Dynamic Imaging Using Motion-Compensated Smoothness Regularization on Manifolds (MOCO-STORM)

Qing Zou; Luis Torres; Sean Fain; Mathews Jacob

doi:10.1109/ISBI52829.2022.9761440

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Conference proceeding

Dynamic Imaging Using Motion-Compensated Smoothness Regularization on Manifolds (MOCO-STORM)

Qing Zou, Luis Torres, Sean Fain and Mathews Jacob

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)

IEEE International Symposium on Biomedical Imaging (ISBI), 19th (Kolkata, India, 03/28/2022–03/31/2022)

2022

DOI: 10.1109/ISBI52829.2022.9761440

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Abstract

We introduce an unsupervised deep manifold learning algorithm for motion-compensated dynamic MRI. We assume that the motion fields in a free-breathing lung MRI dataset live on a manifold. The motion field at each time instant is modeled as the output of a deep generative model, driven by low-dimensional time-varying latent vectors that capture the temporal variability. The images at each time instant are modeled as the deformed version of an image template using the above motion fields. The template, the parameters of the deep generator, and the latent vectors are learned from the k-t space data in an unsupervised fashion. The manifold motion model serves as a regularizer, making the joint estimation of the motion fields and images from few radial spokes/frame well-posed. The utility of the algorithm is demonstrated in the context of motion-compensated high-resolution lung MRI.

Algorithms

Machine Learning

Magnetic Resonance Imaging

Medical Imaging

Lungs

Manifolds (mathematics)

Regularization

Smoothness

Spokes

Details

Title: Subtitle: Dynamic Imaging Using Motion-Compensated Smoothness Regularization on Manifolds (MOCO-STORM)
Creators: Qing Zou - University of Iowa, Iowa Technology Institute
Luis Torres
Sean Fain
Mathews Jacob
Resource Type: Conference proceeding
Publication Details: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
Conference: IEEE International Symposium on Biomedical Imaging (ISBI), 19th (Kolkata, India, 03/28/2022–03/31/2022)
DOI: 10.1109/ISBI52829.2022.9761440
Publisher: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Language: English
Date published: 2022
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Technology Institute; Iowa Neuroscience Institute; Radiation Oncology
Record Identifier: 9984251625802771

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