胎儿脑核磁共振图像处理技术进展

doi:10.3778/j.issn.1673-9418.2501023

摘要/Abstract

摘要： 胎儿脑核磁共振成像技术因其无创、无辐射和高软组织对比度，已成为评估胎儿大脑发育和诊断先天性脑异常的重要工具。高质量的胎儿脑核磁共振图像在临床诊疗和胎儿脑发育等科学研究方面发挥着重要作用。图像处理技术可提升胎儿脑核磁共振图像质量，满足诊断与研究需求，故其在胎儿脑核磁共振图像领域的研究具有重要意义。对胎儿脑结构及其核磁共振图像数据集进行简要介绍，并对图像质量评价、图像配准、图像去噪、图像偏差场校正、图像去伪影及超分辨率重建六个方面的技术进行阐述。阐述了图像处理技术应用于胎儿脑核磁共振图像的重要性；介绍了胎儿脑结构及其核磁共振图像数据集；分别就六个方面的图像处理技术进行详细介绍，系统地阐述了不同技术的国内外研究现状，对不同方法的性能进行比较与分析，并对已取得的成果与面临的挑战进行了小结；从技术、临床应用等角度探讨了胎儿脑核磁共振图像处理领域存在的问题和未来的研究方向。

关键词: 胎儿脑, 核磁共振图像, 图像处理, 深度学习

Abstract: Fetal brain MRI, due to its non-invasiveness, absence of radiation, and high soft-tissue contrast, has become an important tool for assessing fetal brain development and diagnosing congenital brain abnormalities. High-quality fetal brain MR images play an important role in clinical diagnosis, treatment, and scientific research of fetal brain development. Image processing techniques can enhance the quality of fetal brain MR images, meeting the requirements for diagnosis and research. Thus, the studies in this field hold significant importance. This paper provides a brief introduction to fetal brain structure and its MR image datasets, and elaborates on six techniques, including image quality assessment, image registration, image denoising, image bias field correction, image artifact correction, and super-resolution reconstruction. Firstly, the importance of image processing technologies for fetal brain MR images is presented. Subsequently, the structure of the fetal brain and its MR image datasets are introduced. Then the six image processing techniques are introduced respectively. The research status both at home and abroad is systematically described. The performance of different methods is compared and analyzed. And the current achievements and challenges are summarized respectively. Finally, the existing issues and future research directions in the field of fetal brain MR image processing are discussed from the perspectives of technology and clinical application.

Key words: fetal brain, magnetic resonance image, image processing, deep learning

刘梦宇, 罗琴, 姚雄, 王健华, 陈健. 胎儿脑核磁共振图像处理技术进展[J]. 计算机科学与探索, 2025, 19(11): 2895-2912.

LIU Mengyu, LUO Qin, YAO Xiong, WANG Jianhua, CHEN Jian. Progress in Fetal Brain Magnetic Resonance Image Processing Technologies[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(11): 2895-2912.

参考文献

[1] MAKROPOULOS A, COUNSELL S J, RUECKERT D. A review on automatic fetal and neonatal brain MRI segmentation[J]. NeuroImage, 2018, 170: 231-248.
[2] 李胜利, 文华轩, 田晓先. 胎儿颅脑超声检查: 诊断思维[J]. 中华医学超声杂志(电子版), 2015, 12(8): 590-598.
LI S L, WEN H X, TIAN X X. Ultrasonic examination of fetal brain: diagnostic thinking[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2015, 12(8): 590-598.
[3] 冯欢, 李丽. 发生胎儿窘迫高危因素预测及治疗的研究进展[J]. 医学诊断, 2024, 14(4): 420-425.
FENG H, LI L. Research progress on prediction and treatment of risk factors for fetal distress[J]. Medical Diagnosis, 2024, 14(4): 420-425.
[4] WRIGHT I C, RABE-HESKETH S, WOODRUFF P W, et al. Meta-analysis of regional brain volumes in schizophrenia[J]. The American Journal of Psychiatry, 2000, 157(1): 16-25.
