Cardiac-induced liver deformation as a measure of liver stiffness using dynamic imaging without magnetization tagging-preclinical proof-of-concept, clinical translation, reproducibility and feasibility in patients with cirrhosis

Cine MR feature tracking analysis for diagnosing thymic epithelial tumors: a feasibility study

BACKGROUND

To assess the feasibility of the cine MR feature tracking technique for the evaluation of cardiovascular-induced morphological deformation in the diagnosis of thymic epithelial tumors (TETs).

METHODS

Our study population consisted of 43 patients with pathologically proven TETs including 10 low-grade thymomas, 23 high-grade thymomas, and 10 thymic carcinomas. Cine MR images were acquired using a balanced steady-state free precession sequence with short periods of breath-hold in the axial and oblique planes in the slice with the largest lesion cross-sectional area. The tumor margin was manually delineated in the diastolic phase and was automatically tracked for all other cardiac phases. The change rates of the long-to-short diameter ratio (∆LSR) and tumor area (∆area) associated with pulsation were compared between the three pathological groups using the Kruskal-Wallis H test and the Mann-Whitney U test. A receiver-operating characteristic (ROC) curve analysis was performed to assess the ability of each parameter to differentiate thymic carcinomas from thymomas.

RESULTS

∆LSR and ∆area were significantly different among the three groups in the axial plane (p = 0.028 and 0.006, respectively) and in the oblique plane (p = 0.034 and 0.043, respectively). ∆LSR and ∆area values were significantly lower in thymic carcinomas than in thymomas in the axial plane (for both, p = 0.012) and in the oblique plane (p = 0.015 and 0.011, respectively). The area under the ROC curves for ∆LSR and ∆area for the diagnosis of thymic carcinoma ranged from 0.755 to 0.764.

CONCLUSIONS

Evaluation of morphological deformation using cine-MR feature tracking analysis can help diagnose histopathological subtypes of TETs and identify thymic carcinomas preoperatively.

Ex vivo bovine liver nonlinear viscoelastic properties: MR elastography and rheological measurements

INTRODUCTION

Knowledge of the nonlinear viscoelastic properties of the liver is important, but the complex tissue behavior outside the linear viscoelastic regime has impeded their characterization, particularly in vivo. Combining static compression with magnetic resonance (MR) elastography has the potential to be a useful imaging method for assessing large deformation mechanical properties of soft tissues in vivo. However, this remains to be verified. Therefore this study aims first to determine whether MR elastography can measure the nonlinear mechanical properties of ex vivo bovine liver tissue under varying levels of uniform and focal preloads (up to 30%), and second to compare MR elastography-derived complex shear modulus with standard rheological measurements.

METHOD

Nine fresh bovine livers were collected from a local abattoir, and experiments were conducted within 12hr of death. Two cubic samples (∼10 × 10 × 10 cm³) were dissected from each liver and imaged using MR elastography (60 Hz) under 4 levels of uniform and focal preload (1, 10, 20, and 30% of sample width) to investigate the relationship between MR elastography-derived complex shear modulus (G∗) and the maximum principal Right Cauchy Green Strain (C₁₁). Three tissue samples from each of the same 9 livers underwent oscillatory rheometry under the same 4 preloads (1, 10, 20, and 30% strain). MR elastography-derived complex shear modulus (G∗) from the uniform preload was validated against rheometry by fitting the frequency dependence of G∗ with a power-law and extrapolating rheometry-derived G∗ to 60 Hz.

RESULTS

MR elastography-derived G∗ increased with increasing compressive large deformation strain, and followed a power-law curve (G∗ = 1.73 × C₁₁^-0.38, R² = 0.96). Similarly, rheometry-derived G∗ at 1 Hz, increasing from 0.66 ± 1.03 kPa (1% strain) to 1.84 ± 1.65 kPa (30% strain, RM one-way ANOVA, P < 0.001), and the frequency dependence of G∗ followed a power-law with the exponent decreasing from 0.13 to 0.06 with increasing preload. MR elastography-derived G∗ was 1.4-3.1 times higher than the extrapolated rheometry-derived G∗ at 60 Hz, but the strain dependence was consistent between rheometry and MR elastography measurements.

CONCLUSIONS

This study demonstrates that MR elastography can detect changes in ex vivo bovine liver complex shear modulus due to either uniform or focal preload and therefore can be a useful technique to characterize nonlinear viscoelastic properties of soft tissue, provided that strains applied to the tissue can be quantified. Although MR elastography could reliably characterize the strain dependence of the ex vivo bovine liver, MR elastography overestimated the complex shear modulus of the tissue compared to rheological measurements, particularly at lower preload (<10%). That is likely to be important in clinical hepatic MR elastography diagnosis studies if preload is not carefully considered. A limitation is the absence of overlapping frequency between rheometry and MR elastography for formal validation.

Native-resolution myocardial principal Eulerian strain mapping using convolutional neural networks and Tagged Magnetic Resonance Imaging

BACKGROUND

Assessment of regional myocardial function at native pixel-level resolution can play a crucial role in recognizing the early signs of the decline in regional myocardial function. Extensive data processing in existing techniques limits the effective resolution and accuracy of the generated strain maps. The purpose of this study is to compute myocardial principal strain maps ε_p1 and ε_p2 from tagged MRI (tMRI) at the native image resolution using deep-learning local patch convolutional neural network (CNN) models (DeepStrain).

METHODS

For network training, validation, and testing, realistic tMRI datasets were generated and consisted of 53,606 cine images simulating the heart, the liver, blood pool, and backgrounds, including ranges of shapes, positions, motion patterns, noise, and strain. In addition, 102 in-vivo image datasets from three healthy subjects, and three Pulmonary Arterial Hypertension patients, were acquired and used to assess the network's in-vivo performance. Four convolutional neural networks were trained for mapping input tagging patterns to corresponding ground-truth principal strains using different cost functions. Strain maps using harmonic phase analysis (HARP) were obtained with various spectral filtering settings for comparison. CNN and HARP strain maps were compared at the pixel level versus the ground-truth and versus the least-loss in-vivo maps using Pearson correlation coefficients (R) and the median error and Inter-Quartile Range (IQR) histograms.

RESULTS

CNN-based local patch DeepStrain maps at a phantom resolution of 1.1mm × 1.1 mm and in-vivo resolution of 2.1mm × 1.6 mm were artifact-free with multiple fold improvement with ε_p1 ground-truth median error of 0.009(0.007) vs. 0.32(0.385) using HARP and ε_p2 ground-truth error of 0.016(0.021) vs. 0.181(0.08) using HARP. CNN-based strain maps showed substantially higher agreement with the ground-truth maps with correlation coefficients R > 0.91 for ε_p1 and ε_p2 compared to R < 0.21 and R < 0.82 for HARP-generated maps, respectively.

CONCLUSION

CNN-generated Eulerian strain mapping permits artifact-free visualization of myocardial function at the native image resolution.