without correction, leading to a PDFF bias of 4.9% in vitro and swaps in vivo. Using bipolar readout gradients, in vivo PDFF of epicardial adipose tissue was significantly lower than in subcutaneous fat (80.4±7.1% vs 92.5±4.3%, P<0.0001). ConclusionAiming for an accurate PDFF quantification, high-resolution free-running cardiac CSE-MRI imaging proved to benefit from bipolar echoes with k-space trajectory correction at 3T. This free-breathing acquisition framework enables to investigate epicardial adipose tissue PDFF in metabolic diseases.

Correcting gradient chain induced fat quantification errors in radial multi-echo Dixon imaging using a gradient modulation transfer function

Purpose: Multi-echo Stack-of-stars (SoS) radial k-space trajectories with golden angle ordering are becoming popular for free-breathing abdominal Dixon imaging and proton density fat fraction (PDFF) mapping. Gradient chain imperfections including eddy currents and system delays are known to affect the image quality of radial imaging and to confound the estimation of PDFF mapping. This work proposes a retrospective trajectory correction method based on a simple gradient modulation transfer function (GMTF) measurement to predict and correct gradient chain induced k-space trajectory errors.Methods: The GMTF was measured using the standard hardware of a 3 Tesla scanner on a phantom using the thin slice method and was applied to a 3D radial SoS Dixon imaging sequence. The impact of the GMTF-based correction on image reconstruction and PDFF quantification was investigated using numerical simulations and validated on experimental phantom data as well as on in vivo leg and liver data of healthy volunteers.Results: Correcting the k-space trajectories with the measured GMTF during image reconstruction reduced PDFF quantification errors for phantom and in vivo acquisitions. A Bland-Altman comparison of the measured PDFF phantom and reference data confirmed that the GMTF correction narrowed down the limits of agreement (LoA) from 1.3% ± 8.1% (uncorrected) to 1.9% ± 5.4% (GMTF-corrected) over the full PDFF range (0%–100%) and from −0.26% ± 2.8% (uncorrected) to 0.12% ± 1.5% (GMTF-corrected) within the 0%–50% PDFF range. Liver PDFF estimation was improved by reducing the standard deviation of the mean liver PDFF and the bias of the mean liver PDFF for all subjects.Conclusion: The proposed GMTF-based k-space trajectory correction is a fast alternative method for avoiding PDFF quantitation errors caused by gradient-system induced k-space trajectory errors in 3D radial multi-echo gradient-echo acquisitions.

Free-breathing high isotropic resolution quantitative susceptibility mapping (QSM) of liver using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction.

To enable free-breathing and high isotropic resolution liver quantitative susceptibility mapping (QSM) using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction. Using 3D multi-echo UTE cones MRI, a respiratory motion was estimated from the k-space center of the imaging data. After sorting the k-space data with estimated motion, respiratory motion state-resolved reconstruction was performed for multi-echo data followed by nonlinear least-squares fitting for proton density fat fraction (PDFF), , and fat-corrected B0 field maps. PDFF and B0 field maps were subsequently used for QSM reconstruction. The proposed method was compared with motion-averaged (gridding) reconstruction and conventional 3D multi-echo Cartesian MRI in moving gadolinium phantom and in vivo studies. Region of interest (ROI)-based linear regression analysis was performed on these methods to investigate correlations between gadolinium concentration and QSM in the phantom study and between and QSM in in vivo study. Cones with motion-resolved reconstruction showed sharper image quality compared to motion-averaged reconstruction with a substantial reduction of motion artifacts in both moving phantom and in vivo studies. For ROI-based linear regression analysis of the phantom study, susceptibility values from cones with motion-resolved reconstruction ( = 0.31 0.05, = 0.999) and Cartesian without motion ( = 0.32 0.04, = 1.000) showed linear relationships with gadolinium concentrations and showed good agreement with each other. For in vivo, motion-resolved reconstruction showed higher goodness of fit ( = 0.00261 0.524, = 0.977) compared to motion-averaged reconstruction ( = 0.0021 0.572, = 0.723) in ROI-based linear regression analysis between and QSM. Feasibility of free-breathing liver QSM was demonstrated with motion-resolved 3D multi-echo UTE cones MRI, achieving high isotropic resolution currently unachievable in conventional Cartesian MRI.