[5] MURRAY R M, JONES P, O??CALLAGHAN E. Fetal brain development and later schizophrenia[J]. Ciba Foundation Symposium, 1991, 156: 155-163.
[6] ROZA S J, GOVAERT P P, VROOMAN H A, et al. Foetal growth determines cerebral ventricular volume in infants[J]. NeuroImage, 2008, 39(4): 1491-1498.
[7] PALMEN S J M C, HULSHOFF POL H E, KEMNER C, et al. Increased gray-matter volume in medication-naive high-functioning children with autism spectrum disorder[J]. Psychological Medicine, 2005, 35(4): 561-570.
[8] MOVSAS T Z, PINTO-MARTIN J A, WHITAKER A H, et al. Autism spectrum disorder is associated with ventricular enlargement in a low birth weight population[J]. The Journal of Pediatrics, 2013, 163(1): 73-78.
[9] LIMPEROPOULOS C. Disorders of the fetal circulation and the fetal brain[J]. Clinics in Perinatology, 2009, 36(3): 561-577.
[10] 华益民, 周开宇. 胎儿心力衰竭的准确评估及治疗[J]. 临床儿科杂志, 2011, 29(7): 609-612.
HUA Y M, ZHOU K Y. Evaluation and treatment of fetal heart failure[J]. Journal of Clinical Pediatrics, 2011, 29(7): 609-612.
[11] PAYETTE K, DE DUMAST P, KEBIRI H, et al. An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset[J]. Scientific Data, 2021, 8: 167.
[12] FAGHIHPIRAYESH R, KARIMI D, ERDO?MU? D, et al. Deep learning framework for real-time fetal brain segmentation in MRI[C]//Proceedings of the 7th International Workshop on Perinatal, Preterm and Paediatric Image Analysis. Cham: Springer, 2022: 60-70.
[13] 苏宝兰. 三点定位法用于胎儿头颅磁共振扫描对图像质量的影响[J]. 医学理论与实践, 2025, 38(1): 117-120.
SU B L. The impact of the three-point positioning method on image quality in fetal cranial magnetic resonance imaging[J]. The Journal of Medical Theory and Practice, 2025, 38(1): 117-120.
[14] JAFARI-KHOUZANI K. MRI upsampling using feature-based nonlocal means approach[J]. IEEE Transactions on Medical Imaging, 2014, 33(10): 1969-1985.
[15] RUEDA A, MALPICA N, ROMERO E. Single-image super-resolution of brain MR images using overcomplete dictionaries[J]. Medical Image Analysis, 2013, 17(1): 113-132.
[16] LARGENT A, KAPSE K, BARNETT S D, et al. Image quality assessment of fetal brain MRI using multi-instance deep learning methods[J]. Journal of Magnetic Resonance Imaging, 2021, 54(3): 818-829.
[17] LI G, WANG L, YAP P T, et al. Computational neuroanatomy of baby brains: a review[J]. NeuroImage, 2019, 185: 906-925.
[18] 施俊, 汪琳琳, 王珊珊, 等. 深度学习在医学影像中的应用综述[J]. 中国图象图形学报, 2020, 25(10): 1953-1981.
SHI J, WANG L L, WANG S S, et al. Applications of deep learning in medical imaging: a survey[J]. Journal of Image and Graphics, 2020, 25(10): 1953-1981.
[19] KONKEL L. The brain before birth: using fMRI to explore the secrets of fetal neurodevelopment[J]. Environmental Health Perspectives, 2018, 126(11): 112001.
[20] 马立霜, 刘超, 冯众. 先天性结构畸形产前产后一体化诊断与治疗模式[J]. 临床小儿外科杂志, 2023, 22(8): 701-705.
MA L S, LIU C, FENG Z. Integrated prenatal and postnatal managements of congenital structural malformations[J]. Journal of Clinical Pediatric Surgery, 2023, 22(8): 701-705.
