ABSTRACT
Ventral incisional hernia repair is one of the most common surgical procedures. The characterization of the abdominal wall layer mechanical properties is the first step towards personalized treatment. This study investigates the capability of elastography to assess these properties using anin vivoandin vitromodel of abdominal wall layers. Two experiment approaches are considered: shear wave elastography imaging and guided wave dispersion characterization, where the latter is used as a reference. Results show measurement biases in the shear wave elastography approach in such a layer structure configuration. Methods to overcome these biases are suggested to improve and to correct the elastography approach for abdominal wall layers and similar anatomical structures.
Subject(s)
Abdominal Wall , Elasticity Imaging Techniques , Abdominal Wall/diagnostic imaging , Bias , Elasticity Imaging Techniques/methods , Phantoms, ImagingABSTRACT
In most elastography experiments, shear waves are generated using a single source on the surface with a shaker, or in the bulk with radiation pressure of ultrasound. However, emitting controlled shear waves from multiple sources is a good way to improve the signal to-noise-ratio for shear-wave elastography. The experiments are conducted using six shakers with independent driving electronics in gelatin-graphite to mimic the tissue. Based on time reversal, our approach shows the feasibility of controlling shear-wave field in space with multiple focal spots at chosen locations, and in time with a chosen delay between each focusing. Improved by 10 dB compared to the use of a single source, the signal-to-noise ratio demonstrates that time-reversal as an adaptive filter is a good method to deliver maximum energy vibrations toward deep regions. Furthermore, this adaptive approach allows controlled vibrations to be delivered through bone conduction: a shear-wave focal spot is experimentally observed in a soft brain tissue-mimicking phantom using the multiple sources array applied to a skull model.