The School of Electrical and Computer Engineering The Zlotowski Center for Neuroscience Ben-Gurion University of the Negev, Israel.
The School of Electrical and Computer Engineering The Zlotowski Center for Neuroscience Ben-Gurion University of the Negev, Israel.
Med Image Anal. 2020 Oct;65:101747. doi: 10.1016/j.media.2020.101747. Epub 2020 Jun 11.
A main challenge in magnetic resonance imaging (MRI) is speeding up scan time. Beyond improving patient experience and reducing operational costs, faster scans are essential for time-sensitive imaging, such as fetal, cardiac, or functional MRI, where temporal resolution is important and target movement is unavoidable, yet must be reduced. Current MRI acquisition methods speed up scan time at the expense of lower spatial resolution and costlier hardware. We introduce a practical, software-only framework, based on deep learning, for accelerating MRI acquisition, while maintaining anatomically meaningful imaging. This is accomplished by MRI subsampling followed by estimating the missing k-space samples via generative adversarial neural networks. A generator-discriminator interplay enables the introduction of an adversarial cost in addition to fidelity and image-quality losses used for optimizing the reconstruction. Promising reconstruction results are obtained from feasible sampling patterns of up to a fivefold acceleration of diverse brain MRIs, from a large publicly available dataset of healthy adult scans as well as multimodal acquisitions of multiple sclerosis patients and dynamic contrast-enhanced MRI (DCE-MRI) sequences of stroke and tumor patients. Clinical usability of the reconstructed MRI scans is assessed by performing either lesion or healthy tissue segmentation and comparing the results to those obtained by using the original, fully sampled images. Reconstruction quality and usability of the DCE-MRI sequences is demonstrated by calculating the pharmacokinetic (PK) parameters. The proposed MRI reconstruction approach is shown to outperform state-of-the-art methods for all datasets tested in terms of the peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), as well as either the mean squared error (MSE) with respect to the PK parameters, calculated for the fully sampled DCE-MRI sequences, or the segmentation compatibility, measured in terms of Dice scores and Hausdorff distance. The code is available on GitHub.
磁共振成像（MRI）中的一个主要挑战是加快扫描速度。除了改善患者体验和降低运营成本外，更快的扫描对于时间敏感的成像（如胎儿、心脏或功能 MRI）至关重要，在这些成像中，时间分辨率很重要，目标运动是不可避免的，但必须减少。当前的 MRI 采集方法通过牺牲较低的空间分辨率和更昂贵的硬件来加快扫描速度。我们引入了一种实用的、仅基于软件的基于深度学习的框架，用于加速 MRI 采集，同时保持具有解剖意义的成像。这是通过 MRI 欠采样并通过生成对抗神经网络估计缺失的 k 空间样本来实现的。生成器 - 鉴别器相互作用使得可以在用于优化重建的保真度和图像质量损失之外引入对抗性成本。通过对来自健康成年人扫描的大型公共可用数据集以及多发性硬化症患者的多模态采集和中风和肿瘤患者的动态对比增强 MRI（DCE-MRI）序列的各种脑 MRI 的多达五倍加速的可行采样模式，获得了有希望的重建结果。通过对病变或健康组织进行分割并将结果与使用原始、完全采样图像获得的结果进行比较，评估重建 MRI 扫描的临床可用性。通过计算药代动力学（PK）参数来证明 DCE-MRI 序列的重建质量和可用性。在所测试的所有数据集上，与最先进的方法相比，所提出的 MRI 重建方法在峰值信噪比（PSNR）、结构相似性指数（SSIM）方面表现出色，以及针对完全采样的 DCE-MRI 序列计算的 PK 参数的均方误差（MSE）或分割兼容性，以 Dice 分数和 Hausdorff 距离衡量。代码可在 GitHub 上获得。