[21] TORRES H R, MORAIS P, OLIVEIRA B, et al. A review of image processing methods for fetal head and brain analysis in ultrasound images[J]. Computer Methods and Programs in Biomedicine, 2022, 215: 106629.
[22] 中华预防医学会出生缺陷预防与控制专业委员会产前超声诊断学组, 中华医学会超声医学分会妇产学组. 胎儿生物学参数超声参考值专家共识: 双顶径、头围、腹围和股骨长[J]. 中华医学超声杂志（电子版）, 2024, 21(8): 757-761.
Prenatal Ultrasound Diagnosis Group of Committee for Birth Defect Prevention and Control of Chinese Association of Preventive Medicine, Gynecology Group of Chinese Society of Ultrasound Medicine of Chinese Medical Association. Expert consensus on ultrasound reference values of fetal biological parameters: biparietal diameter, head circumference, abdominal circumference, and femur length[J]. Chinese Journal of Medical Ultrasound (Electronic Edition),2024, 21(8): 757-761.
[23] 姜雨汀, 谢红宁. 国际妇产超声学会(ISUOG)实践指南(更新版)解读: 胎儿中枢神经系统超声检查(第一部分和第二部分)[J]. 中华超声影像学杂志, 2021(7): 553-562.
JIANG Y T, XIE H N. Interpretation of ISUOG practice guidelines(updated): sonographic examination of the fetal central nervous system(part 1 and part 2)[J]. Chinese Journal of Ultrasonography, 2021(7): 553-562.
[24] 袁敏, 张阳, 肖喜荣. 小头畸形产前诊断与管理的研究进展[J]. 复旦学报(医学版), 2022, 49(2): 270-276.
YUAN M, ZHANG Y, XIAO X R. Progress on prenatal diagnosis and management of microcephaly[J]. Fudan University Journal of Medical Sciences, 2022, 49(2): 270-276.
[25] CICERI T, CASARTELLI L, MONTANO F, et al. Fetal brain MRI atlases and datasets: a review[J]. NeuroImage, 2024, 292: 120603.
[26] 杲悦, 何雪, 林祥涛, 等. 10孕周胎儿标本脑发育结构9.4 T MRI表现与组织病理学特征的对照研究[J]. 中华解剖与临床杂志, 2023, 28(11): 697-702.
GAO Y, HE X, LIN X T, et al. Mapping fetal specimens brain development of 10-weeks gestational age with 9.4 T magnetic resonance imaging and histopathological features[J]. Chinese Journal of Anatomy and Clinics, 2023, 28(11): 697-702.
[27] 汪华, 汪龙霞. 胎儿大脑皮质发育的超声和磁共振成像研究进展[J]. 中华超声影像学杂志, 2015(11): 1009-1012.
WANG H, WANG L X. The research progress of ultrasound and magnetic resonance imaging in fetal cerebral cortical development[J]. Chinese Journal of Ultrasonography, 2015(11): 1009-1012.
[28] 佘佳燕, 陈宇婕, 宁刚. 胎儿侧脑室扩张的产前诊断及预后评估[J]. 中华放射学杂志, 2023, 57(5): 576-580.
SHE J Y, CHEN Y J, NING G. Prenatal diagnosis and prognostic evaluation of fetal ventriculomegaly[J]. Chinese Journal of Radiology, 2023, 57(5): 576-580.
[29] 宁刚. 胎儿MRI：出生缺陷产前影像学诊断的生力军[J]. 中华妇幼临床医学杂志（电子版）, 2021, 17(3): 257-261.
NING G. Fetal MRI: plays a key role in prenatal imaging diagnosis of birth defects[J]. Chinese Journal of Obstetrics & Gynecology and Pediatrics (Electronic Edition), 2021, 17(3): 257-261.
[30] PAYETTE K, LI H B, DE DUMAST P, et al. Fetal brain tissue annotation and segmentation challenge results[J]. Medical Image Analysis, 2023, 88: 102833.
