MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition.

PURPOSE: To develop a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LACs) in positron emission tomography (PET) using a physical compartmental model and ultrashort echo time (UTE)/multi-echo Dixon (mUTE) acquisitions. METHODS: We propose a three-dimensional (3D) mUTE sequence to acquire signals from water, fat, and short T2 components (e.g., bones) simultaneously in a single acquisition. The proposed mUTE sequence integrates 3D UTE with multi-echo Dixon acquisitions and uses sparse radial trajectories to accelerate imaging speed. Errors in the radial k-space trajectories are measured using a special k-space trajectory mapping sequence and corrected for image reconstruction. A physical compartmental model is used to fit the measured multi-echo MR signals to obtain fractions of water, fat, and bone components for each voxel, which are then used to estimate the continuous LAC map for PET attenuation correction. RESULTS: The performance of the proposed method was evaluated via phantom and in vivo human studies, using LACs from computed tomography (CT) as reference. Compared to Dixon- and atlas-based MRAC methods, the proposed method yielded PET images with higher correlation and similarity in relation to the reference. The relative absolute errors of PET activity values reconstructed by the proposed method were below 5% in all of the four lobes (frontal, temporal, parietal, and occipital), cerebellum, whole white matter, and gray matter regions across all subjects (n = 6). CONCLUSIONS: The proposed mUTE method can generate subject-specific, continuous LAC map for PET attenuation correction in PET/MR.

Free-Breathing Three-Dimensional T1 Mapping of the Heart Using Subspace-Based Data Acquisition and Image Reconstruction.

Mapping the longitudinal relaxation time constant (T1) of the myocardium using Magnetic Resonance Imaging (MRI) is an emerging technique for quantitative assessment of the morphology and viability of the myocardium. However, three-dimensional (3D) T1 mapping of the heart is challenging due to the high dimensionality of the signal and the presence of cardiac and respiratory motions. We propose a subspace-based method for free-breathing 3D T1 mapping of the heart without respiratory gating. The image function is represented as a high-order partially separable (PS) function to explore the inherent spatiotemporal correlations of the underlying signal. A special data acquisition scheme enabled by the high-order PS model is used for sparse sampling of the (k,t)-space, where complementary sparse datasets are acquired, each covering only a small portion of the (k,t)-space to characterize a single subspace (spatial or temporal). High-resolution dynamic MR images are reconstructed from the highly undersampled (k,t)-space using low-rank tensor and sparsity constraints. We demonstrate the feasibility of our proposed method using in vivo data obtained from healthy subjects on a 3T MR scanner. The proposed method can enable new clinical applications of T1 mapping in cardiac MR.

Quantification of liver fat in the presence of iron overload.

PURPOSE: To evaluate the accuracy of R2* models (1/T2 * = R2*) for chemical shift-encoded magnetic resonance imaging (CSE-MRI)-based proton density fat-fraction (PDFF) quantification in patients with fatty liver and iron overload, using MR spectroscopy (MRS) as the reference standard. MATERIALS AND METHODS: Two Monte Carlo simulations were implemented to compare the root-mean-squared-error (RMSE) performance of single-R2* and dual-R2* correction in a theoretical liver environment with high iron. Fatty liver was defined as hepatic PDFF >5.6% based on MRS; only subjects with fatty liver were considered for analyses involving fat. From a group of 40 patients with known/suspected iron overload, nine patients were identified at 1.5T, and 13 at 3.0T with fatty liver. MRS linewidth measurements were used to estimate R2* values for water and fat peaks. PDFF was measured from CSE-MRI data using single-R2* and dual-R2* correction with magnitude and complex fitting. RESULTS: Spectroscopy-based R2* analysis demonstrated that the R2* of water and fat remain close in value, both increasing as iron overload increases: linear regression between R2*W and R2*F resulted in slope = 0.95 [0.79-1.12] (95% limits of agreement) at 1.5T and slope = 0.76 [0.49-1.03] at 3.0T. MRI-PDFF using dual-R2* correction had severe artifacts. MRI-PDFF using single-R2* correction had good agreement with MRS-PDFF: Bland-Altman analysis resulted in -0.7% (bias) ± 2.9% (95% limits of agreement) for magnitude-fit and -1.3% ± 4.3% for complex-fit at 1.5T, and -1.5% ± 8.4% for magnitude-fit and -2.2% ± 9.6% for complex-fit at 3.0T. CONCLUSION: Single-R2* modeling enables accurate PDFF quantification, even in patients with iron overload. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:428-439.

Quantitative susceptibility mapping in the abdomen as an imaging biomarker of hepatic iron overload.

PURPOSE: The purpose of this work was to develop and demonstrate feasibility and initial clinical validation of quantitative susceptibility mapping (QSM) in the abdomen as an imaging biomarker of hepatic iron overload. THEORY AND METHODS: In general, QSM is faced with the challenges of background field removal and dipole inversion. Respiratory motion, the presence of fat, and severe iron overload further complicate QSM in the abdomen. We propose a technique for QSM in the abdomen that addresses these challenges. Data were acquired from 10 subjects without hepatic iron overload and 33 subjects with known or suspected iron overload. The proposed technique was used to estimate the susceptibility map in the abdomen, from which hepatic iron overload was measured. As a reference, spin-echo data were acquired for R2-based LIC estimation. Liver R2* was measured for correlation with liver susceptibility estimates. RESULTS: Correlation between susceptibility and R2-based LIC estimation was R(2) = 0.76 at 1.5 Tesla (T) and R(2) = 0.83 at 3T. Furthermore, high correlation between liver susceptibility and liver R2* (R(2) = 0.94 at 1.5T; R(2) = 0.93 at 3T) was observed. CONCLUSION: We have developed and demonstrated initial validation of QSM in the abdomen as an imaging biomarker of hepatic iron overload.

