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

磁共振成像（Magnetic Resonance Imaging, MRI）是现今临床医学诊断的重要手段之一，但如何提高成像技术、应用场景等成为该领域近来的热门研究问题。

随着人工智能及机器学习的发展，深度学习方法被应用于医学图像处理中，同时也使磁共振成像在新的时代背景下有了新的发展动力。

近日，上海科技大学图书信息中心发布了一篇题为《深度学习时代下的磁共振成像》的研究报告。

该报告检索2013年至今近十年发表的SCI期刊论文作为分析对象，考察该领域的研究发展态势和变化趋势。

该报告根据磁共振成像及深度学习的语义特征、相关的关键词，设计了检索式：主题=(“Magnetic Resonance Imag*” OR MRI) AND “deep learn*”，时间范围为2013-2022年，同时限制文献类型为Article。

报告的数据源来自Web of Science数据库中的SCI索引集、InCites数据库。

其中，在Web of Science数据库SCI索引集中，共检索到2013-2022年间发表的该主题共计3254篇研究论文。

论文数量进入爆发增长期

深度学习时代下的磁共振成像领域在2013-2021间每年的SCI论文产出数量是逐年递增的。

2013-2015年文章数量还处于零星增长阶段，2016-2017年发文量增长明显，但仍相对为低位，而从2018年起的近五年里则呈现了爆发增长的趋势，并且增长率越来越高。

2022年度，截至检索日期（2022-06-27）共发文674篇。

图1 深度学习时代下的磁共振成像领域的SCI论文产出趋势

磁共振成像领域的主要科研论文产出机构集中在美国、中国、欧洲、韩国，该报告根据SCI一区论文占比（%Q1）、ESI高被引论文占比（%HCP）、标准化引文影响力（CNCI）、篇均被引、国际合作论文占比（%国际合作）等几项指标，将发表论文数量前50名的机构整理成表。

其中，帝国理工学院无论是高被引论文占比、CNCI或是篇均被引，都是领域内最高水平。

此外，北卡罗来纳大学教堂山分校、高丽大学、纽约大学、伦敦大学学院等也表现出了很高的水平。

表1 深度学习时代下的磁共振成像领域主要的SCI论文产出机构及影响力

报告中同样按照以上几项指标，将发表论文最多、最为突出的数十位科学家进行了汇总整理。

其中，任职于上海科技大学/联影智能（原任职于北卡罗来纳大学教堂山分校）的沈定刚教授贡献最为突出，研究成果最多并且各指标均处于很高的水平。

美国埃默里大学的Yang Xiaofeng副教授，Liu Tian副教授、Wang Tonghe助理教授、Lei Yang老师等都较大程度地贡献了该领域的论文产出。

来自帝国理工学院的Rueckert, Daniel教授，在各影响力指标上都处于所分析对象中的最高数值。

而韩国科学技术院Ye, Jong Chul教授在影响力指标上也有非常明显的优势。

表2 深度学习时代下的磁共振成像领域主要的SCI论文产出机构及影响力

AI+磁共振成像的研究主题是什么？

为了解该领域近年的研究中主要的主题分布情况，该报告以上文中提及的数据源为基础，通过图谱构建工具VOSviewer提取了论文的关键词，并设置了出现频次在10以上的关键词集合通过Co-occurrence共现网络计算获得了图2。

报告尝试从这张主题分布图中揭示出该领域研究主题的研究高地（出现频次多的主题词）分布。

图2 深度学习时代下的磁共振成像领域主题词共现分布图

表3 深度学习时代下的磁共振成像领域各集合主题词及其热度指数

在该报告所引用的数据源中，ESI数据库提供了研究前沿（Research Fronts）数据，通过对最近5年内引用率最高的论文之间进行共被引聚类，识别由这些共被引论文聚类形成的集合，将这些集合定义了研究前沿RF。

ESI数据库揭示了10000个左右的RF，每个RF包含数量不等的高被引论文。进入RF的论文，在一定程度上体现了科学家和研究机构对研究的创新性和对前沿的贡献。

根据比对，以下39篇论文为该领域中的研究前沿论文：

AGGARWAL, H. K., MANI, M. P. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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. & 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.