「AI+磁共振成像」研究进入挑起期：沈定刚教授SCI论文33篇，影响最为突出

识别由这些总共被推学术著作聚类形成的集合，将这些集合定义了深入研究基础性RF。

ESI信息库系统概述了10000个差不多的RF，每个RF包含生产量不等的高被推学术著作。进入RF的学术著作，在一定程度上体现了研究者和深入研究政府部门对深入研究的前瞻性和对基础性的重大贡献。

根据鉴定，都有39篇学术著作为该信息核心技术里的深入研究基础性学术著作：

AGGARWAL, H. K., MANI, M. P. Company JACOB, M. 2019. MoDL: Model-Based Deep Learning Architecture for Inverse Problems. Ieee Transactions on Medical Imaging, 38, 394-405.

AHMADI, M., SHARIFI, A., FARD, M. J. Company SOLEIMANI, N. Detection of brain lesion location in MRI images using convolutional neural network and robust PCA. International Journal of Neuroscience, 12.

AKCAKAYA, M., MOELLER, S., WEINGARTNER, S. Company UGURBIL, K. 2019. Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging. Magnetic Resonance in Medicine, 81, 439-453.

AVENDI, M. R., KHERADVAR, A. Company JAFARKHANI, H. 2016. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Medical Image Analysis, 30, 108-119.

BERNARD, O., LALANDE, A., ZOTTI, C., et al. 2018. Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? Ieee Transactions on Medical Imaging, 37, 2514-2525.

BHATTACHARYA, S., MADDIKUNTA, P. K. R., PHAM, Q. V., et al. 2021. Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey. Sustainable Cities and Society, 65, 18.

CHEN, K. T., GONG, E. H., MACRUZ, F. B. D., et al. 2019. Ultra-Low-Dose F-18-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs. Radiology, 290, 649-656.

CHEN, P. J., LIN, M. C., LAI, M. J., LIN, J. C., LU, H. H. S. Company TSENG, V. S. 2018. Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis. Gastroenterology, 154, 568-575.

COLE, J. H., POUDEL, R. P. K., TSAGKRASOULIS, D., et al. 2017. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage, 163, 115-124.

DE VOS, B. D., BERENDSEN, F. F., VIERGEVER, M. A., SOKOOTI, H., STARING, M. Company ISGUM, I. 2019. A deep learning framework for unsupervised affine and deformable image registration. Medical Image Analysis, 52, 128-143.

DENG, Y., REN, Z. Q., KONG, Y. Y., BAO, F. Company DAI, Q. H. 2017. A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification. Ieee Transactions on Fuzzy Systems, 25, 1006-1012.

GIBSON, E., LI, W. Q., SUDRE, C., et al. 2018. NiftyNet: a deep-learning platform for medical imaging. Computer Methods and Programs in Biomedicine, 158, 113-122.

HAMM, C. A., WANG, C. J., SAVIC, L. J., et al. 2019. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. European Radiology, 29, 3338-3347.

HAMMERNIK, K., KLATZER, T., KOBLER, E., et al. 2018. Learning a variational network for reconstruction of accelerated MRI data. Magnetic Resonance in Medicine, 79, 3055-3071.

HAN, X. 2017. MR-based synthetic CT generation using a deep convolutional neural network method. Medical Physics, 44, 1408-1419.

HAN, Y., SUNWOO, L. Company YE, J. C. 2020. k-Space Deep Learning for Accelerated MRI. Ieee Transactions on Medical Imaging, 39, 377-386.

HAN, Y., YOO, J., KIM, H. H., SHIN, H. J., SUNG, K. Company YE, J. C. 2018. Deep learning with domain adaptation for accelerated projection-reconstruction MR. Magnetic Resonance in Medicine, 80, 1189-1205.

HAZLETT, H. C., GU, H. B., MUNSELL, B. C., et al. 2017. Early brain development in infants at high risk for autism spectrum disorder. Nature, 542, 348-+.

