Wavelet MRE: Imaging propagating broadband acoustic waves with wavelet-based motion-encoding gradients.

PURPOSE: To demonstrate a novel MR elastography (MRE) technique, termed here wavelet MRE. With this technique, broadband motion sensitivity is achievable. Moreover, the true tissue displacement can be reconstructed with a simple inverse transform. METHODS: A wavelet MRE sequence was developed with motion-encoding gradients based on Haar wavelets. From the phase images' displacement was estimated using an inverse transform. Simulations were performed using a frequency sweep and a transient as ground-truth motions. A PVC phantom was scanned using wavelet MRE and standard MRE with both transient (one and 10 cycles of 90-Hz motion) and steady-state dual-frequency motion (30 and 60 Hz) for comparison. The technique was tested in a human brain, and motion trajectories were estimated for each voxel. RESULTS: In simulation, the displacement information estimated from wavelet MRE closely matched the true motion. In the phantom test, the MRE phase data generated from the displacement information derived from wavelet MRE agreed well with standard MRE data. Testing of wavelet MRE to assess transient motion waveforms in the brain was successful, and the tissue motion observed was consistent with a previous study. CONCLUSION: The uniform and broadband frequency response of wavelet MRE makes it a promising method for imaging transient, multifrequency motion, or motion with unknown frequency content. One potential application is measuring the response of brain tissue undergoing low-amplitude, transient vibrations as a model for the study of traumatic brain injury.

Accelerated free-breathing liver fat and R 2 * quantification using multi-echo stack-of-radial MRI with motion-resolved multidimensional regularized reconstruction: Initial retrospective evaluation.

PURPOSE: To improve image quality, mitigate quantification biases and variations for free-breathing liver proton density fat fraction (PDFF) and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ quantification accelerated by radial k-space undersampling. METHODS: A free-breathing multi-echo stack-of-radial MRI method was developed with compressed sensing with multidimensional regularization. It was validated in motion phantoms with reference acquisitions without motion and in 11 subjects (6 patients with nonalcoholic fatty liver disease) with reference breath-hold Cartesian acquisitions. Images, PDFF, and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ maps were reconstructed using different radial view k-space sampling factors and reconstruction settings. Results were compared with reference-standard results using Bland-Altman analysis. Using linear mixed-effects model fitting (p < 0.05 considered significant), mean and SD were evaluated for biases and variations of PDFF and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , respectively, and coefficient of variation on the first echo image was evaluated as a surrogate for image quality. RESULTS: Using the empirically determined optimal sampling factor of 0.25 in the accelerated in vivo protocols, mean differences and limits of agreement for the proposed method were [-0.5; -33.6, 32.7] s-1 for R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and [-1.0%; -5.8%, 3.8%] for PDFF, close to those of a previous self-gating method using fully sampled radial views: [-0.1; -27.1, 27.0] s-1 for R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and [-0.4%; -4.5%, 3.7%] for PDFF. The proposed method had significantly lower coefficient of variation than other methods (p < 0.001). Effective acquisition time of 64 s or 59 s was achieved, compared with 171 s or 153 s for two baseline protocols with different radial views corresponding to sampling factor of 1.0. CONCLUSION: This proposed method may allow accelerated free-breathing liver PDFF and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ mapping with reduced biases and variations.

State-of-the-Art Quantification of Liver Iron With MRI-Vendor Implementation and Available Tools.

The role of MRI to estimate liver iron concentration (LIC) for identifying patients with iron overload and guiding the titration of chelation therapy is increasingly established for routine clinical practice. However, the existence of multiple MRI-based LIC quantification techniques limits standardization and widespread clinical adoption. In this article, we review the existing and widely accepted MRI-based LIC estimation methods at 1.5 T and 3 T: signal intensity ratio (SIR) and relaxometry (R2 and R2*) and discuss the basic principles, acquisition and analysis protocols, and MRI-LIC calibrations for each technique. Further, we provide an up-to-date information on MRI vendor implementations and available offline commercial and free software for each MRI-based LIC quantification approach. We also briefly review the emerging and advanced MRI techniques for LIC estimation and their current limitations for clinical use. Lastly, we discuss the implications of MRI-based LIC measurements on clinical use and decision-making in the management of patients with iron overload. Some of the key highlights from this review are as follows: 1) Both R2 and R2* can estimate accurate and reproducible LIC, when validated acquisition parameters and analysis protocols are applied, 2) Although the Ferriscan R2 method has been widely used, recent consensus and guidelines endorse R2*-MRI as the most accurate and reproducible method for LIC estimation, 3) Ongoing efforts aim to establish R2*-MRI as the standard approach for quantifying LIC, and 4) Emerging R2*-MRI techniques employ radial sampling strategies and offer improved motion compensation and broader dynamic range for LIC estimation. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Application of the magnetic resonance 3D multiecho Dixon sequence for quantifying hepatic iron overload and steatosis in patients with thalassemia.