MRI proton density fat fraction for estimation of tumor grade in steatotic hepatocellular carcinoma

ObjectivesImage-based detection of intralesional fat in focal liver lesions has been established in diagnostic guidelines as a feature indicative of hepatocellular carcinoma (HCC) and associated with a favorable prognosis. Given recent advances in MRI-based fat quantification techniques, we investigated a possible relationship between intralesional fat content and histologic tumor grade in steatotic HCCs.MethodsPatients with histopathologically confirmed HCC and prior MRI with proton density fat fraction (PDFF) mapping were retrospectively identified. Intralesional fat of HCCs was assessed using an ROI-based analysis and the median fat fraction of steatotic HCCs was compared between tumor grades G1-3 with non-parametric testing. ROC analysis was performed in case of statistically significant differences (p < 0.05). Subgroup analyses were conducted for patients with/without liver steatosis and with/without liver cirrhosis.ResultsA total of 57 patients with steatotic HCCs (62 lesions) were eligible for analysis. The median fat fraction was significantly higher for G1 lesions (median [interquartile range], 7.9% [6.0─10.7%]) than for G2 (4.4% [3.2─6.6%]; p = .001) and G3 lesions (4.7% [2.8─7.8%]; p = .036). PDFF was a good discriminator between G1 and G2/3 lesions (AUC .81; cut-off 5.8%, sensitivity 83%, specificity 68%) with comparable results in patients with liver cirrhosis. In patients with liver steatosis, intralesional fat content was higher than in the overall sample, with PDFF performing better in distinguishing between G1 and G2/3 lesions (AUC .92; cut-off 8.8%, sensitivity 83%, specificity 91%).ConclusionsQuantification of intralesional fat using MRI PDFF mapping allows distinction between well- and less-differentiated steatotic HCCs.Clinical relevancePDFF mapping may help optimize precision medicine as a tool for tumor grade assessment in steatotic HCCs. Further investigation of intratumoral fat content as a potential prognostic indicator of treatment response is encouraged.Key Points• MRI proton density fat fraction mapping enables distinction between well- (G1) and less- (G2 and G3) differentiated steatotic hepatocellular carcinomas.• In a retrospective single-center study with 62 histologically proven steatotic hepatocellular carcinomas, G1 tumors showed a higher intralesional fat content than G2 and G3 tumors (7.9% vs. 4.4% and 4.7%; p = .004).• In liver steatosis, MRI proton density fat fraction mapping was an even better discriminator between G1 and G2/G3 steatotic hepatocellular carcinomas.

Changes in pancreatic exocrine function, fat and fibrosis in diabetes mellitus: analysis using MR imaging.

To evaluate the relationships between hemoglobin A1c (HbA1c) levels with exocrine pancreatic function using cine-dynamic magnetic resonance cholangiopancreatography (MRCP) and the pancreatic parenchyma using fat-suppressed T1 mapping and the proton density fat fraction (PDFF). Patients who underwent 3T-MRI and HbA1c measurement were retrospectively recruited. MRI included cine-dynamic MRCP with a spatially selective inversion-recovery (SS-IR) pulse, fat-suppressed Look-Locker T1 mapping and multiecho 3D Dixon-based PDFF mapping. The pancreatic exocrine secretion grade on cine-dynamic MRCP, T1 values, and PDFF were analyzed in non-diabetic (n = 32), pre-diabetic (n = 44) and diabetic (n = 23) groups defined using HbA1c. PDFF was weakly correlation with HbA1c (ρ = 0.30, p = 0.002). No correlations were detected between HbA1c and secretion grade (ρ = - 0.16, p = 0.118) or pancreatic parenchymal T1 (ρ = 0.13, p = 0.19). The secretion grade was comparable between the three groups. The T1 value was higher in diabetic (T1 = 1006.2+/- 224.8 ms) than in non-diabetic (T1 = 896.2+/- 86.3 ms, p = 0.010) and pre-diabetic (T1 = 870.1+/- 91.7 ms, p < 0.010) patients. The PDFF was higher in diabetic (FF = 11.8+/- 8.7 %) than in non-diabetic (FF = 6.8+/- 4.2 %, p = 0.014) patients. Pancreatic exocrine function, T1, and FF showed no correlation with HbA1c. Pancreatic T1 and fat fraction is increased in patients with Type 2 diabetes mellitus. This study demonstrates unaffected exocrine function in pre-diabetes and diabetes and confirms that pancreatic parenchymal T1 and FF are increased in patients with diabetes.

