IEEE Transactions on Medical Imaging最新文献_第10页

Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis 用于肺结节分析的具有边际排序损失的多任务深度模型

IF 10.6 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging

Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2934577

Lihao Liu, Q. Dou, Hao Chen, J. Qin, P. Heng

Lung cancer is the leading cause of cancer deaths worldwide and early diagnosis of lung nodule is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accurate lung nodule benign-malignant classification and attribute score regression. However, this is quite challenging due to the considerable difficulty of lung nodule heterogeneity modeling and the limited discrimination capability on ambiguous cases. To solve these challenges, we propose a Multi-Task deep model with Margin Ranking loss (referred as MTMR-Net) for automated lung nodule analysis. Compared to existing methods which consider these two tasks separately, the relatedness between lung nodule classification and attribute score regression is explicitly explored in a cause-and-effect manner within our multi-task deep model, which can contribute to the performance gains of both tasks. The results of different tasks can be yielded simultaneously for assisting the radiologists in diagnosis interpretation. Furthermore, a Siamese network with a margin ranking loss is elaborately designed to enhance the discrimination capability on ambiguous nodule cases. To further explore the internal relationship between two tasks and validate the effectiveness of the proposed model, we use the recursive feature elimination method to iteratively rank the most malignancy-related features. We validate the efficacy of our method MTMR-Net on the public benchmark LIDC-IDRI dataset. Extensive experiments show that the diagnosis results with internal relationship explicitly explored in our model has met some similar patterns in clinical usage and also demonstrate that our approach can achieve competitive classification performance and more accurate scoring on attributes over the state-of-the-arts. Codes are publicly available at: https://github.com/CaptainWilliam/MTMR-NET.

癌症是全球癌症死亡的主要原因，肺结节的早期诊断对治疗和挽救生命具有重要意义。自动肺结节分析需要准确的肺结节良恶性分类和属性评分回归。然而，这是相当具有挑战性的，因为肺结节异质性建模相当困难，并且对模糊病例的识别能力有限。为了解决这些挑战，我们提出了一种具有边际排名损失的多任务深度模型（称为MTMR-Net），用于自动肺结节分析。与分别考虑这两项任务的现有方法相比，在我们的多任务深度模型中，以因果关系的方式明确探讨了肺结节分类和属性得分回归之间的相关性，这有助于提高这两项工作的性能。不同任务的结果可以同时产生，以帮助放射科医生进行诊断解释。此外，精心设计了一个具有边际排名损失的暹罗网络，以增强对模糊结节病例的识别能力。为了进一步探索两个任务之间的内部关系并验证所提出的模型的有效性，我们使用递归特征消除方法对与恶性肿瘤最相关的特征进行迭代排序。我们在公共基准LIDC-IDRI数据集上验证了我们的方法MTMR-Net的有效性。大量实验表明，在我们的模型中明确探索的具有内部关系的诊断结果在临床使用中符合一些类似的模式，也表明我们的方法可以实现有竞争力的分类性能和比现有技术更准确的属性评分。代码可在以下网址公开获取：https://github.com/CaptainWilliam/MTMR-NET.

{"title":"Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis","authors":"Lihao Liu, Q. Dou, Hao Chen, J. Qin, P. Heng","doi":"10.1109/TMI.2019.2934577","DOIUrl":"https://doi.org/10.1109/TMI.2019.2934577","url":null,"abstract":"Lung cancer is the leading cause of cancer deaths worldwide and early diagnosis of lung nodule is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accurate lung nodule benign-malignant classification and attribute score regression. However, this is quite challenging due to the considerable difficulty of lung nodule heterogeneity modeling and the limited discrimination capability on ambiguous cases. To solve these challenges, we propose a Multi-Task deep model with Margin Ranking loss (referred as MTMR-Net) for automated lung nodule analysis. Compared to existing methods which consider these two tasks separately, the relatedness between lung nodule classification and attribute score regression is explicitly explored in a cause-and-effect manner within our multi-task deep model, which can contribute to the performance gains of both tasks. The results of different tasks can be yielded simultaneously for assisting the radiologists in diagnosis interpretation. Furthermore, a Siamese network with a margin ranking loss is elaborately designed to enhance the discrimination capability on ambiguous nodule cases. To further explore the internal relationship between two tasks and validate the effectiveness of the proposed model, we use the recursive feature elimination method to iteratively rank the most malignancy-related features. We validate the efficacy of our method MTMR-Net on the public benchmark LIDC-IDRI dataset. Extensive experiments show that the diagnosis results with internal relationship explicitly explored in our model has met some similar patterns in clinical usage and also demonstrate that our approach can achieve competitive classification performance and more accurate scoring on attributes over the state-of-the-arts. Codes are publicly available at: https://github.com/CaptainWilliam/MTMR-NET.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"718-728"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2934577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47058684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 79

