Joint Alignment and Reconstruction of Multislice Dynamic MRI Using Variational Manifold Learning

Qing Zou; Abdul Haseeb Ahmed; Prashant Nagpal; Sarv Priya; Rolf F Schulte; Mathews Jacob

doi:10.1109/ISBI52829.2022.9761631

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Conference proceeding

Joint Alignment and Reconstruction of Multislice Dynamic MRI Using Variational Manifold Learning

Qing Zou, Abdul Haseeb Ahmed, Prashant Nagpal, Sarv Priya, Rolf F Schulte and Mathews Jacob

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp.1-4

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

2022

DOI: 10.1109/ISBI52829.2022.9761631

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Abstract

Free-breathing cardiac MRI schemes are emerging as competitive alternatives to breath-held cine MRI protocols, enabling applicability to pediatric and other population groups that cannot hold their breath. Because the data from the slices are acquired sequentially, the cardiac/respiratory motion patterns may be different for each slice; current free-breathing approaches perform independent recovery of each slice. In addition to not being able to exploit the inter-slice redundancies, manual intervention or sophisticated post-processing methods are needed to align the images post-recovery for quantification. To overcome these challenges, we propose an unsupervised variational deep manifold learning scheme for the joint alignment and reconstruction of multislice dynamic MRI. The proposed scheme jointly learns the parameters of the deep network as well as the latent vectors for each slice, which capture the motion-induced dynamic variations, from the k-t space data of the specific subject. The variational framework minimizes the non-uniqueness in the representation, thus offering improved alignment and reconstructions.

Machine Learning

Magnetic Resonance Imaging

Medical Imaging

Alignment

Breathing

Image reconstruction

Manifolds (mathematics)

Recovery

Details

Title: Subtitle: Joint Alignment and Reconstruction of Multislice Dynamic MRI Using Variational Manifold Learning
Creators: Qing Zou
Abdul Haseeb Ahmed
Prashant Nagpal
Sarv Priya
Rolf F Schulte
Mathews Jacob
Resource Type: Conference proceeding
Publication Details: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp.1-4
Conference: IEEE International Symposium on Biomedical Imaging (ISBI), 19 (Kolkata, India, 03/28/2022–03/31/2022)
DOI: 10.1109/ISBI52829.2022.9761631
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: 9984252345702771

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