Improving Amide Proton Transfer-Weighted MRI Reconstruction Using T2-Weighted Images.
Improving Amide Proton Transfer-Weighted MRI Reconstruction Using T2-Weighted Images.
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DOI：
10.1007/978-3-030-59713-9_1
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发表时间：
2020-10
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影响因子：
--
通讯作者：
Patel VM
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文献类型：
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作者：
Wang P;Guo P;Lu J;Zhou J;Jiang S;Patel VM
目前的酰胺质子转移加权(APTw)成像方案通常从获取高分辨率T2加权(T2w)图像开始，然后在由获取的T2w图像确定的特定几何形状和位置(即切片)处进行APTw成像。虽然已经提出了许多先进的MRI重建方法来加速MRI，但现有的APTw MRI方法缺乏利用获取的T2w图像中的结构信息进行重建的能力。在本文中，我们提出了一种新的APTw图像重建框架，该框架通过直接从高度欠采样的k空间数据和对应的同一位置的T2w图像重建APTw图像来加速APTw成像。该框架首先提出了一种新的基于稀疏表示的切片匹配算法，该算法的目标是找到匹配的T2w切片，该算法只给出欠采样的APTw图像。设计了一种递归特征共享重建网络(RFS-Rec)，利用卷积递归神经网络(CRNN)从匹配的T2w图像中提取中间特征，从而将缺失的结构信息融入到欠采样的APT原始图像中，从而有效地改善了重建APTw图像的图像质量。我们在两个真实的数据集上对所提出的方法进行了评估，该数据集包括来自大鼠和人类的大脑数据。大量实验表明，本文提出的RFS-Rec方法比现有的方法具有更好的性能。
Current protocol of Amide Proton Transfer-weighted (APTw) imaging commonly starts with the acquisition of high-resolution T2-weighted (T2w) images followed by APTw imaging at particular geometry and locations (i.e. slice) determined by the acquired T2w images. Although many advanced MRI reconstruction methods have been proposed to accelerate MRI, existing methods for APTw MRI lacks the capability of taking advantage of structural information in the acquired T2w images for reconstruction. In this paper, we present a novel APTw image reconstruction framework that can accelerate APTw imaging by reconstructing APTw images directly from highly undersampled k-space data and corresponding T2w image at the same location. The proposed framework starts with a novel sparse representation-based slice matching algorithm that aims to find the matched T2w slice given only the undersampled APTw image. A Recurrent Feature Sharing Reconstruction network (RFS-Rec) is designed to utilize intermediate features extracted from the matched T2w image by a Convolutional Recurrent Neural Network (CRNN), so that the missing structural information can be incorporated into the undersampled APT raw image thus effectively improving the image quality of the reconstructed APTw image. We evaluate the proposed method on two real datasets consisting of brain data from rats and humans. Extensive experiments demonstrate that the proposed RFS-Rec approach can outperform the state-of-the-art methods.