Analyzing Dynamical Brain Functional Connectivity as Trajectories on Space of Covariance Matrices. 将动态脑功能连接作为协方差矩阵空间上的轨迹分析

IF 10.6 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging

Pub Date : 2020-03-01 Epub Date: 2019-08-02 DOI: 10.1109/TMI.2019.2931708

Mengyu Dai, Zhengwu Zhang, Anuj Srivastava

Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task. This paper aims to statistically analyze the dynamic nature of FC by representing the collective time-series data, over a set of brain regions, as a trajectory on the space of covariance matrices, or symmetric-positive definite matrices (SPDMs). We use a recently developed metric on the space of SPDMs for quantifying differences across FC observations, and for clustering and classification of FC trajectories. To facilitate large scale and high-dimensional data analysis, we propose a novel, metric-based dimensionality reduction technique to reduce data from large SPDMs to small SPDMs. We illustrate this comprehensive framework using data from the Human Connectome Project (HCP) database for multiple subjects and tasks, with task classification rates that match or outperform state-of-the-art techniques.

人脑功能连接（FC）通常被测量为当大脑休息或执行任务时，大脑各区域功能MRI反应的相似性。本文旨在通过将一组大脑区域上的集体时间序列数据表示为协方差矩阵或对称正定矩阵（SPDM）空间上的轨迹来统计分析FC的动态性质。我们使用最近开发的SPDM空间度量来量化FC观测结果之间的差异，并对FC轨迹进行聚类和分类。为了促进大规模和高维数据分析，我们提出了一种新的基于度量的降维技术，将数据从大SPDM降到小SPDM。我们使用来自人类连接体项目（HCP）数据库的多个受试者和任务的数据来说明这一综合框架，任务分类率与最先进的技术相匹配或优于最先进的方法。

引用次数: 0

One-Shot Generative Adversarial Learning for MRI Segmentation of Craniomaxillofacial Bony Structures 一次生成对抗学习在颅颌面骨结构MRI分割中的应用

IF 10.6 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging

Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2935409

Xu Chen, J. Xia, D. Shen, C. Lian, Li Wang, H. Deng, S. Fung, Dong Nie, Kim-Han Thung, P. Yap, J. Gateno

Compared to computed tomography (CT), magnetic resonance imaging (MRI) delineation of craniomaxillofacial (CMF) bony structures can avoid harmful radiation exposure. However, bony boundaries are blurry in MRI, and structural information needs to be borrowed from CT during the training. This is challenging since paired MRI-CT data are typically scarce. In this paper, we propose to make full use of unpaired data, which are typically abundant, along with a single paired MRI-CT data to construct a one-shot generative adversarial model for automated MRI segmentation of CMF bony structures. Our model consists of a cross-modality image synthesis sub-network, which learns the mapping between CT and MRI, and an MRI segmentation sub-network. These two sub-networks are trained jointly in an end-to-end manner. Moreover, in the training phase, a neighbor-based anchoring method is proposed to reduce the ambiguity problem inherent in cross-modality synthesis, and a feature-matching-based semantic consistency constraint is proposed to encourage segmentation-oriented MRI synthesis. Experimental results demonstrate the superiority of our method both qualitatively and quantitatively in comparison with the state-of-the-art MRI segmentation methods.