[31] SHEN L, ZHENG J, SHPANSKAYA K, et al. Fetal brain MRI from Stanford Lucile Packard Children’s Hospital[EB/OL]. [2024-11-06]. https://arxiv.org/abs/1812.07102.
[32] URRU A, NAKAKI A, BENKARIM O, et al. An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis[J]. Computer Methods and Programs in Biomedicine, 2023, 230: 107334.
[33] GHOLIPOUR A, ROLLINS C K, VELASCO-ANNIS C, et al. A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth[J]. Scientific Reports, 2017, 7: 476.
[34] LALA S. Convolutional neural networks for image reconstruction and image quality assessment of 2D fetal brain MRI[D]. Cambridge: Massachusetts Institute of Technology, 2019.
[35] XU J S, LALA S, GAGOSKI B, et al. Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency[C]//Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. Cham: Springer, 2020: 386-395.
[36] GAGOSKI B, XU J S, WIGHTON P, et al. Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T[J]. Magnetic Resonance in Medicine, 2022, 87(4): 1914-1922.
[37] ZHANG W H, ZHANG X, LI L Y, et al. A joint brain extraction and image quality assessment framework for fetal brain MRI slices[J]. NeuroImage, 2024, 290: 120560.
[38] XU J. Online, low-latency decision making for fetal magnetic resonance imaging with machine learning[D]. Cambridge: Massachusetts Institute of Technology, 2020.
[39] 马露凡, 罗凤, 严江鹏, 等. 深度医学图像配准研究进展: 迈向无监督学习[J]. 中国图象图形学报, 2021, 26(9): 2037-2057.
MA L F, LUO F, YAN J P, et al. Deep-learning based medical image registration pathway: towards unsupervised learning[J]. Journal of Image and Graphics, 2021, 26(9): 2037-2057.
[40] ROUSSEAU F, GLENN O, IORDANOVA B, et al. A novel approach to high resolution fetal brain MR imaging[C]//Proceedings of the 2005 International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Heidelberg: Springer, 2005: 548-555.
[41] ROUSSEAU F, GLENN O A, IORDANOVA B, et al. Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images[J]. Academic Radiology, 2006, 13(9): 1072-1081.
[42] ALANSARY A, RAJCHL M, MCDONAGH S G, et al. PVR: patch-to-volume reconstruction for large area motion correction of fetal MRI[J]. IEEE Transactions on Medical Imaging, 2017, 36(10): 2031-2044.
[43] EBNER M, WANG G T, LI W Q, et al. An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI[J]. NeuroImage, 2020, 206: 116324.
[44] TOURBIER S, VELASCO-ANNIS C, TAIMOURI V, et al. Automated template-based brain localization and extraction for fetal brain MRI reconstruction[J]. NeuroImage, 2017, 155: 460-472.
[45] CORDERO-GRANDE L, ORTUNO-FISAC J E, DEL HOYO A A, et al. Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration[J]. IEEE Transactions on Medical Imaging, 2023, 42(3): 810-822.
[46] KIM K, HABAS P A, ROUSSEAU F, et al. Intersection based motion correction of multislice MRI for 3-D in utero fetal brain image formation[J]. IEEE Transactions on Medical Imaging, 2010, 29(1): 146-158.
[47] XU J S, MOYER D, GRANT P E, et al. SVoRT: iterative transformer for slice-to-volume registration in fetal brain MRI[C]//Proceedings of the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention. Cham: Springer, 2022: 3-13.
[48] TAN J P, ZHANG X, LV Y, et al. Self-supervised fetal MRI 3D reconstruction based on radiation diffusion generation model[EB/OL]. [2024-11-06]. https://arxiv.org/abs/2310.10209.
[49] COMTE V, ALENYA M, URRU A, et al. Deep cascaded registration and weakly-supervised segmentation of fetal brain MRI[J]. Heliyon, 2025, 11(1): e40148.
[50] HOU B, ALANSARY A, MCDONAGH S, et al. Predicting slice-to-volume transformation in presence of arbitrary subject motion[C]//Proceedings of the 2017 International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2017: 296-304.
