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"Deep learning-based acceleration of Compressed SENSE Cardiac MR imaging - accelerating total scan-times and reducing the number of breath holds"
Huangling Lu, Joe F. Juffermans, Nicola Pezzotti, Marius Staring and Hildo J. Lamb
Abstract
With compressed sensing (CS) undersampled data points are used for MR image reconstruction to reduce acquisition times with preservation of SNR, but CS tends to simplify image content with higher levels of acceleration. Deep learning (DL) reconstruction methods could accelerate the acquisition process with preservation of high image quality by learning from high complexity images. In cardiac MR imaging high levels of acceleration allows multi-slice imaging during one breath-hold (BH) which could reduce scan-times significantly. We investigate the feasibility of a prospectively assessed DL-based reconstruction technique combined with different levels of acceleration using Compressed Sensing artificial intelligence framework in cardiac MR imaging.
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Copyright © 2023 by the authors.
Published version © 2023 by .
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @article{Lu:2023, |
| author |
= {Lu, Huangling and Juffermans, Joe F. and Pezzotti, Nicola and Staring, Marius and Lamb, Hildo J.}, |
| title |
= {Deep learning-based acceleration of Compressed SENSE Cardiac MR imaging - accelerating total scan-times and reducing the number of breath holds}, |
| journal |
= {Society for Cardiovascular Magnetic Resonance}, |
| month |
= {January}, |
| year |
= {2023}, |
| } |
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| last modified: 10-02-2023 |
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