Mani Merry P, Aggarwal Hemant K, Ghosh Sanjay, Jacob Mathews
University of Iowa, Iowa, USA.
Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:913-916. doi: 10.1109/isbi45749.2020.9098593. Epub 2020 May 22.
We propose a model-based deep learning architecture for the reconstruction of highly accelerated diffusion magnetic resonance imaging (MRI) that enables high resolution imaging. The proposed reconstruction jointly recovers all the diffusion weighted images in a single step from a joint k-q under-sampled acquisition in a parallel MRI setting. We propose the novel use of a pre-trained denoiser as a regularizer in a model-based reconstruction for the recovery of highly under-sampled data. Specifically, we designed the denoiser based on a general diffusion MRI tissue microstructure model for multi-compartmental modeling. By using a wide range of biologically plausible parameter values for the multi-compartmental microstructure model, we simulated diffusion signal that spans the entire microstructure parameter space. A neural network was trained in an unsupervised manner using an autoencoder to learn the diffusion MRI signal subspace. We employed the autoencoder in a model-based reconstruction and show that the autoencoder provides a strong denoising prior to recover the q-space signal. We show reconstruction results on a simulated brain dataset that shows high acceleration capabilities of the proposed method.
我们提出了一种基于模型的深度学习架构，用于重建高加速扩散磁共振成像（MRI），以实现高分辨率成像。所提出的重建方法在并行MRI设置中，通过联合k-q欠采样采集，在单个步骤中共同恢复所有扩散加权图像。我们提出在基于模型的重建中，将预训练的去噪器作为正则化器用于恢复高度欠采样的数据。具体而言，我们基于用于多室建模的通用扩散MRI组织微观结构模型设计了去噪器。通过为多室微观结构模型使用广泛的生物学上合理的参数值，我们模拟了跨越整个微观结构参数空间的扩散信号。使用自动编码器以无监督方式训练神经网络，以学习扩散MRI信号子空间。我们在基于模型的重建中使用自动编码器，并表明自动编码器在恢复q空间信号之前提供了强大的去噪能力。我们在模拟脑数据集上展示了重建结果，表明了所提出方法的高加速能力。
