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Liquid-liver phantom: mimicking the viscoelastic dispersion of human liver for elastography in ultrasound and MRI

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 Added by Anna Morr
 Publication date 2021
  fields Physics
and research's language is English




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Different clinical elastography devices show different liver-stiffness values in the same subject, hindering comparison of values and establishment of system-independent thresholds for disease detection. Therefore, authorities request standardized phantoms that address the viscosity-related dispersion of stiffness over frequency. A linear polymerized polyacrylamide phantom (PAAm) was calibrated to the viscoelastic properties of healthy human liver in vivo. Shear-wave speed as a surrogate of stiffness was quantified between 5 Hz and 3000 Hz frequency-range by shear rheometry, ultrasound-based time-harmonic elastography, clinical MR elastography (MRE), and tabletop MRE. Imaging parameters for ultrasound were close to those of liver in vivo. Reproducibility, aging behavior and temperature dependency were assessed and fulfilled requirements for quantitative elastography. In addition, the phantom was used to characterize the frequency bandwidth of shear-wave speed of several clinical elastography methods. The liquid-liver phantom has favorable properties for standardization and development of liver elastography: first, it can be used across clinical and experimental elastography devices in ultrasound and MRI. Second, being a liquid, it can easily be adapted in size and shape to specific technical requirements, and by adding inclusions and scatterers. Finally, since the phantom is based on non-crosslinked linear PAA constituents, it is easy to produce, indicating potential widespread use among researchers and vendors to standardize liver-stiffness measurements.



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In the framework of algebraic inversion, Magnetic Resonance Elastography (MRE) repeatability, reproducibility and robustness were evaluated on extracted shear velocities (or elastic moduli). The same excitation system was implemented at two sites equipped with clinical MR scanners of 1.5 T and 3 T. A set of four elastic, isotropic, homogeneous calibrated phantoms of distinct elasticity representing the spectrum of liver fibrosis severity was mechanically characterized. The repeatability of the measurements and the reproducibility between the two platforms were found to be excellent with mean coefficients of variations of 1.62% for the shear velocity mean values and 1.95% for the associated standard deviations. MRE velocities were robust to the amplitude and pattern variations of the displacement field with virtually no difference between outcomes from both magnets at identical excitation frequencies even when the displacement field amplitude was 6 times smaller. However, MRE outcomes were very sensitive to the number of voxels per wavelength, s, of the recorded displacement field, with relative biases reaching 62% and precision losing up to a factor 23.5. For both magnetic field strengths, MRE accuracy and precision were largely degraded outside of established conditions of validity ($6 lesssim s lesssim 9$) resulting in estimated shear velocity values not significantly different between phantoms of increasing elasticity. When fulfilling the spatial sampling conditions, either prospectively in the acquisition or retrospectively before the reconstruction, MRE produced quantitative measurements that allowed to unambiguously discriminate, with infinitesimal p-values, between the phantoms mimicking increasing severity of liver fibrosis.
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Segmentation of medical images is a challenging task owing to their complexity. A standard segmentation problem within Magnetic Resonance Imaging (MRI) is the task of labeling voxels according to their tissue type. Image segmentation provides volumetric quantification of liver area and thus helps in the diagnosis of disorders, such as Hepatitis, Cirrhosis, Jaundice, Hemochromatosis etc.This work deals with comparison of segmentation by applying Level Set Method,Fuzzy Level Information C-Means Clustering Algorithm and Gradient Vector Flow Snake Algorithm.The results are compared using the parameters such as Number of pixels correctly classified, and percentage of area segmented.
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