与计算机断层扫描(CT)相比，磁共振成像(MRI)描绘颅颌面(CMF)骨骼结构可以避免有害的辐射暴露。但MRI显示骨边界模糊，训练时需要借鉴CT的结构信息。这是具有挑战性的，因为配对的MRI-CT数据通常很少。在本文中，我们建议充分利用通常丰富的未配对数据，以及单个配对的MRI- ct数据，构建一个一次性生成对抗模型，用于CMF骨结构的MRI自动分割。我们的模型包括一个学习CT和MRI之间映射的跨模态图像合成子网络和一个MRI分割子网络。这两个子网以端到端的方式进行联合训练。此外，在训练阶段，提出了基于邻域的锚定方法来减少跨模态合成中固有的歧义问题，并提出了基于特征匹配的语义一致性约束来促进面向分割的MRI合成。实验结果表明，我们的方法在定性和定量上都优于最先进的MRI分割方法。

{"title":"One-Shot Generative Adversarial Learning for MRI Segmentation of Craniomaxillofacial Bony Structures","authors":"Xu Chen, J. Xia, D. Shen, C. Lian, Li Wang, H. Deng, S. Fung, Dong Nie, Kim-Han Thung, P. Yap, J. Gateno","doi":"10.1109/TMI.2019.2935409","DOIUrl":"https://doi.org/10.1109/TMI.2019.2935409","url":null,"abstract":"Compared to computed tomography (CT), magnetic resonance imaging (MRI) delineation of craniomaxillofacial (CMF) bony structures can avoid harmful radiation exposure. However, bony boundaries are blurry in MRI, and structural information needs to be borrowed from CT during the training. This is challenging since paired MRI-CT data are typically scarce. In this paper, we propose to make full use of unpaired data, which are typically abundant, along with a single paired MRI-CT data to construct a one-shot generative adversarial model for automated MRI segmentation of CMF bony structures. Our model consists of a cross-modality image synthesis sub-network, which learns the mapping between CT and MRI, and an MRI segmentation sub-network. These two sub-networks are trained jointly in an end-to-end manner. Moreover, in the training phase, a neighbor-based anchoring method is proposed to reduce the ambiguity problem inherent in cross-modality synthesis, and a feature-matching-based semantic consistency constraint is proposed to encourage segmentation-oriented MRI synthesis. Experimental results demonstrate the superiority of our method both qualitatively and quantitatively in comparison with the state-of-the-art MRI segmentation methods.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"40 4","pages":"787-796"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2935409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41266392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction 用于多对比度MRI重建的耦合字典学习

IF 10.6 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging

Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2932961

P. Song, L. Weizman, J. Mota, Yonina C. Eldar, M. Rodrigues

Magnetic resonance (MR) imaging tasks often involve multiple contrasts, such as T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities in either structure level or gray level. In this paper, we propose a coupled dictionary learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage the dependency correlation between different contrasts for guided or joint reconstruction from their under-sampled

$k$

-space data. Our approach iterates between three stages: coupled dictionary learning, coupled sparse denoising, and enforcing

$k$

-space consistency. The first stage learns a set of dictionaries that not only are adaptive to the contrasts, but also capture correlations among multiple contrasts in a sparse transform domain. By capitalizing on the learned dictionaries, the second stage performs coupled sparse coding to remove the aliasing and noise in the corrupted contrasts. The third stage enforces consistency between the denoised contrasts and the measurements in the

$k$

-space domain. Numerical experiments, consisting of retrospective under-sampling of various MRI contrasts with a variety of sampling schemes, demonstrate that CDLMRI is capable of capturing structural dependencies between different contrasts. The learned priors indicate notable advantages in multi-contrast MR imaging and promising applications in quantitative MR imaging such as MR fingerprinting.

磁共振(MR)成像任务通常涉及多重对比，如t1加权、t2加权和流体衰减反演恢复(FLAIR)数据。这些对比捕获了与相同底层解剖结构相关的信息，因此在结构级别或灰度级别上表现出相似性。在本文中，我们提出了一种基于耦合字典学习的多对比MRI重建(CDLMRI)方法，利用不同对比之间的依赖相关性，从其欠采样的$k$空间数据中进行引导或联合重建。我们的方法在三个阶段之间迭代:耦合字典学习、耦合稀疏去噪和强制k空间一致性。第一阶段学习一组字典，这些字典不仅可以适应对比，还可以捕获稀疏变换域中多个对比之间的相关性。通过利用学习到的字典，第二阶段执行耦合稀疏编码，以消除损坏对比度中的混叠和噪声。第三阶段加强去噪对比度和k空间域中测量值之间的一致性。数值实验通过对不同采样方案下的不同MRI对比进行回顾性欠采样，证明了CDLMRI能够捕捉不同对比之间的结构依赖性。学习到的先验在多对比磁共振成像中具有显著的优势，在定量磁共振成像(如磁共振指纹识别)中具有广阔的应用前景。