Quantitative MRI in cardiometabolic disease: From conventional cardiac and liver tissue mapping techniques to multi-parametric approaches.

Cardiometabolic disease refers to the spectrum of chronic conditions that include diabetes, hypertension, atheromatosis, non-alcoholic fatty liver disease, and their long-term impact on cardiovascular health. Histological studies have confirmed several modifications at the tissue level in cardiometabolic disease. Recently, quantitative MR methods have enabled non-invasive myocardial and liver tissue characterization. MR relaxation mapping techniques such as T1, T1ρ, T2 and T2* provide a pixel-by-pixel representation of the corresponding tissue specific relaxation times, which have been shown to correlate with fibrosis, altered tissue perfusion, oedema and iron levels. Proton density fat fraction mapping approaches allow measurement of lipid tissue in the organ of interest. Several studies have demonstrated their utility as early diagnostic biomarkers and their potential to bear prognostic implications. Conventionally, the quantification of these parameters by MRI relies on the acquisition of sequential scans, encoding and mapping only one parameter per scan. However, this methodology is time inefficient and suffers from the confounding effects of the relaxation parameters in each single map, limiting wider clinical and research applications. To address these limitations, several novel approaches have been proposed that encode multiple tissue parameters simultaneously, providing co-registered multiparametric information of the tissues of interest. This review aims to describe the multi-faceted myocardial and hepatic tissue alterations in cardiometabolic disease and to motivate the application of relaxometry and proton-density cardiac and liver tissue mapping techniques. Current approaches in myocardial and liver tissue characterization as well as latest technical developments in multiparametric quantitative MRI are included. Limitations and challenges of these novel approaches, and recommendations to facilitate clinical validation are also discussed.

Estimation of field inhomogeneity map following magnitude-based ambiguity-resolved water-fat separation

Magnitude-based PDFF (Proton Density Fat Fraction) and R2∗ mapping with resolved water-fat ambiguity is extended to calculate field inhomogeneity (field map) using the phase images. The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and R2∗ from magnitude fitting may be updated using the estimated field maps. The limits of quantification of our voxel-independent implementation were assessed. Bland-Altman was used to compare PDFF and field maps from our method against a reference complex-based method on 152 UK Biobank subjects (1.5 T Siemens). A separate acquisition (3 T Siemens) presenting field inhomogeneities was also used. The proposed field mapping was accurate beyond double the complex-based limit range. High agreement was obtained between the proposed method and the reference in UK. Robust field mapping was observed at 3 T, for inhomogeneities over 400 Hz including rapid variation across edges. Field mapping following unambiguous magnitude-based water-fat separation was demonstrated in-vivo and showed potential at 3 T.

MR Fingerprinting for Liver Tissue Characterization: A Histopathologic Correlation Study.