KAMNITSAS, K., LEDIG, C., NEWCOMBE, V. F. J., et al. 2017. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36, 61-78.

KNOLL, F., HAMMERNIK, K., KOBLER, E., et al. 2019. Assessment of the generalization of learned image reconstruction and the potential for transfer learning. Magnetic Resonance in Medicine, 81, 116-128.

LEYNES, A. P., YANG, J., WIESINGER, F., et al. 2018. Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI. Journal of Nuclear Medicine, 59, 852-858.

LIU, F., ZHOU, Z. Y., JANG, H., SAMSONOV, A., et al. 2018. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magnetic Resonance in Medicine, 79, 2379-2391.

MARDANI, M., GONG, E. H., CHENG, J. Y., et al. 2019. Deep Generative Adversarial Neural Networks for Compressive Sensing MRI. Ieee Transactions on Medical Imaging, 38, 167-179.

MUHAMMAD, K., KHAN, S., DEL SER, J. Company DE ALBUQUERQUE, V. H. C. 2021. Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey. Ieee Transactions on Neural Networks and Learning Systems, 32, 507-522.

NAIR, T., PRECUP, D., ARNOLD, D. L. Company ARBEL, T. 2020. Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation. Medical Image Analysis, 59, 10.

NIE, D., TRULLO, R., LIAN, J., WANG, L., et al. 2018. Medical Image Synthesis with Deep Convolutional Adversarial Networks. Ieee Transactions on Biomedical Engineering, 65, 2720-2730.

ORTIZ, A., MUNILLA, J., GORRIZ, J. M. Company RAMIREZ, J. 2016. Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease. International Journal of Neural Systems, 26, 23.

PEREIRA, S., PINTO, A., ALVES, V. Company SILVA, C. A. 2016. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. Ieee Transactions on Medical Imaging, 35, 1240-1251.

QIN, C., SCHLEMPER, J., CABALLERO, J., et al. 2019. Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction. Ieee Transactions on Medical Imaging, 38, 280-290.

QUAN, T. M., NGUYEN-DUC, T. Company JEONG, W. K. 2018. Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss. Ieee Transactions on Medical Imaging, 37, 1488-1497.

REHMAN, A., NAZ, S., RAZZAK, M. I., AKRAM, F. Company IMRAN, M. 2020. A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning. Circuits Systems and Signal Processing, 39, 757-775.

SAJJAD, M., KHAN, S., MUHAMMAD, K., et al. 2019. Multi-grade brain tumor classification using deep CNN with extensive data augmentation. Journal of Computational Science, 30, 174-182.

SCHLEMPER, J., CABALLERO, J., HAJNAL, J. V., PRICE, A. N. Company RUECKERT, D. 2018. A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction. Ieee Transactions on Medical Imaging, 37, 491-503.

SHARIF, M. I., LI, J. P., KHAN, M. A. Company SALEEM, M. A. 2020. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognition Letters, 129, 181-189.

SHI, J., ZHENG, X., LI, Y., ZHANG, Q. Company YING, S. H. 2018. Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease. Ieee Journal of Biomedical and Health Informatics, 22, 173-183.

TAO, Q., YAN, W. J., WANG, Y. Y., PAIMAN, et al. 2019. Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study. Radiology, 290, 81-88.

WACHINGER, C., REUTER, M. Company KLEIN, T. 2018. DeepNAT: Deep convolutional neural network for segmenting neuroanatomy. Neuroimage, 170, 434-445.

WANG, B., LEI, Y., TIAN, S. B., WANG, T. H., et al. 2019. Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation. Medical Physics, 46, 1707-1718.

YANG, G., YU, S. M., DONG, H., SLABAUGH, G., et al. 2018. DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction. Ieee Transactions on Medical Imaging, 37, 1310-1321.

