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 compari

SUBMITTER: Le J 

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

REPOSITORIES: biostudies-literature