[51] MA L F, LIN W L, ZHANG H, et al. Automated fetal brain volume reconstruction from motion-corrupted stacks with deep learning[C]//Proceedings of the 2024 IEEE International Symposium on Biomedical Imaging. Piscataway: IEEE, 2024: 1-5.
[52] 张冉, 王雷, 夏威, 等. 2D/3D图像配准中的相似性测度和优化算法[J]. 激光与红外, 2014, 44(1): 98-102.
ZHANG R, WANG L, XIA W, et al. Comparison of similarity measurement and optimization methods in 2D/3D image registration[J]. Laser & Infrared, 2014, 44(1): 98-102.
[53] FERRANTE E, PARAGIOS N. Slice-to-volume medical image registration: a survey[J]. Medical Image Analysis, 2017, 39: 101-123.
[54] 张明娜, 吕晓琪, 谷宇. 残差混合注意力结合多分辨率约束的图像配准[J]. 光学精密工程, 2022, 30(10): 1203-1216.
ZHANG M N, LV X Q, GU Y. Image registration based on residual mixed attention and multi-resolution constraints[J]. Optics and Precision Engineering, 2022, 30(10): 1203-1216.
[55] ZHANG X B. Improved non-local means using structural similarity for image denoising[J]. Circuits, Systems, and Signal Processing, 2025, 44(4): 2706-2736.
[56] ROUSSEAU F, OUBEL E, PONTABRY J, et al. BTK: an open-source toolkit for fetal brain MR image processing[J]. Computer Methods and Programs in Biomedicine, 2013, 109(1): 65-73.
[57] ROUSSEAU F, GOUNOT D, STUDHOLME C. On high-resolution image estimation using low-resolution brain MRI[C]//Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway: IEEE, 2013: 1081-1084.
[58] JI L X, HENDRIX C L, THOMASON M E. Empirical evaluation of human fetal fMRI preprocessing steps[J]. Network Neuroscience, 2022, 6(3): 702-721.
[59] BILLOT B, MOYER D, KARANI N, et al. Equivariant and denoising CNNs to decouple intensity and spatial features for motion tracking in fetal brain MRI[C]//Proceedings of the 6th International Conference on Medical Imaging with Deep Learning, 2023.
[60] BILLOT B, DEY N, MOYER D, et al. SE(3)-equivariant and noise-invariant 3D rigid motion tracking in brain MRI[J]. IEEE Transactions on Medical Imaging, 2024, 43(11): 4029-4040.
[61] DE NEGREIROS A C S V, GIRALDI G, WERNER H, et al. Self-supervised image denoising methods: an application in fetal MRI[C]//Proceedings of the Anais do XVIII Workshop de Vis?o Computacional, 2023: 137-141.
[62] LEHTINEN J, MUNKBERG J, HASSELGREN J, et al. Noise2Noise: learning image restoration without clean data[EB/OL]. [2024-11-06]. https://arxiv.org/abs/1803.04189.
[63] KRULL A, BUCHHOLZ T O, JUG F. Noise2Void-learning denoising from single noisy images[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 2124-2132.
[64] FERRAZZI G, KUKLISOVA MURGASOVA M, ARICHI T, et al. Resting state fMRI in the moving fetus: a robust framework for motion, bias field and spin history correction[J]. NeuroImage, 2014, 101: 555-568.
[65] SESHAMANI S, CHENG X, FOGTMANN M, et al. A method for handling intensity inhomogenieties in fMRI sequences of moving anatomy of the early developing brain[J]. Medical Image Analysis, 2014, 18(2): 285-300.
[66] KIM K, HABAS P, RAJAGOPALAN V, et al. Non-iterative relative bias correction for 3D reconstruction of in utero fetal brain MR imaging[C]//Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology. Piscataway: IEEE, 2010: 879-882.
[67] TAYMOURTASH A, SCHWARTZ E, NENNING K H, et al. Quantifying residual motion artifacts in fetal fMRI data[C]//Proceedings of the 1st International Workshop on Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis. Cham: Springer, 2019: 171-180.