{"title":"Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction","authors":"P. Song, L. Weizman, J. Mota, Yonina C. Eldar, M. Rodrigues","doi":"10.1109/TMI.2019.2932961","DOIUrl":"https://doi.org/10.1109/TMI.2019.2932961","url":null,"abstract":"Magnetic resonance (MR) imaging tasks often involve multiple contrasts, such as T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities in either structure level or gray level. In this paper, we propose a coupled dictionary learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage the dependency correlation between different contrasts for guided or joint reconstruction from their under-sampled <inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>-space data. Our approach iterates between three stages: coupled dictionary learning, coupled sparse denoising, and enforcing <inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>-space consistency. The first stage learns a set of dictionaries that not only are adaptive to the contrasts, but also capture correlations among multiple contrasts in a sparse transform domain. By capitalizing on the learned dictionaries, the second stage performs coupled sparse coding to remove the aliasing and noise in the corrupted contrasts. The third stage enforces consistency between the denoised contrasts and the measurements in the <inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>-space domain. Numerical experiments, consisting of retrospective under-sampling of various MRI contrasts with a variety of sampling schemes, demonstrate that CDLMRI is capable of capturing structural dependencies between different contrasts. The learned priors indicate notable advantages in multi-contrast MR imaging and promising applications in quantitative MR imaging such as MR fingerprinting.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"621-633"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2932961","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47818490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Table of contents 目录表

IF 10.6 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging

Pub Date : 2020-03-01 DOI: 10.1109/tmi.2020.2973610

引用次数: 0

Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models 通过学习非线性低维模型进行约束磁共振光谱成像

IF 10.6 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging

Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2930586

F. Lam, Yahang Li, Xi Peng

Magnetic resonance spectroscopic imaging (MRSI) is a powerful molecular imaging modality but has very limited speed, resolution, and SNR tradeoffs. Construction of a low-dimensional model to effectively reduce the dimensionality of the imaging problem has recently shown great promise in improving these tradeoffs. This paper presents a new approach to model and reconstruct the spectroscopic signals by learning a nonlinear low-dimensional representation of the general MR spectra. Specifically, we trained a deep neural network to capture the low-dimensional manifold, where the high-dimensional spectroscopic signals reside. A regularization formulation is proposed to effectively integrate the learned model and physics-based data acquisition model for MRSI reconstruction with the capability to incorporate additional spatiospectral constraints. An efficient numerical algorithm was developed to solve the associated optimization problem involving back-propagating the trained network. Simulation and experimental results were obtained to demonstrate the representation power of the learned model and the ability of the proposed formulation in producing SNR-enhancing reconstruction from the practical MRSI data.

磁共振波谱成像（MRSI）是一种强大的分子成像方式，但在速度、分辨率和信噪比方面的权衡非常有限。构建一个低维模型来有效地降低成像问题的维数，最近在改善这些权衡方面显示出了巨大的前景。本文提出了一种通过学习一般MR光谱的非线性低维表示来建模和重建光谱信号的新方法。具体来说，我们训练了一个深度神经网络来捕捉高维光谱信号所在的低维流形。提出了一种正则化公式，以有效地集成用于MRSI重建的学习模型和基于物理的数据采集模型，并能够结合额外的时空约束。开发了一种有效的数值算法来解决涉及训练网络反向传播的相关优化问题。仿真和实验结果证明了所学习的模型的表示能力以及所提出的公式在从实际MRSI数据中产生SNR增强重建的能力。

