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Accelerating cardiac cine MRI using a deep learning‐based ESPIRiT reconstruction
Author(s) -
Sandino Christopher M.,
Lai Peng,
Vasanawala Shreyas S.,
Cheng Joseph Y.
Publication year - 2021
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28420
Subject(s) - deep learning , artificial intelligence , computer science , image quality , iterative reconstruction , heartbeat , artificial neural network , convolutional neural network , compressed sensing , pattern recognition (psychology) , cardiac imaging , sensitivity (control systems) , computer vision , image (mathematics) , radiology , medicine , computer security , electronic engineering , engineering
Purpose To propose a novel combined parallel imaging and deep learning‐based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data. Methods We propose DL‐ESPIRiT, an unrolled neural network architecture that utilizes an extended coil sensitivity model to address SENSE‐related field‐of‐view (FOV) limitations in previously proposed deep learning‐based reconstruction frameworks. Additionally, we propose a novel neural network design based on (2+1)D spatiotemporal convolutions to produce more accurate dynamic MRI reconstructions than conventional 3D convolutions. The network is trained on fully sampled 2D cardiac cine datasets collected from 11 healthy volunteers with IRB approval. DL‐ESPIRiT is compared against a state‐of‐the‐art parallel imaging and compressed sensing method known as l 1 ‐ESPIRiT. The reconstruction accuracy of both methods is evaluated on retrospectively undersampled datasets ( R = 12) with respect to standard image quality metrics as well as automatic deep learning‐based segmentations of left ventricular volumes. Feasibility of DL‐ESPIRiT is demonstrated on two prospectively undersampled datasets acquired in a single heartbeat per slice. Results The (2+1)D DL‐ESPIRiT method produces higher fidelity image reconstructions when compared to l 1 ‐ESPIRiT reconstructions with respect to standard image quality metrics ( P < .001). As a result of improved image quality, segmentations made from (2+1)D DL‐ESPIRiT images are also more accurate than segmentations from l 1 ‐ESPIRiT images. Conclusions DL‐ESPIRiT synergistically combines a robust parallel imaging model and deep learning‐based priors to produce high‐fidelity reconstructions of retrospectively undersampled 2D cardiac cine data acquired with reduced FOV. Although a proof‐of‐concept is shown, further experiments are necessary to determine the efficacy of DL‐ESPIRiT in prospectively undersampled data.