Real-time deep artifact suppression using recurrent U-Nets for low-latency cardiac MRI.
Real-time deep artifact suppression using recurrent U-Nets for low-latency cardiac MRI.
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DOI：
10.1002/mrm.28834
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发表时间：
2021-10
影响因子：
3.3
通讯作者：
Muthurangu V
中科院分区：
文献类型：
--
作者：
Jaubert O;Montalt-Tordera J;Knight D;Coghlan GJ;Arridge S;Steeden JA;Muthurangu V
执行真实的实时低延迟MRI以指导各种心脏介入。真实的实时采集通常需要迭代图像重建策略，这导致重建时间较长。在这项研究中，我们的目标是使用深度学习以低延迟重建高度欠采样的径向真实的时间数据。提出了一种具有卷积长短期记忆层的2D U‐Net，以利用空间和先前的时间信息来重建具有低延迟的高度加速的微小黄金径向数据。使用屏气CINE数据集（包括来自7个不同方向的770个时间序列）训练网络。通过回顾性欠采样幅度图像来创建合成配对数据，并训练网络以恢复目标图像。本着介入成像的精神，对网络进行了训练，并针对不同的加速度和方向进行了测试。在1名健康受试者和3名接受导管插入术的患者中，前瞻性采集数据并进行真实的实时重建。将图像与滑动窗口和压缩感知重建以及传统笛卡尔真实的时间序列进行视觉比较。对于模拟数据中所有考虑的指标，所提出的网络可以很好地推广到不同的加速度和看不见的方向（与类似的加速度和特定方向的网络相比，结构相似性指数降低不到4%）。所提出的重建进行了交互式演示，成功地描绘导管在体内低延迟（39 ms，包括19 ms的深伪影抑制）和图像质量相比，优于其他重建。在非笛卡尔真实的时间介入心脏MR的时间关键应用中成功证明了深度伪影抑制。
Real‐time low latency MRI is performed to guide various cardiac interventions. Real‐time acquisitions often require iterative image reconstruction strategies, which lead to long reconstruction times. In this study, we aim to reconstruct highly undersampled radial real‐time data with low latency using deep learning. A 2D U‐Net with convolutional long short‐term memory layers is proposed to exploit spatial and preceding temporal information to reconstruct highly accelerated tiny golden radial data with low latency. The network was trained using a dataset of breath‐hold CINE data (including 770 time series from 7 different orientations). Synthetic paired data were created by retrospectively undersampling the magnitude images, and the network was trained to recover the target images. In the spirit of interventional imaging, the network was trained and tested for varying acceleration rates and orientations. Data were prospectively acquired and reconstructed in real time in 1 healthy subject interactively and in 3 patients who underwent catheterization. Images were visually compared to sliding window and compressed sensing reconstructions and a conventional Cartesian real‐time sequence. The proposed network generalized well to different acceleration rates and unseen orientations for all considered metrics in simulated data (less than 4% reduction in structural similarity index compared to similar acceleration and orientation‐specific networks). The proposed reconstruction was demonstrated interactively, successfully depicting catheters in vivo with low latency (39 ms, including 19 ms for deep artifact suppression) and an image quality comparing favorably to other reconstructions. Deep artifact suppression was successfully demonstrated in the time‐critical application of non‐Cartesian real‐time interventional cardiac MR.
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影响因子：
3.3
作者：
Uecker, Martin;Lai, Peng;Murphy, Mark J.;Virtue, Patrick;Elad, Michael;Pauly, John M.;Vasanawala, Shreyas S.;Lustig, Michael
通讯作者：
Lustig, Michael
DOI：
10.1002/jmri.25749
发表时间：
2017-10
期刊：
Journal of magnetic resonance imaging : JMRI
影响因子：
--
作者：
Campbell-Washburn AE;Tavallaei MA;Pop M;Grant EK;Chubb H;Rhode K;Wright GA
通讯作者：
Wright GA
影响因子：
6.7
作者：
Frahm, Jens;Voit, Dirk;Uecker, Martin
通讯作者：
Uecker, Martin
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
影响因子：
10.6
作者：
Schlemper, Jo;Caballero, Jose;Rueckert, Daniel
通讯作者：
Rueckert, Daniel