[1] |
LIU X, XU H W, JIANG T, et al. MRI and 18F-FDG PET/CT findings of a giant cell tumor of the tendon sheath of the knee joint (pigmented villonodular synovitis): a case report and literature review[J]. Hell J Nucl Med, 2021, 24(2): 149-154.
|
[2] |
YANG W H. The development of ultra-high field magnetic resonance imaging[J]. Physics, 2019, 48(4): 227-236.
|
|
杨文晖. 磁共振成像发展与超高场磁共振成像技术[J]. 物理, 2019, 48(4): 227-236.
|
[3] |
WANG Y S, DENG A Q, MAO J L, et al. Automatic segmentation of knee joint synovial magnetic resonance images based on 3D VNetTrans[J]. Chinese J Magn Reson, 2022, 39(3): 303-315.
|
|
王颖珊, 邓奥琦, 毛瑾玲, 等. 基于3D VNetTrans的膝关节滑膜磁共振图像自动分割[J]. 波谱学杂志, 2022, 39(3): 303-315.
|
[4] |
PADORMO F, BEQIRI A, HAJNAL J V, et al. Parallel transmission for ultrahigh-field imaging[J]. NMR Biomed, 2016, 29(9): 1145-1161.
doi: 10.1002/nbm.3313
pmid: 25989904
|
[5] |
YETISIR F, ABACI TURK E, GUERIN B, et al. Safety and imaging performance of two-channel RF shimming for fetal MRI at 3 T[J]. Magn Reson Med, 2021, 86(5): 2810-2821.
doi: 10.1002/mrm.28895
|
[6] |
ZENG Q, GUO R, ZHENG J, et al. Impacts of RF shimming on local SAR caused by MRI 3 T birdcage coil near femoral plate implants[C]// IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, San Diego, CA, USA: IEEE, 2017: 1005-1006.
|
[7] |
GAGLIARDI V, RETICO A, BIAGI L, et al. Subject-specific knee SAR prediction using a degenerate birdcage at 7 T[C]// IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy: IEEE, 2018: 1-5.
|
[8] |
LIU W, WANG H, ZHANG P, et al. Statistical evaluation of radiofrequency exposure during magnetic resonant imaging: Application of whole-body individual human model and body motion in the coil[J]. Int J Env Res Pub He, 2019, 16(6): 1069.
doi: 10.3390/ijerph16061069
|
[9] |
STEENSMA B R, MELIADÒ E F, LUIJTEN P, et al. SAR and temperature distributions in a database of realistic human models for 7 T cardiac imaging[J]. NMR Biomed, 2021, 34(7): e4525.
doi: 10.1002/nbm.4525
pmid: 33955061
|
[10] |
CARLUCCIO G, AKGUN C, VAUGHAN J T, et al. Temperature-based MRI safety simulations with a limited number of tissues[J]. Magn Reson Med, 2021, 86(1): 543-550.
doi: 10.1002/mrm.28693
pmid: 33547673
|
[11] |
VAN DEN BERGEN B, VAN DEN BERG C A, BARTELS L W, et al. 7 T body MRI: B-1 shimming with simultaneous SAR reduction[J]. Phys Med Biol, 2007, 52(17): 5429.
doi: 10.1088/0031-9155/52/17/022
|
[12] |
DE BUCK M H, JEZZARD P, JEONG H, et al. An investigation into the minimum number of tissue groups required for 7 T in-silico parallel transmit electromagnetic safety simulations in the human head[J]. Magn Reson Med, 2021, 85(2): 1114-1122.
doi: 10.1002/mrm.v85.2
|
[13] |
WOLF S, DIEHL D, GEBHARDT M, et al. SAR simulations for high-field MRI: how much detail, effort, and accuracy is needed?[J] Magn Reson Med, 2013, 69(4): 1157-1168.
doi: 10.1002/mrm.24329
pmid: 22611018
|
[14] |
SENGAR S S, MEULENGRACHT C, BOESEN M P, et al. Multi-planar 3D knee MRI segmentation via UNet inspired architectures[J]. Int J Imag Syst Tech, 2023, 33(3): 985-998.
