Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
Neuroimage. 2023 Mar;268:119886. doi: 10.1016/j.neuroimage.2023.119886. Epub 2023 Jan 17.
Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series of images at multi-echo time points to estimate tissue field, which prolongs scan time and requires specific reconstruction technique. In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM. Our approach involves optimizing a Cartesian multi-echo k-space sampling pattern with a deep reconstruction network. Next, this optimized sampling pattern was implemented in an mGRE sequence using Cartesian fan-beam k-space segmenting and ordering for prospective scans. Furthermore, we propose to insert a recurrent temporal feature fusion module into the reconstruction network to capture signal redundancies along echo time. Our ablation studies show that both the optimized sampling pattern and proposed reconstruction strategy help improve the quality of the multi-echo image reconstructions. Generalization experiments show that LARO is robust on the test data with new pathologies and different sequence parameters. Our code is available at https://github.com/Jinwei1209/LARO-QSM.git.
定量磁化率映射（QSM）涉及在多个回波时间点采集和重建一系列图像，以估计组织场，这延长了扫描时间，并需要特定的重建技术。在本文中，我们提出了一个新的框架，称为学习采集和重建优化（LARO），旨在加速用于 QSM 的多回波梯度回波（mGRE）脉冲序列。我们的方法涉及使用深度重建网络优化笛卡尔多回波 k 空间采样模式。接下来，使用笛卡尔扇形束 k 空间分段和排序，在前瞻性扫描中在 mGRE 序列中实现此优化的采样模式。此外，我们建议在重建网络中插入一个递归时间特征融合模块，以捕获沿回波时间的信号冗余。我们的消融研究表明，优化的采样模式和提出的重建策略都有助于提高多回波图像重建的质量。泛化实验表明，LARO 在具有新病理学和不同序列参数的测试数据上具有稳健性。我们的代码可在 https://github.com/Jinwei1209/LARO-QSM.git 上获得。
