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摘要翻译：
深度神经网络在医学图像重建领域显示出良好的应用前景。本文提出了一种基于深度神经网络的磁共振成像重建算法，即定量磁化率映射(QSM)，通过偶极子反褶积，从磁共振成像场图中恢复磁化率源。以往的QSM方法需要多方位数据（如通过多方位采样或宇宙计算磁化率）或正则化项（如截断k空间划分或TKD；形态学支持的偶极子反演或MEDI）来解决病态反卷积问题。不幸的是，它们要么需要长时间的多方向扫描，要么遭受伪影。为了克服这些缺点，构造了一个深度神经网络QSMnet，从单一方向数据中生成高质量的易感性图。该网络具有改进的U-网结构，并使用金标准的COSMOS QSM地图进行训练。来自5个受试者（每个受试者5个方向）的25个数据集在使用增强方法将数据加倍后进行斑块式训练。另外两个5个方位数据集用于验证和测试（每个数据集一个）。测试数据集的QSMnet图与TKD和MEDI的图在多个头部方位下的图像质量和一致性方面进行了比较。定量和定性的图像质量比较表明，QSMnet的图像质量优于TKD和MEDI，与COSMOS的图像质量相当。此外，QSMnet映射在多个方向上显示出明显优于TKD或MEDI映射的一致性。作为初步应用，该网络对两名患者进行了测试。QSMnet图显示了与MEDI相似的病变对比，显示了未来应用的潜力。
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英文标题：
《Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet》
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作者：
Jaeyeon Yoon, Enhao Gong, Itthi Chatnuntawech, Berkin Bilgic, Jingu
Lee, Woojin Jung, Jingyu Ko, Hosan Jung, Kawin Setsompop, Greg Zaharchuk,
Eung Yeop Kim, John Pauly, and Jongho Lee
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
Deep neural networks have demonstrated promising potential for the field of medical image reconstruction. In this work, an MRI reconstruction algorithm, which is referred to as quantitative susceptibility mapping (QSM), has been developed using a deep neural network in order to perform dipole deconvolution, which restores magnetic susceptibility source from an MRI field map. Previous approaches of QSM require multiple orientation data (e.g. Calculation of Susceptibility through Multiple Orientation Sampling or COSMOS) or regularization terms (e.g. Truncated K-space Division or TKD; Morphology Enabled Dipole Inversion or MEDI) to solve the ill-conditioned deconvolution problem. Unfortunately, they either require long multiple orientation scans or suffer from artifacts. To overcome these shortcomings, a deep neural network, QSMnet, is constructed to generate a high quality susceptibility map from single orientation data. The network has a modified U-net structure and is trained using gold-standard COSMOS QSM maps. 25 datasets from 5 subjects (5 orientation each) were applied for patch-wise training after doubling the data using augmentation. Two additional datasets of 5 orientation data were used for validation and test (one dataset each). The QSMnet maps of the test dataset were compared with those from TKD and MEDI for image quality and consistency in multiple head orientations. Quantitative and qualitative image quality comparisons demonstrate that the QSMnet results have superior image quality to those of TKD or MEDI and have comparable image quality to those of COSMOS. Additionally, QSMnet maps reveal substantially better consistency across the multiple orientations than those from TKD or MEDI. As a preliminary application, the network was tested for two patients. The QSMnet maps showed similar lesion contrasts with those from MEDI, demonstrating potential for future applications.
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PDF链接：
https://arxiv.org/pdf/1803.05627
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