Time-Resolved Reconstruction of Motion, Force, and Stiffness Using Spectro-Dynamic MRI

Measuring the dynamics and mechanical properties of muscles and joints is important to understand the (patho)physiology of muscles. However, acquiring dynamic time-resolved MRI data is challenging. We have previously developed Spectro-Dynamic MRI which allows the characterization of dynamical system...

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Bibliographic Details
Published in:IEEE transactions on computational imaging 2023, Vol.9, p.917-927
Main Authors: van Riel, Max H. C., van Leeuwen, Tristan, Berg, Cornelis A. T. van den, Sbrizzi, Alessandro
Format: Article
Language:English
Subjects:
Data acquisition
Data models
Dynamic mechanical properties
Dynamic models
Dynamical systems
Dynamics
Elastic systems
Image reconstruction
Imaging
Iterative algorithms
Iterative methods
Magnetic resonance imaging
Mathematical models
Mechanical properties
Motion measurement
Motion simulation
Muscles
real-time imaging
Regularization
spectro-dynamic MRI
Temporal resolution
time-resolved imaging
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Summary:Measuring the dynamics and mechanical properties of muscles and joints is important to understand the (patho)physiology of muscles. However, acquiring dynamic time-resolved MRI data is challenging. We have previously developed Spectro-Dynamic MRI which allows the characterization of dynamical systems at a high spatial and temporal resolution directly from k-space data. This work presents an extended Spectro-Dynamic MRI framework that reconstructs 1) time-resolved MR images, 2) time-resolved motion fields, 3) dynamical parameters, and 4) an activation force, at a temporal resolution of 11 ms. An iterative algorithm solves a minimization problem containing four terms: a motion model relating the motion to the fully-sampled k-space data, a dynamical model describing the expected type of dynamics, a data consistency term describing the undersampling pattern, and finally a regularization term for the activation force. We acquired MRI data using a dynamic motion phantom programmed to move like an actively driven linear elastic system, from which all dynamic variables could be accurately reconstructed, regardless of the sampling pattern. The proposed method performed better than a two-step approach, where time-resolved images were first reconstructed from the undersampled data without any information about the motion, followed by a motion estimation step.
ISSN:2573-0436
2333-9403
DOI:10.1109/TCI.2023.3324757