Accurate fatty acid composition estimation of adipose tissue in the abdomen based on bipolar multi-echo MRI.

PURPOSE: To develop a bipolar multi-echo MRI method for the accurate estimation of the adipose tissue fatty acid composition (FAC) using clinically relevant protocols at clinical field strength. METHODS: The proposed technique jointly estimates confounding factors (field map, R2* , eddy-current phase) and triglyceride saturation state parameters by fitting multi-echo gradient echo acquisitions to a complex signal model. The noise propagation behavior was improved by applying a low-rank enforcing denoising technique and by addressing eddy-current-induced phase discrepancies analytically. The impact of the total echo train duration on the FAC parameter map accuracy was analyzed in an oil phantom at 3T. Accuracy and reproducibility assessment was based on in vitro oil phantom measurements at two field strengths (3T and 1.5T) and with two different protocols. Repeatability was assessed in vivo in patients (n = 8) with suspected fatty liver disease using test-retest acquisitions in the abdominal subcutaneous, perirenal and mesenteric fat depots. RESULTS: Echo train readout durations of at least five times the conventional in-phase time were required for accurate FAC estimation in areas of high fat content. In vitro, linear regression and Bland-Altman analyses demonstrated strong (r > 0.94) and significant (P â‰ª 0.01) correlations between measured and reference FACs for all acquisitions, with smaller overall intercepts and biases at 3T compared to 1.5T. In vivo, reported mean absolute differences between test and retest were 1.54%, 3.31%, and 2.63% for the saturated, mono-unsaturated, and poly-unsaturated fat component, respectively. CONCLUSIONS: Accurate and reproducible MRI-based FAC quantification within a breath-hold is possible at clinical field strengths.

Single-breath-hold 3-D CINE imaging of the left ventricle using Cartesian sampling.

OBJECTIVES: Our objectives were to evaluate a single-breath-hold approach for Cartesian 3-D CINE imaging of the left ventricle with a nearly isotropic resolution of [Formula: see text] and a breath-hold duration of [Formula: see text]19 s against a standard stack of 2-D CINE slices acquired in multiple breath-holds. Validation is performed with data sets from ten healthy volunteers. MATERIALS AND METHODS: A Cartesian sampling pattern based on the spiral phyllotaxis and a compressed sensing reconstruction method are proposed to allow 3-D CINE imaging with high acceleration factors. The fully integrated reconstruction uses multiple graphics processing units to speed up the reconstruction. The 2-D CINE and 3-D CINE are compared based on ventricular function parameters, contrast-to-noise ratio and edge sharpness measurements. RESULTS: Visual comparisons of corresponding short-axis slices of 2-D and 3-D CINE show an excellent match, while 3-D CINE also allows reformatting to other orientations. Ventricular function parameters do not significantly differ from values based on 2-D CINE imaging. Reconstruction times are below 4 min. CONCLUSION: We demonstrate single-breath-hold 3-D CINE imaging in volunteers and three example patient cases, which features fast reconstruction and allows reformatting to arbitrary orientations.

Single-breath-hold abdominal [Formula: see text] mapping using 3D Cartesian Look-Locker with spatiotemporal sparsity constraints.

OBJECTIVE: Our aim was to develop and validate a 3D Cartesian Look-Locker [Formula: see text] mapping technique that achieves high accuracy and whole-liver coverage within a single breath-hold. MATERIALS AND METHODS: The proposed method combines sparse Cartesian sampling based on a spatiotemporally incoherent Poisson pattern and k-space segmentation, dedicated for high-temporal-resolution imaging. This combination allows capturing tissue with short relaxation times with volumetric coverage. A joint reconstruction of the 3D + inversion time (TI) data via compressed sensing exploits the spatiotemporal sparsity and ensures consistent quality for the subsequent multistep [Formula: see text] mapping. Data from the National Institute of Standards and Technology (NIST) phantom and 11 volunteers, along with reference 2D Look-Locker acquisitions, are used for validation. 2D and 3D methods are compared based on [Formula: see text] values in different abdominal tissues at 1.5 and 3 T. RESULTS: [Formula: see text] maps obtained from the proposed 3D method compare favorably with those from the 2D reference and additionally allow for reformatting or volumetric analysis. Excellent agreement is shown in phantom [bias[Formula: see text] < 2%, bias[Formula: see text] < 5% for (120; 2000) ms] and volunteer data (3D and 2D deviation < 4% for liver, muscle, and spleen) for clinically acceptable scan (20 s) and reconstruction times (< 4 min). CONCLUSION: Whole-liver [Formula: see text] mapping with high accuracy and precision is feasible in one breath-hold using spatiotemporally incoherent, sparse 3D Cartesian sampling.