{"title":"Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models","authors":"F. Lam, Yahang Li, Xi Peng","doi":"10.1109/TMI.2019.2930586","DOIUrl":"https://doi.org/10.1109/TMI.2019.2930586","url":null,"abstract":"Magnetic resonance spectroscopic imaging (MRSI) is a powerful molecular imaging modality but has very limited speed, resolution, and SNR tradeoffs. Construction of a low-dimensional model to effectively reduce the dimensionality of the imaging problem has recently shown great promise in improving these tradeoffs. This paper presents a new approach to model and reconstruct the spectroscopic signals by learning a nonlinear low-dimensional representation of the general MR spectra. Specifically, we trained a deep neural network to capture the low-dimensional manifold, where the high-dimensional spectroscopic signals reside. A regularization formulation is proposed to effectively integrate the learned model and physics-based data acquisition model for MRSI reconstruction with the capability to incorporate additional spatiospectral constraints. An efficient numerical algorithm was developed to solve the associated optimization problem involving back-propagating the trained network. Simulation and experimental results were obtained to demonstrate the representation power of the learned model and the ability of the proposed formulation in producing SNR-enhancing reconstruction from the practical MRSI data.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"545-555"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2930586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49664595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 41

Simultaneous Multi-VENC and Simultaneous Multi-Slice Phase Contrast Magnetic Resonance Imaging 同时多VENC和同时多切片相位对比磁共振成像

IF 10.6 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging

Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2934422

Suhyung Park, Liyong Chen, Jennifer Townsend, Hyunyeol Lee, D. Feinberg

This work develops a novel, simultaneous multi-VENC and simultaneous multi-slice (SMV+SMS) imaging in a single acquisition for robust phase contrast (PC) MRI. To this end, the pulse sequence was designed to permit concurrent acquisition of multiple VENCs as well as multiple slices on a shared frequency encoding gradient, in which each effective echo time for multiple VENCs was controlled by adjusting net gradient area while multiple slices were simultaneously excited by employing multiband resonance frequency (RF) pulses. For VENC and slice separation, RF phase cycling and gradient blip were applied to create both inter-VENC and inter-slice shifts along phase encoding direction, respectively. With an alternating RF phase cycling that generates oscillating steady-state with low and high signal amplitude, the acquired multi-VENC k-space was reformulated into 3D undersampled k-space by generating a virtual dimension along VENC direction for modulation induced artifact reduction. In vivo studies were conducted to validate the feasibility of the proposed method in comparison with conventional PC MRI. The proposed method shows comparable performance to the conventional method in delineating both low and high flow velocities across cardiac phases with high spatial coverage without apparent artifacts. In the presence of high flow velocity that is above the VENC value, the proposed method exhibits clear depiction of flow signals over conventional method, thereby leading to high VNR image with improved velocity dynamic range.

这项工作开发了一种新的、同时进行多VENC和同时进行多切片（SMV+SMS）成像的单次采集，用于稳健相位对比（PC）MRI。为此，脉冲序列被设计为允许在共享频率编码梯度上同时采集多个VENC以及多个切片，其中通过调整净梯度面积来控制多个VENCs的每个有效回波时间，同时通过使用多频带共振频率（RF）脉冲来同时激励多个切片。对于VENC和切片分离，分别应用RF相位循环和梯度光点来产生沿相位编码方向的VENC间和切片间偏移。通过产生具有低和高信号幅度的振荡稳态的交替RF相位循环，通过产生沿VENC方向的虚拟维度来减少调制引起的伪影，将所获取的多VENC k空间重新表述为3D欠采样k空间。与传统的PC MRI相比，进行了体内研究以验证所提出的方法的可行性。在没有明显伪影的情况下，所提出的方法在描绘跨心时相的低流速和高流速方面显示出与传统方法相当的性能。在存在高于VENC值的高流速的情况下，与传统方法相比，所提出的方法表现出对流量信号的清晰描述，从而产生具有改进的速度动态范围的高VNR图像。