Background Liver MR fingerprinting (MRF) enables simultaneous quantification of T1, T2, T2*, and proton density fat fraction (PDFF) maps in single breath-hold acquisitions. Histopathologic correlation studies are desired for its clinical use. Purpose To compare liver MRF-derived metrics with separate reference quantitative MRI in participants with diffuse liver disease, evaluate scan-rescan repeatability of liver MRF, and validate MRF-derived measurements for histologic grading of liver biopsies. Materials and Methods This prospective study included participants with diffuse liver disease undergoing MRI from July 2021 to January 2022. Participants underwent two-dimensional single-section liver MRF and separate reference quantitative MRI. Linear regression, Bland-Altman plots, and coefficients of variation were used to assess the bias and repeatability of liver MRF measurements. For participants undergoing liver biopsy, the association between mapping and histologic grading was evaluated by using the Spearman correlation coefficient. Results Fifty-six participants (mean age, 59 years ± 15 [SD]; 32 women) were included to compare mapping techniques and 23 participants were evaluated with liver biopsy (mean age, 52.7 years ± 12.7; 14 women). The linearity of MRF with reference measurements in participants with diffuse liver disease (R2 value) for T1, T2, T2*, and PDFF maps was 0.86, 0.88, 0.54, and 0.99, respectively. The overall coefficients of variation for repeatability in the liver were 3.2%, 5.5%, 7.1%, and 4.6% for T1, T2, T2*, and PDFF maps, respectively. MRF-derived metrics showed high diagnostic performance in differentiating moderate or severe changes from mild or no changes (area under the receiver operating characteristic curve for fibrosis, inflammation, steatosis, and siderosis: 0.62 [95% CI: 0.52, 0.62], 0.92 [95% CI: 0.88, 0.92], 0.97 [95% CI: 0.96, 0.97], and 0.74 [95% CI: 0.57, 0.74], respectively). Conclusion Liver MR fingerprinting provided repeatable T1, T2, T2*, and proton density fat fraction maps in high agreement with reference quantitative mapping and may correlate with pathologic grades in participants with diffuse liver disease. © RSNA, 2022 Online supplemental material is available for this article.

Associations of Texture Features of Proton Density Fat Fraction Maps between Lumbar Vertebral Bone Marrow and Paraspinal Musculature.

Chemical shift encoding-based water–fat MRI (CSE-MRI)-derived proton density fat fraction (PDFF) has been used for non-invasive assessment of regional body fat distributions. More recently, texture analysis (TA) has been proposed to reveal even more detailed information about the vertebral or muscular composition beyond PDFF. The aim of this study was to investigate associations between vertebral bone marrow and paraspinal muscle texture features derived from CSE-MRI-based PDFF maps in a cohort of healthy subjects. In this study, 44 healthy subjects (13 males, 55 ± 30 years; 31 females, 39 ± 17 years) underwent 3T MRI including a six-echo three-dimensional (3D) spoiled gradient echo sequence used for CSE-MRI at the lumbar spine and the paraspinal musculature. The erector spinae muscles (ES), the psoas muscles (PS), and the vertebral bodies L1-4 (LS) were manually segmented. Mean PDFF values and texture features were extracted for each compartment. Features were compared between males and females using logistic regression analysis adjusted for age and body mass index (BMI). All texture features of ES except for Sum Average were significantly (p < 0.05) different between men and women. The three global texture features (Variance, Skewness, Kurtosis) for PS as well as LS showed a significant difference between male and female subjects (p < 0.05). Mean PDFF measured in PS and ES was significantly higher in females, but no difference was found for the vertebral bone marrow’s PDFF. Partial correlation analysis between the texture features of the spine and the paraspinal muscles revealed a highly significant correlation for Variance(global) (r = 0.61 for ES, r = 0.62 for PS; p < 0.001 respectively). Texture analysis using PDFF maps based on CSE-MRI revealed differences between healthy male and female subjects. Global texture features in the lumbar vertebral bone marrow allowed for differentiation between men and women, when the overall PDFF was not significantly different, indicating that PDFF maps may contain detailed and subtle textural information beyond fat fraction. The observed significant correlation of Variance(global) suggests a metabolic interrelationship between vertebral bone marrow and the paraspinal muscles.

Beyond mean value analysis - a voxel-based analysis of the quantitative MR biomarker water T2 in the presence of fatty infiltration in skeletal muscle tissue of patients with neuromuscular diseases.