Improving chemical shift encoded water-fat separation using object-based information of the magnetic field inhomogeneity.

PURPOSE: The purpose of this work was to improve the robustness of existing chemical shift encoded water-fat separation methods by incorporating object-based information of the B0 field inhomogeneity. THEORY: The primary challenge in water-fat separation is the estimation of phase shifts that arise from B0 field inhomogeneity, which is composed of the background field and susceptibility-induced field. The susceptibility-induced field can be estimated if the susceptibility distribution is known or can be approximated. In this work, the susceptibility distribution is approximated from the source images using the known susceptibility values of water, fat, and air. The field estimate is then demodulated from the source images before water-fat separation. METHODS: Chemical shift encoded source images were acquired in anatomical regions that are prone to water-fat swaps. The images were processed using algorithms from the ISMRM Fat-Water Toolbox, with and without the object-based field map information. The estimates were compared to examine the benefit of using the object-based field map information. RESULTS: Multiple cases are shown in which water-fat swaps were avoided by using the object-based information of the B0 field map. CONCLUSION: Object-based information of the B0 field may improve the robustness of existing chemical shift encoded water-fat separation methods.

Quantitative hepatic perfusion modeling using DCE-MRI with sequential breathholds.

PURPOSE: To develop and demonstrate the feasibility of a new formulation for quantitative perfusion modeling in the liver using interrupted DCE-MRI data acquired during multiple sequential breathholds. MATERIALS AND METHODS: A new mathematical formulation to estimate quantitative perfusion parameters using interrupted data was developed. Using this method, we investigated whether a second degree-of-freedom in the tissue residue function (TRF) improves quality-of-fit criteria when applied to a dual-input single-compartment perfusion model. We subsequently estimated hepatic perfusion parameters using DCE-MRI data from 12 healthy volunteers and 9 cirrhotic patients with a history of hepatocellular carcinoma (HCC); and examined the utility of these estimates in differentiating between healthy liver, cirrhotic liver, and HCC. RESULTS: Quality-of-fit criteria in all groups were improved using a Weibull TRF (2 degrees-of-freedom) versus an exponential TRF (1 degree-of-freedom), indicating nearer concordance of source DCE-MRI data with the Weibull model. Using the Weibull TRF, arterial fraction was greater in cirrhotic versus normal liver (39 ± 23% versus 15 ± 14%, P = 0.07). Mean transit time (20.6 ± 4.1 s versus 9.8 ± 3.5 s, P = 0.01) and arterial fraction (39 ± 23% versus 73 ± 14%, P = 0.04) were both significantly different between cirrhotic liver and HCC, while differences in total perfusion approached significance. CONCLUSION: This work demonstrates the feasibility of estimating hepatic perfusion parameters using interrupted data acquired during sequential breathholds.

High-spatial and high-temporal resolution dynamic contrast-enhanced perfusion imaging of the liver with time-resolved three-dimensional radial MRI.

PURPOSE: Detection, characterization, and monitoring the treatment of hepatocellular carcinomas (HCC) in patients with cirrhosis is challenging because of their variable and rapid arterial enhancement. Multiphase dynamic contrast-enhanced MRI is used clinically for HCC assessment; however, the method suffers from limited temporal resolution and difficulty in coordinating imaging and breath-hold timing within a narrow temporal window of interest. In this article, a volumetric, high-spatial resolution, and high-temporal resolution dynamic contrast-enhanced liver imaging method for improved detection and characterization of HCC is demonstrated. METHODS: A time-resolved three-dimensional radial acquisition with iterative sensitivity-encoding reconstruction images the entire abdomen and thorax with high spatial and temporal resolution, using real-time three-dimensional fluoroscopy to match the breath hold to contrast arrival. The sequence was tested on 17 subjects, including eight patients with HCC or other hypervascular focal lesions. RESULTS: This technique was successful in acquiring volumetric imaging of the entire liver with 2.1-mm isotropic spatial and true 4-s temporal resolution. CONCLUSION: This technique may be suitable for detecting, characterizing, and monitoring the treatment of HCC. It also holds significant potential for perfusion modeling, which may provide a noninvasive means to rapidly determine the efficacy of chemotherapeutic agents in these tumors over the entire liver volume.

Comparison of R2* correction methods for accurate fat quantification in fatty liver.

PURPOSE: To compare the performance of fat fraction quantification using single-R(2)* and dual-R(2)* correction methods in patients with fatty liver, using MR spectroscopy (MRS) as the reference standard. MATERIALS AND METHODS: From a group of 97 patients, 32 patients with hepatic fat fraction greater than 5%, as measured by MRS, were identified. In these patients, chemical shift encoded fat-water imaging was performed, covering the entire liver in a single breathhold. Fat fraction was measured from the imaging data by postprocessing using 6 different models: single- and dual-R(2)* correction, each performed with complex fitting, magnitude fitting, and mixed magnitude/complex fitting to compare the effects of phase error correction. Fat fraction measurements were compared with co-registered spectroscopy measurements using linear regression. RESULTS: Linear regression demonstrated higher agreement with MRS using single-R(2)* correction compared with dual-R(2)* correction. Among single-R(2)* models, all 3 fittings methods performed similarly well (slope = 1.0 ± 0.06, r(2) = 0.89-0.91). CONCLUSION: Single-R(2)* modeling is more accurate than dual-R(2)* modeling for hepatic fat quantification in patients, even in those with high hepatic fat concentrations.