[68] NI Q, ZHANG Y, WEN T X, et al. A sparse volume reconstruction method for fetal brain MRI using adaptive kernel regression[J]. BioMed Research International, 2021(1): 6685943.
[69] SOBOTKA D, EBNER M, SCHWARTZ E, et al. Motion correction and volumetric reconstruction for fetal functional magnetic resonance imaging data[J]. NeuroImage, 2022, 255: 119213.
[70] LIM A, LO J, WAGNER M W, et al. Automatic artifact detection algorithm in fetal MRI[J]. Frontiers in Artificial Intelligence, 2022, 5: 861791.
[71] LIM A, LO J, WAGNER M W, et al. Motion artifact correction in fetal MRI based on a generative adversarial network method[J]. Biomedical Signal Processing and Control, 2023, 81: 104484.
[72] SANTOS í M F, GIRALDI G A, JUNIOR H W, et al. Fetal MRI artifacts: semi-supervised generative adversarial neural network for motion artifacts reducing in fetal magnetic resonance images[J]. Journal of Computer and Communications, 2024, 12(6): 210-225.
[73] WANG L, NIE D, LI G N, et al. Benchmark on automatic six-month-old infant brain segmentation algorithms: the iSeg-2017 challenge[J]. IEEE Transactions on Medical Imaging, 2019, 38(9): 2219-2230.
[74] ZHANG L H, LI Y W, ZHOU X Y, et al. Transcending the limit of local window: advanced super-resolution transformer with adaptive token dictionary[C]//Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2024: 2856-2865.
[75] KAINZ B, STEINBERGER M, WEIN W, et al. Fast volume reconstruction from motion corrupted stacks of 2D slices[J]. IEEE Transactions on Medical Imaging, 2015, 34(9): 1901-1913.
[76] VAN REETH E, THAM I W K, TAN C H, et al. Super-resolution in magnetic resonance imaging: a review[J]. Concepts in Magnetic Resonance Part A, 2012, 40A(6): 306-325.
[77] 成云凤, 汪伟. 基于医学图像的超分辨率重建算法综述[J]. 北京生物医学工程, 2019, 38(5): 535-543.
CHENG Y F, WANG W. Review of super-resolution reconstruction algorithms in medical image[J]. Beijing Biomedical Engineering, 2019, 38(5): 535-543.
[78] 冉文兵, 梁永超, 覃芹, 等. 基于生成对抗网络和注意力机制的医学图像超分辨率重建[J]. 智能计算机与应用, 2023, 13(1): 136-141.
RAN W B, LIANG Y C, QIN Q, et al. Super resolution reconstruction of medical images based on generative adversarial network and attention mechanism[J]. Intelligent Computer and Applications, 2023, 13(1): 136-141.
[79] BUADES A, COLL B, MOREL J M. A review of image denoising algorithms, with a new one[J]. Multiscale Modeling & Simulation, 2005, 4(2): 490-530.
[80] JIANG S Z, XUE H, GLOVER A, et al. MRI of moving subjects using multislice snapshot images with volume reconstruction (SVR): application to fetal, neonatal, and adult brain studies[J]. IEEE Transactions on Medical Imaging, 2007, 26(7): 967-980.
[81] GHOLIPOUR A, ESTROFF J A, WARFIELD S K. Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain MRI[J]. IEEE Transactions on Medical Imaging, 2010, 29(10): 1739-1758.
[82] KUKLISOVA-MURGASOVA M, QUAGHEBEUR G, RUTHERFORD M A, et al. Reconstruction of fetal brain MRI with intensity matching and complete outlier removal[J]. Medical Image Analysis, 2012, 16(8): 1550-1564.
[83] LAJOUS H, HILBERT T, ROY C W, et al. Simulated half-Fourier acquisitions single-shot turbo spin echo (HASTE) of the fetal brain: application to super-resolution reconstruction[C]//Proceedings of the 2021 International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. Cham: Springer, 2021: 157-167.