{"title":"Simultaneous Multi-VENC and Simultaneous Multi-Slice Phase Contrast Magnetic Resonance Imaging","authors":"Suhyung Park, Liyong Chen, Jennifer Townsend, Hyunyeol Lee, D. Feinberg","doi":"10.1109/TMI.2019.2934422","DOIUrl":"https://doi.org/10.1109/TMI.2019.2934422","url":null,"abstract":"This work develops a novel, simultaneous multi-VENC and simultaneous multi-slice (SMV+SMS) imaging in a single acquisition for robust phase contrast (PC) MRI. To this end, the pulse sequence was designed to permit concurrent acquisition of multiple VENCs as well as multiple slices on a shared frequency encoding gradient, in which each effective echo time for multiple VENCs was controlled by adjusting net gradient area while multiple slices were simultaneously excited by employing multiband resonance frequency (RF) pulses. For VENC and slice separation, RF phase cycling and gradient blip were applied to create both inter-VENC and inter-slice shifts along phase encoding direction, respectively. With an alternating RF phase cycling that generates oscillating steady-state with low and high signal amplitude, the acquired multi-VENC k-space was reformulated into 3D undersampled k-space by generating a virtual dimension along VENC direction for modulation induced artifact reduction. In vivo studies were conducted to validate the feasibility of the proposed method in comparison with conventional PC MRI. The proposed method shows comparable performance to the conventional method in delineating both low and high flow velocities across cardiac phases with high spatial coverage without apparent artifacts. In the presence of high flow velocity that is above the VENC value, the proposed method exhibits clear depiction of flow signals over conventional method, thereby leading to high VNR image with improved velocity dynamic range.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"742-752"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2934422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42567414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic, Age Consistent Reconstruction of the Corpus Callosum Guided by Coherency From In Utero Diffusion-Weighted MRI 子宫内扩散加权MRI相干引导下胼胝体的自动、年龄一致性重建

IF 10.6 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging

Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2932681

D. Hunt, M. Dighe, Chris Gatenby, C. Studholme

Reconstruction of white matter connectivity in the fetal brain from in utero diffusion-weighted magnetic resonance imaging (MRI) faces many challenges, including subject motion, small anatomical scale, and limited image resolution and signal. These issues are compounded by the need to track significant changes in structural connectivity throughout development. We present an automated method for improved reliability and completeness of tract extraction across a wide range of gestational ages, based on the geometry of coherent patterns in streamline tractography, and apply it to the reconstruction of the corpus callosum. This method, focused specifically at addressing the challenges of fetal brain imaging, avoids depending on a tractography atlas, and handles variations in size, shape, and tissue properties of developing brains, both between subjects and across ages. Although tractography from in utero MRI generally suffers from a significant number of misleading and missing pathways, we demonstrate the feasibility of extracting the coherent bundle of the corpus callosum while avoiding inappropriate diversions into other tracts.

通过子宫内扩散加权磁共振成像（MRI）重建胎儿大脑中的白质连接面临许多挑战，包括受试者运动、解剖规模小以及图像分辨率和信号有限。这些问题因需要在整个发展过程中跟踪结构连通性的重大变化而更加复杂。我们提出了一种基于流线束描记术中相干模式的几何形状的自动化方法，以提高不同胎龄的束提取的可靠性和完整性，并将其应用于胼胝体的重建。这种方法专门致力于解决胎儿大脑成像的挑战，避免了依赖纤维束造影图谱，并处理了受试者之间和不同年龄段发育中大脑的大小、形状和组织特性的变化。尽管子宫内MRI的束描记术通常存在大量误导和缺失的路径，但我们证明了提取胼胝体相干束的可行性，同时避免了不适当的转移到其他束中。

{"title":"Automatic, Age Consistent Reconstruction of the Corpus Callosum Guided by Coherency From In Utero Diffusion-Weighted MRI","authors":"D. Hunt, M. Dighe, Chris Gatenby, C. Studholme","doi":"10.1109/TMI.2019.2932681","DOIUrl":"https://doi.org/10.1109/TMI.2019.2932681","url":null,"abstract":"Reconstruction of white matter connectivity in the fetal brain from in utero diffusion-weighted magnetic resonance imaging (MRI) faces many challenges, including subject motion, small anatomical scale, and limited image resolution and signal. These issues are compounded by the need to track significant changes in structural connectivity throughout development. We present an automated method for improved reliability and completeness of tract extraction across a wide range of gestational ages, based on the geometry of coherent patterns in streamline tractography, and apply it to the reconstruction of the corpus callosum. This method, focused specifically at addressing the challenges of fetal brain imaging, avoids depending on a tractography atlas, and handles variations in size, shape, and tissue properties of developing brains, both between subjects and across ages. Although tractography from in utero MRI generally suffers from a significant number of misleading and missing pathways, we demonstrate the feasibility of extracting the coherent bundle of the corpus callosum while avoiding inappropriate diversions into other tracts.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"601-610"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2932681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42571471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging. 电磁脑成像分布式源的鲁棒经验贝叶斯重构

