论文标题

基于波浪编码的基于模型的深度学习,用于高度加速成像,并进行关节重建

Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction

论文作者

Cho, Jaejin, Gagoski, Borjan, Kim, Taehyung, Tian, Qiyuan, Frost, Stephen Robert, Chatnuntawech, Itthi, Bilgic, Berkin

论文摘要

目的:提出一个基于波浪编码的模型的深度学习(Wave-Modl)策略,以高度加速3D成像和联合多对比度图像重建,并进一步扩展此策略,以使用T2制备脉冲(3D-QALAS)使用交织的外观 - 锁定序列来实现快速的定量成像。 方法:最近引入的MODL技术成功地将基于卷积的神经网络(CNN)的正规化器纳入了基于物理的并行成像重建中,使用少量网络参数。 Wave-Caipi是一种新兴的并行成像方法,它通过在读数期间在相位和切片编码方向上使用正弦梯度来加速成像速度,从而更好地利用了3D线圈灵敏度曲线。在Wave-Modl中,我们建议将波浪编码策略与展开的网络约束结合起来,以加速采集速度,同时实施波浪编码的数据一致性。我们进一步扩展了波形模型,以在平行成像(CAIPI)采样模式中以受控的混叠来重建多对比度数据,以利用多个图像之间的相似性来提高重建质量。 结果:Wave-Modl以1 mm分辨率以16倍加速度以1 mm的分辨率实现了47秒的mprage采集。对于定量成像,波形允许以12倍加速度分辨率以1 mm分辨率的T1,T2和质子密度映射进行2分钟的采集,从中也可以合成对比度加权图像。 结论:波形允许快速的MR采集和高保真图像重建,并通过将展开的神经网络纳入波 - caipi重建来促进临床和神经科学应用。

Purpose: To propose a wave-encoded model-based deep learning (wave-MoDL) strategy for highly accelerated 3D imaging and joint multi-contrast image reconstruction, and further extend this to enable rapid quantitative imaging using an interleaved look-locker acquisition sequence with T2 preparation pulse (3D-QALAS). Method: Recently introduced MoDL technique successfully incorporates convolutional neural network (CNN)-based regularizers into physics-based parallel imaging reconstruction using a small number of network parameters. Wave-CAIPI is an emerging parallel imaging method that accelerates the imaging speed by employing sinusoidal gradients in the phase- and slice-encoding directions during the readout to take better advantage of 3D coil sensitivity profiles. In wave-MoDL, we propose to combine the wave-encoding strategy with unrolled network constraints to accelerate the acquisition speed while enforcing wave-encoded data consistency. We further extend wave-MoDL to reconstruct multi-contrast data with controlled aliasing in parallel imaging (CAIPI) sampling patterns to leverage similarity between multiple images to improve the reconstruction quality. Result: Wave-MoDL enables a 47-second MPRAGE acquisition at 1 mm resolution at 16-fold acceleration. For quantitative imaging, wave-MoDL permits a 2-minute acquisition for T1, T2, and proton density mapping at 1 mm resolution at 12-fold acceleration, from which contrast weighted images can be synthesized as well. Conclusion: Wave-MoDL allows rapid MR acquisition and high-fidelity image reconstruction and may facilitate clinical and neuroscientific applications by incorporating unrolled neural networks into wave-CAIPI reconstruction.

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