Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction.
Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction.
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DOI：
10.1002/mrm.28420
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发表时间：
2021-01
影响因子：
3.3
通讯作者：
Cheng JY
中科院分区：
文献类型：
--
作者：
Sandino CM;Lai P;Vasanawala SS;Cheng JY
提出一种新型的并行成像和深度学习的重建框架，以稳健地重建高加速的2D心脏Cine MRI数据。
我们提出了DL-Espirit是一种展开的神经网络体系结构，它利用扩展的线圈灵敏度模型来解决与感官相关的视野（FOV）限制，以在先前提出的基于深度学习的重建框架中。此外，我们提出了一种基于（2+1）D时空卷积的新型神经网络设计，以产生比常规3D卷积更准确的动态MRI重建。该网络接受了经过IRB批准的11位健康志愿者收集的完全采样的2D心脏Cine数据集培训。将DL振荡与最新的平行成像和称为L1-埃斯皮特的压缩传感方法进行比较。两种方法的重建精度均在回顾性的无效数据集（r = 12）上，相对于标准图像质量指标以及左心室体积的自动深度学习分割。 DL振荡的可行性在每个切片中单个心跳中获得的两个前瞻性底漆数据集证明了。
与标准图像质量指标相比，（p <0.001）相比，（2+1）D DL-Espirit方法与L1-埃斯Pirit重建相比，产生更高的保真度图像重建（P <0.001）。由于图像质量的提高，由（2+1）d dl-Espirit图像制成的分割也比从L1振荡图像中的分割更准确。
DL-Espirit协同结合了强大的平行成像模型和基于深度学习的先验，以产生回顾性底面2D心脏CINE数据以减少FOV获取的高保真重建。尽管显示了概念验证，但需要进一步的实验来确定DL-振荡在前瞻性不足的数据中的功效。
To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data. 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 eleven healthy volunteers with IRB approval. DL-ESPIRiT is compared against a state-of-the-art parallel imaging and compressed sensing method known as l1-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. The (2+1)D DL-ESPIRiT method produces higher fidelity image reconstructions when compared to l1-ESPIRiT reconstructions with respect to standard image quality metrics (P <0.001). As a result of improved image quality, segmentations made from (2+1)D DL-ESPIRiT images are also more accurate than segmentations from l1-ESPIRiT images. 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.
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影响因子：
3.3
作者：
Feng, Li;Srichai, Monvadi B.;Lim, Ruth P.;Harrison, Alexis;King, Wilson;Adluru, Ganesh;Dibella, Edward V. R.;Sodickson, Daniel K.;Otazo, Ricardo;Kim, Daniel
通讯作者：
Kim, Daniel
影响因子：
3.3
作者：
Hammernik K;Klatzer T;Kobler E;Recht MP;Sodickson DK;Pock T;Knoll F
通讯作者：
Knoll F
DOI：
10.1097/01.rmr.0000136558.09801.dd
发表时间：
2004-08-01
期刊：
Topics in magnetic resonance imaging : TMRI
影响因子：
--
作者：
Blaimer, Martin;Breuer, Felix;Jakob, Peter M
通讯作者：
Jakob, Peter M
影响因子：
3.3
作者：
Griswold, MA;Jakob, PM;Haase, A
通讯作者：
Haase, A
影响因子：
3.3
作者：
Breuer, FA;Kellman, P;Jakob, PM
通讯作者：
Jakob, PM