A scale space based algorithm for automated segmentation of single shot tagged MRI of shearing deformation

TL;DR

This paper introduces a scale space based algorithm for automated segmentation of single-shot tagged MRI images, capable of handling shearing deformations and low SNR conditions, enhancing analysis of complex tissue motions.

Contribution

The novel algorithm effectively segments shearing and broken tag patterns in single-shot tagged MRI, even with modest SNR, advancing automated image analysis techniques.

Findings

Successful segmentation at SNR above 10

Effective handling of shearing deformations

Applicable to in vivo muscle and eye movement data

Abstract

Object This study proposes a scale space based algorithm for automated segmentation of single-shot tagged images of modest SNR. Furthermore the algorithm was designed for analysis of discontinuous or shearing types of motion, i.e. segmentation of broken tag patterns. Materials and methods The proposed algorithm utilizes non-linear scale space for automatic segmentation of single-shot tagged images. The algorithm's ability to automatically segment tagged shearing motion was evaluated in a numerical simulation and in vivo. A typical shearing deformation was simulated in a Shepp-Logan phantom allowing for quantitative evaluation of the algorithm's success rate as a function of both SNR and the amount of deformation. For a qualitative in vivo evaluation tagged images showing deformations in the calf muscles and eye movement in a healthy volunteer were acquired. Results Both the numerical simulation and the in vivo tagged data demonstrated the algorithm's ability for automated segmentation of single-shot tagged MR provided that SNR of the images is above 10 and the amount of deformation does not exceed the tag spacing. The latter constraint can be met by adjusting the tag delay or the tag spacing. Conclusion The scale space based algorithm for automatic segmentation of single-shot tagged MR enables the application of tagged MR to complex (shearing) deformation and the processing of datasets with relatively low SNR.