IF 8.9 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging

Pub Date : 2020-03-01 Epub Date: 2019-07-31 DOI: 10.1109/TMI.2019.2932290

Chang Cai, Mithun Diwakar, Dan Chen, Kensuke Sekihara, Srikantan S Nagarajan

Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling. Since the proposed algorithm builds upon many of the performance features of the sparse source reconstruction algorithm - Champagne and we refer to this algorithm as Smooth Champagne. Smooth Champagne is robust to the effects of high levels of noise, interference, and highly correlated brain source activity. Simulations demonstrate excellent performance of Smooth Champagne when compared to benchmark algorithms in accurately determining the spatial extent of distributed source activity. Smooth Champagne also accurately reconstructs real MEG and EEG data.

电磁脑成像是从磁场和电势的非侵入性记录中重建大脑活动。在这种成像模式中，一个持久的挑战是估计来源的数量、位置和时间进程，特别是对于具有复杂空间范围的分布式脑来源的重建。在这里，我们介绍了一种新的鲁棒经验贝叶斯算法，该算法利用两个关键思想：核平滑和超参数平铺，能够更好地重建分布式脑源活动。由于所提出的算法建立在稀疏源重建算法Champagne的许多性能特征的基础上，因此我们将该算法称为平滑香槟算法。Smooth Champagne对高水平的噪音、干扰和高度相关的脑源活动的影响很强。仿真表明，与基准算法相比，Smooth Champagne在准确确定分布式源活动的空间范围方面表现出色。Smooth Champagne还可以准确地重建真实的脑磁图和脑电图数据。

{"title":"Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging.","authors":"Chang Cai, Mithun Diwakar, Dan Chen, Kensuke Sekihara, Srikantan S Nagarajan","doi":"10.1109/TMI.2019.2932290","DOIUrl":"10.1109/TMI.2019.2932290","url":null,"abstract":"<p><p>Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling. Since the proposed algorithm builds upon many of the performance features of the sparse source reconstruction algorithm - Champagne and we refer to this algorithm as Smooth Champagne. Smooth Champagne is robust to the effects of high levels of noise, interference, and highly correlated brain source activity. Simulations demonstrate excellent performance of Smooth Champagne when compared to benchmark algorithms in accurately determining the spatial extent of distributed source activity. Smooth Champagne also accurately reconstructs real MEG and EEG data.</p>","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"567-577"},"PeriodicalIF":8.9,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49319039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An On-Board Spectral-CT/CBCT/SPECT Imaging Configuration for Small-Animal Radiation Therapy Platform: A Monte Carlo Study 用于小动物放射治疗平台的车载光谱CT/CCBT/SPECT成像配置：蒙特卡罗研究

IF 10.6 1区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Medical Imaging

Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2932333

Hui Wang, K. Nie, Y. Kuang

This study investigated the feasibility of a highly specific multiplexed image-guided small animal radiation therapy (SART) platform based on triple imaging from on-board single-photon emission computed tomography (SPECT), spectral-CT, and cone-beam CT (CBCT) guidance in radiotherapy treatment. As a proof-of-concept, the SART system was built with the capability of triple on-board image guidance by utilizing an x-ray tube and a single cadmium zinc telluride (CZT) semiconductor photon-counting imager via a Monte Carlo simulation study. The x-ray tube can be set at a low tube current for imaging mode and a high tube current for radiation therapy mode, respectively. In the imaging mode, both x-ray and gamma-ray projection data were collected by the imager to reconstruct CBCT, SPECT and spectral CT images of small animals being treated. The modulation transfer function (MTF) of the pixelated CZT imager measured was 8.6 lp/mm. The overall performances of the CBCT and SPECT imaging of the system were evaluated with sufficient spatial resolution and imaging quality to be fitted into the SART platform. The material differentiation and decomposition capacities of spectral CT within the system were verified using K-edge imaging, image-based optimal energy weighted imaging, and image-based linear material decomposition methods. The triple imaging capability of the system was demonstrated using a PMMA phantom containing gadolinium, iodine and radioisotope 99mTc inserts. All the probes were clearly identified in the registered image. The results demonstrated that a novel SART platform with high-quality on-board CBCT, spectral-CT, SPECT image guidance is technically feasible by using a single semiconductor imager, thus affording comprehensive image guidance from anatomical, functional, and molecular levels for radiation treatment beam delivery.