doi: 10.1002/ima.v33.3
|
[15] |
GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[J]. Commun Acm, 2020, 63(11): 139-144.
doi: 10.1145/3422622
|
[16] |
CHANG X, CAI X, YANG G, et al. Applications of generative adversarial networks in medical image processing[J]. Chinese J Magn Reson, 2022, 39(3): 366-380.
|
|
常晓, 蔡昕, 杨光, 等. 生成对抗网络在医学图像转换领域的应用[J]. 波谱学杂志, 2022, 39(3): 366-380.
|
[17] |
CHEN Y, SHI F, CHRISTODOULOU A G, et al. Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network[C]// Medical Image Computing and Computer Assisted Intervention-MICCAI 2018: 21st International Conference, Granada, Spain:Springer, 2018: 91-99.
|
[18] |
KANG E, KOO H J, YANG D H, et al. Cycle-consistent adversarial denoising network for multiphase coronary CT angiography[J]. Med Phys, 2019, 46(2): 550-562.
doi: 10.1002/mp.13284
pmid: 30449055
|
[19] |
LAHIRI A, AYUSH K, KUMAR BISWAS P, et al. Generative adversarial learning for reducing manual annotation in semantic segmentation on large scale miscroscopy images: Automated vessel segmentation in retinal fundus image as test case[C]// Proceedings of the IEEE conference on computer vision and pattern recognition workshops, Honolulu, HI, USA: IEEE, 2017: 42-48.
|
[20] |
GAJ S, YANG M, NAKAMURA K, et al. Automated cartilage and meniscus segmentation of knee MRI with conditional generative adversarial networks[J]. Magn Reson Med, 2020, 84(1): 437-449.
doi: 10.1002/mrm.28111
pmid: 31793071
|
[21] |
ZHANG L, GOOYA A, FRANGI A F. Semi-supervised assessment of incomplete LV coverage in cardiac MRI using generative adversarial nets[C]// International Workshop on Simulation and Synthesis in Medical Imaging. Cham: Springer, 2017: 61-68.
|
[22] |
MIRZA M, OSINDERO S. Conditional generative adversarial nets[J]. arXiv preprint arXiv:1411.1784, 2014.
|
[23] |
MA Y, XING C J, XIAO L. Knee joint image segmentation and model construction based on cascaded network[J]. Chinese J Magn Reson, 2022, 39(2): 184-195.
|
|
马岩, 邢藏菊, 肖亮. 基于级联网络的膝关节图像分割与模型构建[J]. 波谱学杂志, 2022, 39(2): 184-195.
|
[24] |
SCHLEMPER J, OKTAY O, SCHAAP M, et al. Attention gated networks: Learning to leverage salient regions in medical images[J]. Med Image Anal, 2019, 53197-207.
|
[25] |
LEE J, GEBHARDT M, WALD L L, et al. Local SAR in parallel transmission pulse design[J]. Magn Reson Med, 2012, 67(6): 1566-1578.
doi: 10.1002/mrm.23140
pmid: 22083594
|
[26] |
MILSHTEYN E, GURYEV G, TORRADO-CARVAJAL A, et al. Individualized SAR calculations using computer vision-based MR segmentation and a fast electromagnetic solver[J]. Magn Reson Med, 2021, 85(1): 429-443
doi: 10.1002/mrm.v85.1
|
[27] |
HARDY B M, BANIK R, YAN X Q, et al. Bench to bore ramifications of inter-subject head differences on RF shimming and specific absorption rates at 7 T[J]. Magn Reson Imaging, 2022, 92: 187-196.
doi: 10.1016/j.mri.2022.07.009
|
[28] |
CHRIST A, KAINZ W, HAHN E G, et al. The Virtual Family—development of surface-based anatomical models of two adults and two children for dosimetric simulations[J]. Phys Med Biol, 2009, 55(2): N23.
doi: 10.1088/0031-9155/55/2/N01
|