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期刊论文 6

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2021 1

2018 1

2015 3

2014 1

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全氟化碳 1

双调线圈 1

氟 1

治疗 1

磁共振成像 1

磁共振成像(MRI) 1

细胞标记 1

脑肿瘤分割；核方法；稀疏编码；字典学习 1

腹腔妊娠 1

血管生成术 1

诊断 1

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全氟化碳乳剂19F磁共振成像的最新进展 Review

Anne H. Schmieder,Shelton D. Caruthers,Jochen Keupp,Samuel A. Wickline,Gregory M. Lanza

《工程（英文）》 2015年 第1卷 第4期   页码 475-489 doi: 10.15302/J-ENG-2015103

摘要：

19F磁共振成像(MRI) 的研究可追溯到30多年前。部分原因归结为MRI仪器、19F/1H线圈设计以及临床前和临床核磁共振仪的超高速脉冲序列的发展。

关键词： 氟     磁共振成像(MRI)     双调线圈     全氟化碳     血管生成术     细胞标记    

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Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies

Ziqi Chen, Xiaoqi Huang, Qiyong Gong, Bharat B. Biswal

《医学前沿（英文）》 2021年 第15卷 第4期   页码 528-540 doi: 10.1007/s11684-020-0798-1

摘要： Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.

关键词： psychoradiology     major depressive disorder     MRI     biomarker    

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Iron oxide nanoparticle-based theranostics for cancer imaging and therapy

Xiaoqing REN,Hongwei CHEN,Victor YANG,Duxin SUN

《化学科学与工程前沿（英文）》 2014年 第8卷 第3期   页码 253-264 doi: 10.1007/s11705-014-1425-y

摘要： Theranostic platform, which is equipped with both diagnostic and therapeutic functions, is a promising approach in cancer treatment. From various nanotheranostics studied, iron oxide nanoparticles have advantages since IONPs have good biocompatibility and spatial imaging capability. This review is focused on the IONP-based nanotheranostics for cancer imaging and treatment. The most recent progress for applications of IONP nanotheranostics is summarized, which includes IONP-based diagnosis, magnetic resonance imaging (MRI), multimodal imaging, chemotherapy, hyperthermal therapy, photodynamic therapy, and gene delivery. Future perspectives and challenges are also outlined for the potential development of IONP based theranostics in clinical use.

关键词： theranostics     iron oxide nanoparticles     MRI     drug delivery     photothermal therapy     photodynamic therapy    

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继发性中晚期腹腔妊娠诊断和治疗 ——附一例病例报道

申颖,戴姝艳

《中国工程科学》 2015年 第17卷 第6期   页码 61-64

摘要： 结果：该病例经磁共振成像（MRI）检查明确诊断，同时成功予以手术治疗。结论：继发性中晚期腹腔妊娠非常罕见，临床症状不典型，早期诊断较困难，MRI是一种有效的诊断方法，手术是最重要的治疗方式。

关键词： 腹腔妊娠     诊断     治疗     磁共振成像    

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基于核稀疏表示的磁共振图像分析及其在脑肿瘤自动分割中的应用 None

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

《信息与电子工程前沿（英文）》 2018年 第19卷 第4期   页码 471-480 doi: 10.1631/FITEE.1620342

摘要： 提出一种基于核稀疏编码的全自动脑肿瘤分割方法，并在3D多模态磁共振成像图（magnetic resonance imaging, MRI）上验证。首先对MRI图像进行预处理以减少噪声，然后通过核字典学习提取非线性特征，用来构建坏死组织、水肿组织、非增强肿瘤组织、增强肿瘤组织和健康组织5个适应性字典。对从原始MRI图像上肿瘤像素点周边m×m×m的小区域提取的特征向量进行稀疏编码，并通过一种基于字典学习的核聚类方法对像素点进行编码。最后通过形态滤波填充在多个相连部分间的区域，提高分割质量。

关键词： 脑肿瘤分割；核方法；稀疏编码；字典学习    

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Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based constraint splitting augmented Lagrangian shrinkage algorithm

Min YUAN,Bing-xin YANG,Yi-de MA,Jiu-wen ZHANG,Fu-xiang LU,Tong-feng ZHANG

《信息与电子工程前沿（英文）》 2015年 第16卷 第12期   页码 1069-1087 doi: 10.1631/FITEE.1400423

摘要： Recently, dictionary learning (DL) based methods have been introduced to compressed sensing magnetic resonance imaging (CS-MRI), which outperforms pre-defined analytic sparse priors. However, single-scale trained dictionary directly from image patches is incapable of representing image features from multi-scale, multi-directional perspective, which influences the reconstruction performance. In this paper, incorporating the superior multi-scale properties of uniform discrete curvelet transform (UDCT) with the data matching adaptability of trained dictionaries, we propose a flexible sparsity framework to allow sparser representation and prominent hierarchical essential features capture for magnetic resonance (MR) images. Multi-scale decomposition is implemented by using UDCT due to its prominent properties of lower redundancy ratio, hierarchical data structure, and ease of implementation. Each sub-dictionary of different sub-bands is trained independently to form the multi-scale dictionaries. Corresponding to this brand-new sparsity model, we modify the constraint splitting augmented Lagrangian shrinkage algorithm (C-SALSA) as patch-based C-SALSA (PB C-SALSA) to solve the constraint optimization problem of regularized image reconstruction. Experimental results demonstrate that the trained sub-dictionaries at different scales, enforcing sparsity at multiple scales, can then be efficiently used for MRI reconstruction to obtain satisfactory results with further reduced undersampling rate. Multi-scale UDCT dictionaries potentially outperform both single-scale trained dictionaries and multi-scale analytic transforms. Our proposed sparsity model achieves sparser representation for reconstructed data, which results in fast convergence of reconstruction exploiting PB C-SALSA. Simulation results demonstrate that the proposed method outperforms conventional CS-MRI methods in maintaining intrinsic properties, eliminating aliasing, reducing unexpected artifacts, and removing noise. It can achieve comparable performance of reconstruction with the state-of-the-art methods even under substantially high undersampling factors.

关键词： Compressed sensing (CS)     Magnetic resonance imaging (MRI)     Uniform discrete curvelet transform (UDCT)     Multi-scale dictionary learning (MSDL)     Patch-based constraint splitting augmented Lagrangian shrinkage algorithm (PB C-SALSA)    

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标题 作者 时间 类型 操作

全氟化碳乳剂19F磁共振成像的最新进展

Anne H. Schmieder,Shelton D. Caruthers,Jochen Keupp,Samuel A. Wickline,Gregory M. Lanza

期刊论文

Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies

Ziqi Chen, Xiaoqi Huang, Qiyong Gong, Bharat B. Biswal

期刊论文

Iron oxide nanoparticle-based theranostics for cancer imaging and therapy

Xiaoqing REN,Hongwei CHEN,Victor YANG,Duxin SUN

期刊论文

继发性中晚期腹腔妊娠诊断和治疗 ——附一例病例报道

申颖,戴姝艳

期刊论文

基于核稀疏表示的磁共振图像分析及其在脑肿瘤自动分割中的应用

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

期刊论文

Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based constraint splitting augmented Lagrangian shrinkage algorithm

Min YUAN,Bing-xin YANG,Yi-de MA,Jiu-wen ZHANG,Fu-xiang LU,Tong-feng ZHANG

期刊论文