[84] SHI W, XU H A, SUN C, et al. AFFIRM: affinity fusion-based framework for iteratively random motion correction of multi-slice fetal brain MRI[J]. IEEE Transactions on Medical Imaging, 2023, 42(1): 209-219.
[85] MCDONAGH S, HOU B, ALANSARY A, et al. Context-sensitive super-resolution for fast fetal magnetic resonance imaging[C]//Proceedings of the 2017 International Workshop on Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment. Cham: Springer, 2017: 116-126.
[86] XU J S, ABACI TURK E, GRANT P E, et al. STRESS: super-resolution for dynamic fetal MRI using self-supervised learning[C]//Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention. Cham: Springer, 2021: 197-206.
[87] SONG L Y, WANG Q, LIU T, et al. Deep robust residual network for super-resolution of 2D fetal brain MRI[J]. Scientific Reports, 2022, 12: 406.
[88] L?CHLER K, LAJOUS H, UNSER M, et al. Self-supervised isotropic superresolution fetal brain MRI[C]//Proceedings of the 2023 IEEE 20th International Symposium on Biomedical Imaging. Piscataway: IEEE, 2023: 1-5.
[89] 钟梦圆, 姜麟. 超分辨率图像重建算法综述[J]. 计算机科学与探索, 2022, 16(5): 972-990.
ZHONG M Y, JIANG L. Review of super-resolution image reconstruction algorithms[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(5): 972-990.
[90] 赵頔. 基于插值和邻域嵌入的图像超分辨率算法研究[D]. 金华: 浙江师范大学, 2017.
ZHAO D. Research on image super-resolution algorithms through interpolation and neighbor embedding[D]. Jinhua: Zhejiang Normal University, 2017.
[91] 史振威, 雷森. 图像超分辨重建算法综述[J]. 数据采集与处理, 2020, 35(1): 1-20.
SHI Z W, LEI S. Review of image super-resolution reconstruction[J]. Journal of Data Acquisition and Processing, 2020, 35(1): 1-20.
[92] LI Y, SIXOU B, PEYRIN F. A review of the deep learning methods for medical images super resolution problems[J]. IRBM, 2021, 42(2): 120-133.
[93] 王宽全, 梁栋, 王玮, 等. 一种基于深度解耦网络的MRI偏置场修正方法及系统: 202310846071[P]. 2024-05-14.
WANG K Q, LIANG D, WANG W, et al. A MRI bias field correction method and system based on deep decoupling network: 202310846071[P]. 2024-05-14.
[94] SLED J G, ZIJDENBOS A P, EVANS A C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data[J]. IEEE Transactions on Medical Imaging, 1998, 17(1): 87-97.
[95] ZAITSEV M, MACLAREN J, HERBST M. Motion artifacts in MRI: a complex problem with many partial solutions[J]. Journal of Magnetic Resonance Imaging, 2015, 42(4): 887-901.
[96] SINGH A, SALEHI S S M, GHOLIPOUR A. Deep predictive motion tracking in magnetic resonance imaging: application to fetal imaging[J]. IEEE Transactions on Medical Imaging, 2020, 39(11): 3523-3534.
[97] KRICHEN M. Generative adversarial networks[C]//Proceedings of the 14th International Conference on Computing Communication and Networking Technologies. Piscataway: IEEE, 2023: 1-7.
[98] MAO Y Q, XU N, WU Y N, et al. Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images[J]. Cell Reports Medicine, 2025, 6(3): 101988.
[99] 中国信息通信研究院. 人工智能大模型赋能医疗健康产业白皮书[EB/OL]. (2023-10-31)[2025-03-22]. http://aimd.org.cn/newsinfo/6513785.html?templateId=506998.
China Academy of Information and Communications Technology. White paper on empowering the healthcare industry with large-scale AI models[EB/OL]. (2023-10-31)[2025-03-22]. http://aimd.org.cn/newsinfo/6513785.html?templateId=506998.