本研究调查了基于机载单光子发射计算机断层扫描（SPECT）、光谱CT和锥束CT（CBCT）引导的三重成像的高度特异性多路图像引导小动物放射治疗（SART）平台在放射治疗中的可行性。作为概念验证，通过蒙特卡洛模拟研究，利用x射线管和单个碲化镉锌（CZT）半导体光子计数成像器，构建了具有三重机载图像制导能力的SART系统。x射线管可以分别设置为用于成像模式的低管电流和用于放射治疗模式的高管电流。在成像模式中，成像器收集x射线和伽马射线投影数据，以重建正在接受治疗的小动物的CBCT、SPECT和光谱CT图像。测量的像素化CZT成像器的调制传递函数（MTF）为8.6lp/mm。系统的CBCT和SPECT成像的总体性能以足够的空间分辨率和成像质量进行了评估，以适应SART平台。使用K边缘成像、基于图像的最优能量加权成像和基于图像的线性材料分解方法验证了系统内光谱CT的材料区分和分解能力。使用含有钆、碘和放射性同位素99mTc插入物的PMMA体模证明了该系统的三重成像能力。所有探针在注册图像中都被清楚地识别。结果表明，通过使用单个半导体成像器，一种具有高质量机载CBCT、光谱CT和SPECT图像引导的新型严重急性呼吸系统综合征平台在技术上是可行的，从而为放射治疗光束输送提供解剖、功能和分子水平的全面图像引导。

{"title":"An On-Board Spectral-CT/CBCT/SPECT Imaging Configuration for Small-Animal Radiation Therapy Platform: A Monte Carlo Study","authors":"Hui Wang, K. Nie, Y. Kuang","doi":"10.1109/TMI.2019.2932333","DOIUrl":"https://doi.org/10.1109/TMI.2019.2932333","url":null,"abstract":"This study investigated the feasibility of a highly specific multiplexed image-guided small animal radiation therapy (SART) platform based on triple imaging from on-board single-photon emission computed tomography (SPECT), spectral-CT, and cone-beam CT (CBCT) guidance in radiotherapy treatment. As a proof-of-concept, the SART system was built with the capability of triple on-board image guidance by utilizing an x-ray tube and a single cadmium zinc telluride (CZT) semiconductor photon-counting imager via a Monte Carlo simulation study. The x-ray tube can be set at a low tube current for imaging mode and a high tube current for radiation therapy mode, respectively. In the imaging mode, both x-ray and gamma-ray projection data were collected by the imager to reconstruct CBCT, SPECT and spectral CT images of small animals being treated. The modulation transfer function (MTF) of the pixelated CZT imager measured was 8.6 lp/mm. The overall performances of the CBCT and SPECT imaging of the system were evaluated with sufficient spatial resolution and imaging quality to be fitted into the SART platform. The material differentiation and decomposition capacities of spectral CT within the system were verified using K-edge imaging, image-based optimal energy weighted imaging, and image-based linear material decomposition methods. The triple imaging capability of the system was demonstrated using a PMMA phantom containing gadolinium, iodine and radioisotope 99mTc inserts. All the probes were clearly identified in the registered image. The results demonstrated that a novel SART platform with high-quality on-board CBCT, spectral-CT, SPECT image guidance is technically feasible by using a single semiconductor imager, thus affording comprehensive image guidance from anatomical, functional, and molecular levels for radiation treatment beam delivery.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"588-600"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2932333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49537593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4