# Sparse Elasticity Reconstruction and Clustering using Local Displacement Fields

arXiv: https://arxiv.org/abs/1902.09328 · HTML: https://mlsanity.com/paper/1902.09328/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks](https://mlsanity.com/paper/2102.12505/) (2021)

## Abstract (arXiv)

This paper introduces an elasticity reconstruction method based on local displacement observations of elastic bodies. Sparse reconstruction theory is applied to formulate the underdetermined inverse problems of elasticity reconstruction including unobserved areas. An online local clustering scheme called a superelement is proposed to reduce the number of dimensions of the optimization parameters. Alternating the optimization of element boundaries and elasticity parameters enables the elasticity distribution to be estimated with a higher spatial resolution. The simulation experiments show that elasticity distribution is reconstructed based on observations of approximately 10% of the total body. The estimation error was improved when considering the sparseness of the elasticity distribution.