Preprint
Motion Compensated Unsupervised Deep Learning for 5D MRI
ArXiv.org
Cornell University
09/08/2023
DOI: 10.48550/arxiv.2309.04552
Abstract
We propose an unsupervised deep learning algorithm for the motion-compensated
reconstruction of 5D cardiac MRI data from 3D radial acquisitions. Ungated
free-breathing 5D MRI simplifies the scan planning, improves patient comfort,
and offers several clinical benefits over breath-held 2D exams, including
isotropic spatial resolution and the ability to reslice the data to arbitrary
views. However, the current reconstruction algorithms for 5D MRI take very long
computational time, and their outcome is greatly dependent on the uniformity of
the binning of the acquired data into different physiological phases. The
proposed algorithm is a more data-efficient alternative to current
motion-resolved reconstructions. This motion-compensated approach models the
data in each cardiac/respiratory bin as Fourier samples of the deformed version
of a 3D image template. The deformation maps are modeled by a convolutional
neural network driven by the physiological phase information. The deformation
maps and the template are then jointly estimated from the measured data. The
cardiac and respiratory phases are estimated from 1D navigators using an
auto-encoder. The proposed algorithm is validated on 5D bSSFP datasets acquired
from two subjects.
Details
- Title: Subtitle
- Motion Compensated Unsupervised Deep Learning for 5D MRI
- Creators
- Joseph KettelkampLudovica RomaninDavide PicciniSarv PriyaMathews Jacob
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2309.04552
- ISSN
- 2331-8422
- Publisher
- Cornell University
- Language
- English
- Date posted
- 09/08/2023
- Academic Unit
- Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984463082602771
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