Rosette Trajectories Enable Ungated, Motion-Robust, Simultaneous Cardiac and Liver T2 * Iron Assessment

Free-breathing MRI techniques for fat and R2* quantification in the liver

OBJECTIVE

To review the recent advancements in free-breathing MRI techniques for proton-density fat fraction (PDFF) and R2* quantification in the liver, and discuss the current challenges and future opportunities.

MATERIALS AND METHODS

This work focused on recent developments of different MRI pulse sequences, motion management strategies, and reconstruction approaches that enable free-breathing liver PDFF and R2* quantification.

RESULTS

Different free-breathing liver PDFF and R2* quantification techniques have been evaluated in various cohorts, including healthy volunteers and patients with liver diseases, both in adults and children. Initial results demonstrate promising performance with respect to reference measurements. These techniques have a high potential impact on providing a solution to the clinical need of accurate liver fat and iron quantification in populations with limited breath-holding capacity.

DISCUSSION

As these free-breathing techniques progress toward clinical translation, studies of the linearity, bias, and repeatability of free-breathing PDFF and R2* quantification in a larger cohort are important. Scan acceleration and improved motion management also hold potential for further enhancement.

Optimization in the space domain for density compensation with the nonuniform FFT

The non-uniform Discrete Fourier Transform algorithm has shown great utility for reconstructing images from non-uniformly spaced Fourier samples in several imaging modalities. Due to the non-uniform spacing, some correction for the variable density of the samples must be made. Common methods for generating density compensation values are either sub-optimal or only consider a finite set of points in the optimization. This manuscript presents an algorithm for generating density compensation values from a set of Fourier samples that takes into account the point spread function over an entire rectangular region in the image domain. We show that the reconstructed images using the density compensation values of this method are of superior quality when compared to other standard methods. Results are shown with a numerical phantom and with magnetic resonance images of the abdomen and the knee.

Parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) for accelerating 4D-MRI

Dynamic magnetic resonance imaging (MRI) acquisitions are relatively slow due to physical and physiological limitations. The spatial-temporal dictionary learning (DL) approach accelerates dynamic MRI by learning spatial-temporal correlations, but the regularization parameters need to be manually adjusted, the performance at high acceleration rate is limited, and the reconstruction can be time-consuming. Deep learning techniques have shown good performance in accelerating MRI due to the powerful representational capabilities of neural networks. In this work, we propose a parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) framework that combines dictionary learning with deep learning algorithms and utilizes the spatial-temporal prior information of dynamic MRI data to achieve better reconstruction quality and efficiency. The coefficient estimation modules (CEM) are designed in the framework to adaptively adjust the regularization coefficients. Experimental results show that combining dictionary learning with deep neural networks and using spatial-temporal dictionaries can obviously improve the image quality and computational efficiency compared with the state-of-the-art non-Cartesian imaging methods for accelerating the 4D-MRI especially at high acceleration rate.

Cardiac MRF using rosette trajectories for simultaneous myocardial T1, T2, and proton density fat fraction mapping

The goal of this work is to extend prior work on cardiac MR Fingerprinting (cMRF) using rosette k-space trajectories to enable simultaneous T₁, T₂, and proton density fat fraction (PDFF) mapping in the heart. A rosette trajectory designed for water-fat separation at 1.5T was used in a 2D ECG-triggered 15-heartbeat cMRF sequence. Water and fat specific T₁ and T₂ maps were generated from the cMRF data. A PDFF map was also retrieved using Hierarchical IDEAL by segmenting the rosette cMRF data into multiple echoes. The accuracy of rosette cMRF in T₁, T₂, and PDFF quantification was validated in the ISMRM/NIST phantom and an in-house built fat fraction phantom, respectively. The proposed method was also applied for myocardial tissue mapping of healthy subjects and cardiac patients at 1.5T. T₁, T₂, and PDFF values measured using rosette cMRF in the ISMRM/NIST phantom and the fat fraction phantom agreed well with the reference values. In 16 healthy subjects, rosette cMRF yielded T₁ values which were 80~90 ms higher than spiral cMRF and MOLLI. T₂ values obtained using rosette cMRF were ~3 ms higher than spiral cMRF and ~5 ms lower than conventional T₂-prep bSSFP method. Rosette cMRF was also able to detect abnormal T₁ and T₂ values in cardiomyopathy patients and may provide more accurate maps due to effective fat suppression. In conclusion, this study shows that rosette cMRF has the potential for efficient cardiac tissue characterization through simultaneous quantification of myocardial T₁, T₂, and PDFF.

Characterizing Fibrosis and Inflammation in a Partial Bile Duct Ligation Mouse Model by Multiparametric Magnetic Resonance Imaging

Background

Partial bile duct ligation (PBDL) model is a reliable cholestatic fibrosis experimental model that showed complex histopathological changes. Magnetic resonance imaging (MRI) features of PBDL have not been well characterized.

Purpose

To investigate the potential of MRI parameters in assessing fibrosis in PBDL and explore the relationships between MRI and pathological features.

Animal Model

Established PBDL models.

Population

Fifty‐four mice were randomly divided into four timepoints PBDL groups and one sham group.

Field Strength/Sequence

3.0 T; MRI sequences included T1‐weighted fast spin‐echo (FSE), T2‐weighted single shot FSE, variable flip angle T1 mapping, multi‐echo SE T2 mapping, multi‐echo gradient‐echo T2* mapping, and multi‐b‐value diffusion‐weighted imaging.

Assessment

MRI examination was performed at the corresponding timepoints after surgery. Native T1, ΔT1 (T1native‐T1post), T2, T2*, apparent diffusion coefficient (ADC) values, histogram parameters (skewness and kurtosis), intravoxel incoherent motion parameters (f, D, and D^*) within the entire ligated (PBDL), non‐ligated liver (PBDL), and whole liver (sham) were obtained. Fibrosis and inflammation were assessed in Masson and H&E staining slices using the Metavir and activity scoring system.

Statistical Tests

One‐way ANOVA, Spearman's rank correlation, and receiver operating characteristic curves were performed. P < 0.05 was considered statistically significant.

Results

Fibrosis and inflammation were finally staged as F3 and A3 in ligated livers but were not observed in non‐ligated or sham livers. Ligated livers displayed significantly elevated native T1, ΔT1, T2, and reduced ADC and T2^* than other livers. Spearman's correlation showed better correlation with inflammation (r = 0.809) than fibrosis (r = 0.635) in T2 and both ΔT1 and ADC showed stronger correlation with fibrosis (r = 0.704 and r = −0.718) than inflammation (r = 0.564 and r = −0.550). Area under the curve (AUC) for ΔT1 performed the highest (0.896). When combined with all relative parameters, AUC increased to 0.956.

Data Conclusion

Multiparametric MRI can evaluate and differentiate pathological changes in PBDL. ΔT1 and ADC better correlated with fibrosis while T2 stronger with inflammation.

Level of Evidence

1

Technical Efficacy

Stage 2