The main pathologies in the muscles of patients with neuromuscular diseases (NMD) are fatty infiltration and edema. Recently, quantitative magnetic resonance (MR) imaging for determination of the MR biomarkers proton density fat fraction (PDFF) and water T2 (T2w ) has been advanced. Biophysical effects or pathology can have different effects on MR biomarkers. Thus, for heterogeneously affected muscles, the routinely performed mean or median value analyses of MR biomarkers are questionable. Our work presents a voxel-based histogram analysis of PDFF and T2w images to point out potential quantification errors. In 12 patients with NMD, chemical-shift encoding-based water-fat imaging for PDFF and T2 mapping with spectral adiabatic inversion recovery (SPAIR) for T2w determination was performed. Segmentation of nine thigh muscles was performed bilaterally (n = 216). PDFF and T2 maps were coregistered. A voxel-based comparison of PDFF and T2w showed a decreased T2w with increasing PDFF. Mean T2w and mean T2w without fatty voxels (PDFF < 10%) show good agreement, whereas standard deviation (σ) T2w and σ T2w without fatty voxels show increasing difference with increasing values of σ. Thereby two subgroups can be observed, referring to muscles in which the exclusion of fatty voxels has a negligible influence versus muscles in which a strong dependency of the T2w value distribution on the exclusion of fatty voxels is present. Because of the two opposite effects that influence T2w in a voxel, namely, (i) a pathophysiologically increased water mobility leading to T2w elevation, and (ii) a dependency of T2w on the PDFF leading to decreased T2w , the T2w distribution within a muscle might be heterogenous andthe routine mean or median analysis can lead to a misinterpretation of the muscle health. It was concluded that muscle T2w mean values can wrongly suggest healthy muscle tissue. A deeper analysis of the underlying value distribution is necessary. Therefore, a quantitative analysis of T2w histograms is a potential alternative.

MRI-based quantification of renal fat in obese individuals using different image analysis approaches.

The purpose of this study was to evaluate different renal proton density fat fraction (PDFF) analysis approaches. Additionally, we assessed renal fat in obese individuals and lean individuals. This was a retrospective observational case-control study. Twenty-eight obese individuals and 14 lean controls underwent MRI with multi-point Dixon technique for PDFF maps. The following renal PDFF image analysis approaches were performed and compared: (1) five circular regions of interest (ROIs) in six slices, (2) three circular ROIs in one slice, (3) freehand segmentation of renal parenchyma in one slice, and (4) freehand segmentation of renal parenchyma avoiding the renal border in one slice. Furthermore, renal PDFF was compared between obese and lean individuals. Methods 1, 2, and 4 were positively correlated (r ≥ 0.498, p ≤ 0.001). Renal PDFF values varied more with regards to ROI placement within slices than mean PDFF between slices. Using all methods, the obese individuals had significantly higher renal PDFF values compared with the lean controls. Renal PDFF should be measured covering large areas of the kidney while excluding artifacts. This can be achieved using multiple circular ROIs. Increased lipid accumulation in the kidneys was related to obesity.

On quantification errors of and proton density fat fraction mapping in trabecularized bone marrow in the static dephasing regime.

To study the effect of field inhomogeneity distributions in trabecularized bone regions on the gradient echo (GRE) signal with short TEs and to characterize quantification errors on and proton density fat fraction (PDFF) maps when using a water-fat model with an exponential decay model at short TEs. Field distortions were simulated based on a trabecular bone micro CT dataset. Simulations were performed for different bone volume fractions (BV/TV) and for different bone-fat composition values. A multi-TE UTE acquisition was developed to acquire multiple UTEs with random order to minimize eddy currents. The acquisition was validated in phantoms and applied in vivo in a volunteer's ankle and knee. Chemical shift encoded MRI (CSE-MRI) based on a Cartesian multi-TE GRE scan was acquired in the spine of patients with metastatic bone disease. Simulations showed that signal deviations from the exponential signal decay at short TEs were more prominent for a higher BV/TV. UTE multi-TE measurements reproduced in vivo the simulation-based predicted behavior. In regions with high BV/TV, the presence of field inhomogeneities induced an underestimation in trabecularized bone marrow when using CSE-MRI at 3T with a short TE. can be underestimated when using short TEs (<2ms at 3T) and a water-fat model with an exponential decay model in multi-echo GRE acquisitions of trabecularized bone marrow.

