Preprint
Joint cardiac $T_1$ mapping and cardiac function estimation using a deep manifold framework
ArXiv.org
05/16/2022
DOI: 10.48550/arXiv.2205.07994
Abstract
In this work, we proposed a continuous-acquisition strategy using a gradient
echo (GRE) inversion recovery sequence based on spiral trajectories to
simultaneously obtain the $T_1$ mapping and CINE imaging. The acquisition is
using a free-breathing and ungated fashion. An approach based on variational
auto-encoder(VAE) is used for the motion estimation from the centered k-space
data. The motion signal is then used to train a deep manifold reconstruction
algorithm for image reconstruction. Once the network is trained, we can excite
the latent vectors (the estimated motion signals and the contrast signal) in
any way as we wanted to generate the image frames in the time series. We can
estimate the $T_1$ mapping using the generated image frames where only contrast
is varying. We can also generate the breath-hold CINE in different contrast.
Details
- Title: Subtitle
- Joint cardiac $T_1$ mapping and cardiac function estimation using a deep manifold framework
- Creators
- Qing ZouMathews Jacob
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arXiv.2205.07994
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 05/16/2022
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Technology Institute; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984258753002771
Metrics
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