OBJECTIVE: To investigate the feasibility and diagnostic efficacy of a 3D multiecho Dixon (qDixon) research application for simultaneously quantifying the liver iron concentration (LIC) and steatosis in thalassemia patients. MATERIALS AND METHODS: This prospective study enrolled participants with thalassemia who underwent 3 T MRI of the liver for the evaluation of hepatic iron overload. The imaging protocol including qDixon and conventional T2* mapping based on 2D multiecho gradient echo (ME GRE) sequences respectively. Regions of interest (ROIs) were drawn in the liver on the qDixon maps to obtain R2* and proton density fat fraction (PDFF). The reference R2* value was measured and calculated on conventional T2* mapping using the CMRtools software. Correlation analysis, Linear regression analysis, and Bland-Altman analysis were performed. RESULTS: 84 patients were finally included in this study. The median R2*-ME-GRE was 366.97 (1/s), range [206.68 (1/s), 522.20 (1/s)]. 8 patients had normal hepatic iron deposition, 16 had Insignificant, 42 had mild, 18 had moderate. The median of R2*-qDixon was 376.88 (1/s) [219.33 (1/s), 491.75 (1/s)]. A strong correlation was found between the liver R2*-qDixon and the R2*-ME-GRE (r = 0.959, P < 0.001). The median value of PDFF was 1.76% (1.10%, 2.95%). 8 patients had mild fatty liver, and 1 had severe fatty liver. CONCLUSION: MR qDixon research sequence can rapidly and accurately quantify liver iron overload, that highly consistent with the measured via conventional GRE sequence, and it can also simultaneously detect hepatic steatosis, this has great potential for clinical evaluation of thalassemia patients.

Comparison of R2* and FerriScan liver iron concentration (LIC) quantification in the clinical classification of Iron overload states.

PURPOSE: This study assessed the clinical classification performance of an R2*-based MRI technique for LIC quantification relative to FerriScan, with intra-patient FerriScan LIC uncertainty taken into account. The variabilities of R2* and FerriScan LIC were also assessed. MATERIALS AND METHODS: This was an ethics approved retrospective study, performed on patients undergoing chelation treatment for iron overload. 126 patients (69 women, 57 men), with an age of 42 +/- 16 years (range 19-86 years) were included. FerriScan and R2* MRI at 1.5 T were performed as part of a routine liver iron assessment protocol. For R2* MRI, a commercially available pulse sequence and reconstruction implementation was used, together with a previously derived calibration curve to convert R2* to LIC. Clinical classifications arising from R2*-derived LIC estimates were compared to those based on FerriScan. The accuracy and precision of the R2* technique was calculated. The variabilities of FerriScan- and R2*-derived estimates of LIC were compared with a Wilcoxon Signed Rank test. Significance was set at the 95% confidence level. RESULTS: The precision of R2* ranged from 0.59 to 0.92, with an overall accuracy of 72%. When intra-patient FerriScan LIC uncertainty was considered, precision and accuracy increased to >94% and 97% respectively. The R2*-LIC variability (=17%) was significantly lower than the FerriScan-LIC variability (34%) at the 95% confidence level (p < 10-3). CONCLUSION: MRI R2*-based LIC estimates provided a similar clinical classification as FerriScan. The intra-patient uncertainty of R2*-based LIC estimates was significantly lower than FerriScan.

Feasibility and performance of spin-echo EPI MR elastography at 3 Tesla for staging hepatic fibrosis in the presence of hepatic iron overload.

PURPOSE: To assess the feasibility and performance of MR elastography (MRE) for quantifying liver fibrosis in patients with and without hepatic iron overload. METHODS: This retrospective single-center study analyzed 139 patients who underwent liver MRI at 3 Tesla including MRE (2D spin-echo EPI sequence) and R2* mapping for liver iron content (LIC) estimation. MRE feasibility and diagnostic performance between patients with normal and elevated LIC were compared. RESULTS: Patients with elevated LIC (21%) had significantly higher MRE failure rates (24.1% vs. 3.6%, p < 0.001) compared to patients with normal LIC (79%). For those with only insignificant to mild iron overload (LIC < 5.4 mg/g; 17%), MRE failure rate did not differ significantly from patients without iron overload (8.3% vs. 3.6%, p = 0.315). R2* predicted MRE failure with fair accuracy at a threshold of R2* ≥ 269 s-1 (LIC of approximately 4.6 mg/g). MRE showed good diagnostic performance for detecting significant (≥ F2) and severe fibrosis (≥ F3) in patients without (AUC 0.835 and 0.900) and with iron overload (AUC 0.818 and 0.889) without significant difference between the cohorts (p = 0.884 and p = 0.913). For detecting cirrhosis MRE showed an excellent diagnostic performance in both groups (AUC 0.944 and 1.000, p = 0.009). CONCLUSION: Spin-echo EPI MRE at 3 Tesla is feasible in patients with mild iron overload with good to excellent performance for detecting hepatic fibrosis with a failure rate comparable to patients without iron overload.

Self-supervised MRI denoising: leveraging Stein's unbiased risk estimator and spatially resolved noise maps.

Thermal noise caused by the imaged object is an intrinsic limitation in magnetic resonance imaging (MRI), resulting in an impaired clinical value of the acquisitions. Recently, deep learning (DL)-based denoising methods achieved promising results by extracting complex feature representations from large data sets. Most approaches are trained in a supervised manner by directly mapping noisy to noise-free ground-truth data and, therefore, require extensive paired data sets, which can be expensive or infeasible to obtain for medical imaging applications. In this work, a DL-based denoising approach is investigated which operates on complex-valued reconstructed magnetic resonance (MR) images without noise-free target data. An extension of Stein's unbiased risk estimator (SURE) and spatially resolved noise maps quantifying the noise level with pixel accuracy were employed during the training process. Competitive denoising performance was achieved compared to supervised training with mean squared error (MSE) despite optimizing the model without noise-free target images. The proposed DL-based method can be applied for MR image enhancement without requiring noise-free target data for training. Integrating the noise maps as an additional input channel further enables the regulation of the desired level of denoising to adjust to the preference of the radiologist.