Association Between Adipose Tissue Proton Density Fat Fraction, Resting Metabolic Rate and FTO Genotype in Humans.

BackgroundThe difference of proton density fat fraction (PDFF) between supraclavicular and gluteal adipose tissue might indicate the presence of brown adipose tissue (BAT). Aim of this cross-sectional study was to investigate the association between PDFF over the supraclavicular fat region as a proxy of BAT proportion and resting metabolic rate (RMR). In addition, the association between the single nucleotide polymorphism (SNP) rs1421085 at the fat mass and obesity associated (FTO) gene locus and both PDFF and RMR was investigated.MethodsAnthropometric, clinical, and lifestyle data from 92 healthy adults (66.3% females, mean age: 36.2 ± 13.0 years, mean body mass index: 24.9 ± 5.4 kg/m2) were included in the analysis. The RMR was measured by indirect calorimetry. The magnetic resonance imaging (MRI) was used for the measurement of visceral and subcutaneous adipose tissue (VAT, SAT) volumes and for the measurement of adipose tissue PDFF.ResultsMean RMR of the whole group was 1 474.8 ± 242.2 kcal. Genotype data was available for 90 participants. After adjustment for age, sex, weight change and fat-free mass (FFM), no association was found between supraclavicular PDFF (p = 0.346) and gluteal PDFF (p = 0.252), respectively, and RMR, whereas statistically significant evidence for a negative association between delta PDFF (difference between gluteal PDFF and supraclavicular PDFF) and RMR (p = 0.027) was obtained. No statistically significant evidence was observed for per FTO risk allele change in RMR, gluteal and supraclavicular PDFF maps or volumes of VAT and SAT.ConclusionsSupraclavicular PDFF as a surrogate marker of BAT presence is not a determinant of RMR under basal conditions. In the present study, the FTO rs1421085 variant is not associated with either RMR or PDFF. Further studies are needed to elucidate the effect of BAT on RMR.

Association of Cervical and Lumbar Paraspinal Muscle Composition Using Texture Analysis of MR-Based Proton Density Fat Fraction Maps.

In this study, the associations of cervical and lumbar paraspinal musculature based on a texture analysis of proton density fat fraction (PDFF) maps were investigated to identify gender- and anatomical location-specific structural patterns. Seventy-nine volunteers (25 men, 54 women) participated in the present study (mean age ± standard deviation: men: 43.7 ± 24.6 years; women: 37.1 ± 14.0 years). Using manual segmentations of the PDFF maps, texture analysis was performed and texture features were extracted. A significant difference in the mean PDFF between men and women was observed in the erector spinae muscle (p < 0.0001), whereas the mean PDFF did not significantly differ in the cervical musculature and the psoas muscle (p > 0.05 each). Among others, Variance(global) and Kurtosis(global) showed significantly higher values in men than in women in all included muscle groups (p < 0.001). Not only the mean PDFF values (p < 0.001) but also Variance(global) (p < 0.001), Energy (p < 0.001), Entropy (p = 0.01), Homogeneity (p < 0.001), and Correlation (p = 0.037) differed significantly between the three muscle compartments. The cervical and lumbar paraspinal musculature composition seems to be gender-specific and has anatomical location-specific structural patterns.