Effect of a Low-Rank Denoising Algorithm on Quantitative Magnetic Resonance Imaging-Based Measures of Liver Fat and Iron.

PURPOSE: This study aimed to assess the effect of a low-rank denoising algorithm on quantitative magnetic resonance imaging-based measures of liver fat and iron. MATERIALS AND METHODS: This was an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant, retrospective analysis of 42 consecutive subjects who were imaged at 3T using a multiecho gradient echo sequence that was reconstructed using the multistep adaptive fitting algorithm to obtain quantitative proton density fat fraction (PDFF) and R2* maps (original maps). A patch-wise low-rank denoising algorithm was then applied, and PDFF and R2* maps were created (denoised maps). Three readers independently rated the PDFF maps in terms of vessel and liver edge sharpness and image noise using a 5-point scale. Two other readers independently measured mean and standard deviation of PDFF and R2* values for the original and denoised maps; values were compared using intraclass correlation coefficients (ICCs) and mean difference analyses. RESULTS: Qualitatively, the denoised maps were preferred by all 3 readers based on image noise (P < 0.001) and by 2 of 3 readers based on vessel edge sharpness (P < 0.001-0.99). No reader had a significant preference regarding liver edge sharpness (P = 0.16-0.48). Quantitatively, agreement was near perfect between the original and denoised maps for PDFF (ICC = 0.995) and R2* (ICC = 0.995) values. Mean quantitative values obtained from the original and denoised maps were similar for liver PDFF (7.6 ± 7.7% vs 7.7 ± 7.8%; P = 0.63) and R2* (52.9 ± 40.3s vs 52.8 ± 41.1 s, P = 0.74). CONCLUSIONS: Applying the low-rank denoising algorithm to liver fat and iron quantification reduces image noise in PDFF and R2* maps without adversely affecting mean quantitative values or subjective image quality.

Accelerating multi-echo water-fat MRI with a joint locally low-rank and spatial sparsity-promoting reconstruction.

OBJECTIVES: Our aim was to demonstrate the benefits of using locally low-rank (LLR) regularization for the compressed sensing reconstruction of highly-accelerated quantitative water-fat MRI, and to validate fat fraction (FF) and [Formula: see text] relaxation against reference parallel imaging in the abdomen. MATERIALS AND METHODS: Reconstructions using spatial sparsity regularization (SSR) were compared to reconstructions with LLR and the combination of both (LLR+SSR) for up to seven fold accelerated 3-D bipolar multi-echo GRE imaging. For ten volunteers, the agreement with the reference was assessed in FF and [Formula: see text] maps. RESULTS: LLR regularization showed superior noise and artifact suppression compared to reconstructions using SSR. Remaining residual artifacts were further reduced in combination with SSR. Correlation with the reference was excellent for FF with [Formula: see text] = 0.99 (all methods) and good for [Formula: see text] with [Formula: see text] = [0.93, 0.96, 0.95] for SSR, LLR and LLR+SSR. The linear regression gave slope and bias (%) of (0.99, 0.50), (1.01, 0.19) and (1.01, 0.10), and the hepatic FF/[Formula: see text] standard deviation was 3.5%/12.1 s[Formula: see text], 1.9%/6.4 s[Formula: see text] and 1.8%/6.3 s[Formula: see text] for SSR, LLR and LLR+SSR, indicating the least bias and highest SNR for LLR+SSR. CONCLUSION: A novel reconstruction using both spatial and spectral regularization allows obtaining accurate FF and [Formula: see text] maps for prospectively highly accelerated acquisitions.

Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series.

The purpose of this work is to evaluate methods from deep learning for application to Magnetic Resonance Fingerprinting (MRF). MRF is a recently proposed measurement technique for generating quantitative parameter maps. In MRF a non-steady state signal is generated by a pseudo-random excitation pattern. A comparison of the measured signal in each voxel with the physical model yields quantitative parameter maps. Currently, the comparison is done by matching a dictionary of simulated signals to the acquired signals. To accelerate the computation of quantitative maps we train a Convolutional Neural Network (CNN) on simulated dictionary data. As a proof of principle we show that the neural network implicitly encodes the dictionary and can replace the matching process.