@inproceedings{b543e00312e843c78fc0a13d2ca50631,
title = "Incompressible phase registration for motion estimation from tagged magnetic resonance images",
abstract = "Tagged magnetic resonance imaging has been used for decades to observe and quantify motion and strain of deforming tissue. Three-dimensional (3D) motion estimation has been challenging due to a tradeoff between slice density and acquisition time. Typically, sparse collections of tagged slices are processed to obtain two-dimensional motion, which are then combined into 3D motion using interpolation methods. This paper proposes a new method by reversing this process: first interpolating tagged slices and then directly estimating motion in 3D. We propose a novel image registration framework that uses the concept of diffeomorphic registration with a key novelty that defines a similarity metric involving the simultaneous use of three harmonic phase volumes. The other novel aspect is the use of the harmonic magnitude to enforce incompressibility in the tissue region. The final motion estimates are dense, incompressible, diffeomorphic, and invertible at a 3D voxel level. The approach was evaluated using simulated phantoms and human tongue motion data in speech. Compared with an existing method, it shows major advantages in reducing processing complexity, improving computation speed, allowing running motion calculations, and increasing noise robustness, while maintaining a good accuracy.",
keywords = "Incompressible, Motion, Phase, Registration, Tagged MRI",
author = "Fangxu Xing and Jonghye Woo and Gomez, {Arnold D.} and Pham, {Dzung L.} and Bayly, {Philip V.} and Maureen Stone and Prince, {Jerry L.}",
note = "Funding Information: This work was supported by Grants NIH/NIDCD 1R01DC014717, NIH/NINDS 4R01NS055951, and NIH/NIDCD R00DC012575. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 1st International Workshops on Reconstruction and Analysis of Moving Body Organs, RAMBO 2016 and 1st International Workshops on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease, HVSMR 2016 Held in Conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 ; Conference date: 17-10-2016 Through 21-10-2016",
year = "2017",
doi = "10.1007/978-3-319-52280-7_3",
language = "English",
isbn = "9783319522791",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "24--33",
editor = "Zuluaga, {Maria A.} and Moghari, {Mehdi H.} and Pace, {Danielle F.} and Bernhard Kainz and Kanwal Bhatia",
booktitle = "Reconstruction, Segmentation, and Analysis of Medical Images - 1st International Workshops, RAMBO 2016 and HVSMR 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers",
}