MR-based proton density fat fraction (PDFF) of the vertebral bone marrow differentiates between patients with and without osteoporotic vertebral fractures

SummaryThe bone marrow proton density fat fraction (PDFF) assessed with MRI enables the differentiation between osteoporotic/osteopenic patients with and without vertebral fractures. Therefore, PDFF may be a potentially useful biomarker for bone fragility assessment.IntroductionTo evaluate whether magnetic resonance imaging (MRI)-based proton density fat fraction (PDFF) of vertebral bone marrow can differentiate between osteoporotic/osteopenic patients with and without vertebral fractures.MethodsOf the 52 study patients, 32 presented with vertebral fractures of the lumbar spine (66.4 ± 14.4 years, 62.5% women; acute low-energy osteoporotic/osteopenic vertebral fractures, N = 25; acute high-energy traumatic vertebral fractures, N = 7). These patients were frequency matched for age and sex to patients without vertebral fractures (N = 20, 69.3 ± 10.1 years, 70.0% women). Trabecular bone mineral density (BMD) values were derived from quantitative computed tomography. Chemical shift encoding-based water-fat MRI of the lumbar spine was performed, and PDFF maps were calculated. Associations between fracture status and PDFF were assessed using multivariable linear regression models.ResultsOver all patients, mean PDFF and trabecular BMD correlated significantly (r = − 0.51, P < 0.001). In the osteoporotic/osteopenic group, those patients with osteoporotic/osteopenic fractures had a significantly higher PDFF than those without osteoporotic fractures after adjusting for age, sex, weight, height, and trabecular BMD (adjusted mean difference [95% confidence interval], 20.8% [10.4%, 30.7%]; P < 0.001), although trabecular BMD values showed no significant difference between the subgroups (P = 0.63). For the differentiation of patients with and without vertebral fractures in the osteoporotic/osteopenic subgroup using mean PDFF, an area under the receiver operating characteristic (ROC) curve (AUC) of 0.88 (P = 0.006) was assessed. When evaluating all patients with vertebral fractures, those with high-energy traumatic fractures had a significantly lower PDFF than those with low-energy osteoporotic/osteopenic vertebral fractures (P < 0.001).ConclusionMR-based PDFF enables the differentiation between osteoporotic/osteopenic patients with and without vertebral fractures, suggesting the use of PDFF as a potential biomarker for bone fragility.

Quantification of 1.5 TT1 and T2* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies.

Despite its many advantages, experience with fetal magnetic resonance imaging (MRI) is limited, as is knowledge of how fetal tissue relaxation times change with gestational age (GA). Quantification of fetal tissue relaxation times as a function of GA provides insight into tissue changes during fetal development and facilitates comparison of images across time and subjects. This, therefore, can allow the determination of biophysical tissue parameters that may have clinical utility. To demonstrate the feasibility of quantifying previously unknown T1 and T2* relaxation times of fetal tissues in uncomplicated pregnancies as a function of GA at 1.5 T. Pilot. Nine women with singleton, uncomplicated pregnancies (28-38 weeks GA). All participants underwent two iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) acquisitions at different flip angles (6° and 20°) at 1.5 T. Segmentations of the lungs, liver, spleen, kidneys, muscle, and adipose tissue (AT) were conducted using water-only images and proton density fat fraction maps. Driven equilibrium single pulse observation of T1 (DESPOT1 ) was used to quantify the mean water T1 of the lungs, intraabdominal organs, and muscle, and the mean water and lipid T1 of AT. IDEAL T2* maps were used to quantify the T2* values of the lungs, intraabdominal organs, and muscle. F-tests were performed to assess the T1 and T2* changes of each analyzed tissue as a function of GA. No tissue demonstrated a significant change in T1 as a function of GA (lungs [P= 0.89]; liver [P= 0.14]; spleen [P= 0.59]; kidneys [P= 0.97]; muscle [P= 0.22]; AT: water [P= 0.36] and lipid [P= 0.14]). Only the spleen and muscle T2* showed a significant decrease as a function of GA (lungs [P= 0.67); liver [P= 0.05]; spleen [P< 0.05]; kidneys [P= 0.70]; muscle [P< 0.05]). These preliminary data suggest that the T1 of the investigated tissues is relatively stable over 28-38 weeks GA, while the T2* change in spleen and muscle decreases significantly in that period. 3 TECHNICAL EFFICACY STAGE: 2.