Abstract
MR Elastography is a novel technique enabling the quantification of mechanical properties in tissue with MRI. It relies on a three-step process that includes the generation of a mechanical vibration, motion capture using dedicated MR sequences, and data processing involving inversion algorithms. If not properly tuned to the targeted application, each of those steps may impact the final outcome, potentially causing diagnostic errors and thus eventually treatment mismanagement. Different approaches exist that account for acquisition or reconstruction errors, but simple tools and metrics for quality control shared by both developers and end-users are still missing. In this context, our goal is to provide an easily deployable workflow that uses generic validity criteria to assess the performance of a given MRE protocol, leveraging numerical simulations with an accessible experimental setup. Numerical simulations are used to help both determining sets of relevant acquisition parameters and assessing the data processing's robustness. Simple validity criteria were defined, and the overall pipeline was tested in a custom-built, structured phantom made of silicone-based material. The latter have the advantage of being inexpensive, easy to handle, facilitate the fabrication of complex structures which geometry resembles the anatomical structures of interest, and are longitudinally stable. In this work, we successfully tested and evaluated the overall performances of our entire MR Elastography pipeline using easy-to-implement and accessible tools that could ultimately translate in MRE standardized and cost-effective procedures.
Highlights
Magnetic Resonance Elastography (MRE) non-invasively assesses and quantifies mechanical properties of tissue in vivo [1, 2]
Since MRE relies on the encoding of the tissue displacement into the phase of the complex MR signal, metrics that take into account phase errors are good candidates to measure raw data quality
We showed that synthetic MRE data produced via numerical simulations can be used with our reconstruction pipeline
Summary
Magnetic Resonance Elastography (MRE) non-invasively assesses and quantifies mechanical properties of tissue in vivo [1, 2]. Biomechanical properties of ex vivo tissue specimens that lack perfusion are very different than those of perfused in vivo body parts, making a direct comparison between classical rheometers and MRE difficult In this context, biases and errors intrinsic to the technique should be assessed and controlled to avoid the generation (and further the interpretation) of unreliable elastograms. The ratio of local shear wavelength λ to pixel size a has been introduced as an indicator of reconstruction accuracy [17, 18], and values were reported for different methods [19, 20] From these studies, the range of optimal λ/a appears to be broad (∼5– 20), best performances are generally achieved in the range 6.7–10, especially at low SNR. As observed with synthetic data, the standard deviation within a given region decreases with increasing voxel size, and the values within the inclusions stand out of the background more clearly while inclusion size increases. As for simulations, we observed an increased error in the background
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