Dataset Information

Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net.

ABSTRACT:

Purpose

To develop an end-to-end deep learning solution for quickly reconstructing radial simultaneous multi-slice (SMS) myocardial perfusion datasets with comparable quality to the pixel tracking spatiotemporal constrained reconstruction (PT-STCR) method.

Methods

Dynamic contrast enhanced (DCE) radial SMS myocardial perfusion data were obtained from 20 subjects who were scanned at rest and/or stress with or without ECG gating using a saturation recovery radial CAIPI turboFLASH sequence. Input to the networks consisted of complex coil combined images reconstructed using the inverse Fourier transform of undersampled radial SMS k-space data. Ground truth images were reconstructed using the PT-STCR pipeline. The performance of the residual booster 3D U-Net was tested by comparing it to state-of-the-art network architectures including MoDL, CRNN-MRI, and other U-Net variants.

Results

Results demonstrate significant improvements in speed requiring approximately 8 seconds to reconstruct one radial SMS dataset which is approximately 200 times faster than the PT-STCR method. Images reconstructed with the residual booster 3D U-Net retain quality of ground truth PT-STCR images (0.963 SSIM/40.238 PSNR/0.147 NRMSE). The residual booster 3D U-Net has superior performance compared to existing network architectures in terms of image quality, temporal dynamics, and reconstruction time.

Conclusion

Residual and booster learning combined with the 3D U-Net architecture was shown to be an effective network for reconstructing high-quality images from undersampled radial SMS datasets while bypassing the reconstruction time of the PT-STCR method.

SUBMITTER: Le J

PROVIDER: S-EPMC8493758 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Publications

Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net.

Le Johnathan J Tian Ye Y Mendes Jason J Wilson Brent B Ibrahim Mark M DiBella Edward E Adluru Ganesh G

Magnetic resonance imaging 20210821

<h4>Purpose</h4>To develop an end-to-end deep learning solution for quickly reconstructing radial simultaneous multi-slice (SMS) myocardial perfusion datasets with comparable quality to the pixel tracking spatiotemporal constrained reconstruction (PT-STCR) method.<h4>Methods</h4>Dynamic contrast enhanced (DCE) radial SMS myocardial perfusion data were obtained from 20 subjects who were scanned at rest and/or stress with or without ECG gating using a saturation recovery radial CAIPI turboFLASH se ...[more]

PMID: 34428512

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Dataset Information

Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net.

Purpose

Methods

Results

Conclusion

Publications

Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net.

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