此外，该统计信息还根据上述深入研究里获得的主旨词分布，通过分析方法机器DDA手绘了主旨词与深入研究政府部门的二维标量备注（备注4）。

该统计信息设法概述借助于各政府部门的主要深入研究主旨侧重。备注里数字代备注该政府部门在该主旨词上的深入研究同和。

Convolutional neural network是大多深入研究政府部门学术著作里消失同和略低于的主旨词，其里哈佛该大学、王国医学院和电弟科技该大学三所中小学的学术著作里消失该主旨词的同和略低于。

备注4 尺度求学一时期下的磁总共振显像信息核心技术主旨词与主要深入研究政府部门标量

高血压、癫痫，哪些深入研究朝著最备受瞩目？

该统计信息并用所示谱分析方法机器CiteSpace对磁总共振显像信息核心技术内的学术著作及参考资料集合顺利进行总共现分析方法及聚类分析方法后，获得了该信息核心技术主要的首选弟信息核心技术（朝著）（所示3）。可以显借助于，信息核心技术内深入研究以外度较差的11个聚类朝著如下所示：

Cluster 1 #0 prostate cancer高血压

Cluster 2 #1 autism spectrum disorder癫痫假定障碍

Cluster 3 #2 dynamic MRI高效率MRI

Cluster 4 #3 MRI-based treatment planning基于MRI病人原先

Cluster 5 #4 hybrid deep magnetic resonance fingerprinting混合尺度磁总共振相片

Cluster 6 #5 unified multi-channel classification统一多通道分类

Cluster 7 #6 neural representation大脑备注征

Cluster 8 #7 deep independence network analysis尺度独立网络分析方法

Cluster 9 #8 efficient multi-scale 3D CNN多维度3D频域人工推算机系统

Cluster 10 #9 early brain-development早期大脑生长发育

Cluster 11 #10 abdominal organ腹部骨髓

所示3 尺度求学一时期下的磁总共振显像信息核心技术总共现聚类分析方法

在上述分析方法的基础上，再并用CiteSpace对学术著作信息集及其参考资料集合顺利进行单线分析方法，并获得该信息核心技术每个都只内的主要的首选弟信息核心技术（朝著），如所示4-所示7所示。

由此，可了解该信息核心技术在将近年的首选深入研究朝著变化趋势：

所示4 尺度求学一时期下的磁总共振显像信息核心技术总共被推聚类分析方法（2013-2016）

所示5 尺度求学一时期下的磁总共振显像信息核心技术总共被推聚类分析方法（2017-2018）

所示6 尺度求学一时期下的磁总共振显像信息核心技术总共被推聚类分析方法（2019-2020）

所示7 尺度求学一时期下的磁总共振显像信息核心技术总共被推聚类分析方法（2021-2022）

AI+磁总共振深入研究信息核心技术，有哪些高独立性政府部门？

的国际合著学术著作的多少在一定程度上反映了政府部门的的国际协力程度和的国际独立性。该统计信息根据上铭信息，选取了信息核心技术刊铭较大或独立性较差的6个政府部门，共五哈佛该大学、斯坦福该大学、阿拉巴马该大学达特茅斯小学部、王国医学院、女真该大学、复旦该大学，并据此分析方法这几家American、苏格兰、里国、韩国的政府部门主要的协力取向和协力生产量。

所示8 哈佛该大学供给学术著作的主要协力政府部门

所示9 斯坦福该大学供给学术著作的主要协力政府部门

所示10 阿拉巴马该大学达特茅斯小学部供给学术著作的主要协力政府部门

所示11 王国医学院供给学术著作的主要协力政府部门

所示12 女真该大学供给学术著作的主要协力政府部门

所示13 复旦该大学供给学术著作的主要协力政府部门

以上几个政府部门的国际合著学术著作的协力伙伴，也多为的国际知名高校或大型行业，几个政府部门彼此间也常有合著学术著作，如复旦该大学与飞利浦、斯坦福该大学与哈佛该大学、阿拉巴马该大学达特茅斯小学部与上海交通该大学等。

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