Measuring strain in diffusion-weighted data using tagged magnetic resonance imaging

Fangxu Xing; Xiaofeng Liu; Timothy G. Reese; Maureen Stone; Van J. Wedeen; Jerry L. Prince; Georges El Fakhri; Jonghye Woo

doi:10.1117/12.2610989

4 April 2022 Measuring strain in diffusion-weighted data using tagged magnetic resonance imaging

Fangxu Xing, Xiaofeng Liu, Timothy G. Reese, Maureen Stone, Van J. Wedeen, Jerry L. Prince, Georges El Fakhri, Jonghye Woo

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203205 (2022) https://doi.org/10.1117/12.2610989
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Accurate measurement of strain in a deforming organ has been an important step in motion analysis using medical images. In recent years, internal tissue’s in vivo motion and strain computation is mostly achieved through dynamic magnetic resonance (MR) imaging. However, such data lack information on tissue’s intrinsic fiber directions, preventing computed strain tensors from being projected onto a direction of interest. Although diffusion-weighted MR imaging excels at providing fiber tractography, it yields static images unmatched with dynamic MR data. In this work, we report an algorithm workflow that estimates strain values in the diffusion MR space by matching corresponding tagged dynamic MR images. We focus on processing a dataset of various human tongue deformations in speech. The geometry of tongue muscle fibers is provided by diffusion tractography, while spatiotemporal motion fields are provided by tagged MR analysis. The tongue’s deforming shapes are determined by segmenting a synthetic cine dynamic MR sequence generated from tagged data using a deep neural network. Estimated motion fields are transformed into the diffusion MR space using diffeomorphic registration, eventually leading to strain values computed in the direction of muscle fibers. The method was tested on 78 time volumes acquired during three sets of specific tongue deformations including both speech and protrusion motion. Strain in the line of action of seven internal tongue muscles was extracted and compared both intra- and inter-subject. Resulting compression and stretching patterns of individual muscles revealed unique behavior of individual muscles and their potential activation pattern.

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Citation Download Citation

Fangxu Xing, Xiaofeng Liu, Timothy G. Reese, Maureen Stone, Van J. Wedeen, Jerry L. Prince, Georges El Fakhri, and Jonghye Woo "Measuring strain in diffusion-weighted data using tagged magnetic resonance imaging", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203205 (4 April 2022); https://doi.org/10.1117/12.2610989

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