Welcome to Francis Academic Press

Academic Journal of Medicine & Health Sciences, 2022, 3(3); doi: 10.25236/AJMHS.2022.030309.

A 3D printing method of customized magnetic focusing generator for magnetic field therapy


Chao Yang1, Yunhan Tian2, Jinhua Yu1, Minde Jin2, Ning Xie2, Yinhui Deng1,3

Corresponding Author:
Yinhui Deng

1Biomedical Engineering Center, School of Information Science and Technology, Fudan University, Shanghai, 200433, China

2Shanghai Fuzhi Information Technology Co. Ltd., Shanghai, 200438, China

3Shanghai Mingge Information Technology Co. Ltd., Shanghai, 200438, China


As one of the most malignant tumors, brain tumors seriously threaten people's health. Traditional treatment methods, such as surgery combined with radiotherapy and chemotherapy, will bring great side effects and high treatment costs. As a new type of brain tumor treatment method, magnetic field therapy has the advantages of well effectiveness, non-invasive property and relatively low treatment cost. However, it has the disadvantage that magnetic field energy cannot effectively focus on tumors, especially when facing the differences of brain tumors among different patients. This may lead to side effects similar to those caused by traditional chemotherapy methods that cannot focus on tumors. For this, a 3D printing method of customized magnetic focusing generator for magnetic field therapy is proposed in the paper. First, a state-of-the-art 3D-Unet artificial intelligence model is used to segment the magnetic resonance imaging (MRI) data of patients to obtain the spatial information of brain tumors and personalized patient skulls. Then, through the design of the hemispherical solenoid array model, the multi-dimensional simulation and actual measurement of the magnetic focusing mode are carried out to obtain a solenoid focusing scheme that gives consideration to both magnetic focusing and energy efficiency. Finally, based on high-precision 3D printing technology, 3D reconstruction is carried out for the spatial information of brain tumors and personalized patient skulls with the solenoid focusing scheme. The magnetic field generator is thus obtained based on customized 3D printing. The device not only fits the patient's skull and is suitable for wearing, but also can accurately control the spatial information related to the tumor, so that the magnetic field energy can accurately focus on the brain tumor area of specific patients to achieve accurate magnetic field treatment. Through the experiment, the actual measurement of the magnetic field distribution generated by the device is carried out and compared with the simulation results. While the magnetic focusing is verified, the error between the measurement and simulation reaches only 2.2%, which verifies the feasibility of the overall method. The customized magnetic focusing generator proposed in the paper is expected to provide a new and effective clinical approach for the treatment of brain tumors.


Brain tumor, Magnetic focusing, Artificial intelligence tumor segmentation, Solenoid array, 3D printing

Cite This Paper

Chao Yang, Yunhan Tian, Jinhua Yu, Minde Jin, Ning Xie, Yinhui Deng. A 3D printing method of customized magnetic focusing generator for magnetic field therapy. Academic Journal of Medicine & Health Sciences (2022) Vol. 3, Issue 3: 44-53. https://doi.org/10.25236/AJMHS.2022.030309.


[1] T. Weber, G.J. Cerilli, Inhibition of tumor growth by the use of non-homogeneous magnetic fields, Cancer, 28(1971) 340-343.

[2] C.F. Martino, L. Portelli, K. McCabe, et al., Reduction of the Earth's magnetic field inhibits growth rates of model cancer cell lines, Bioelectromagnetics, 31(2010) 649-655.

[3] I.V. Ulasov, H. Foster, M. Butters, et al., Precision knockdown of EGFR gene expression using radio frequency electromagnetic energy, Journal of Neuro-Oncology, 133(2017) 257-264.

[4] S. Ueno, T. Matsuda, M. Fujiki. Functional mapping of the human motor cortex obtained by focal and vectorial magnetic stimulation of the brain, IEEE Transactions on Magnetics, 26(1990) 1539-1544.

[5] B.J. Roth, P.J. Maccabee, L.P. Eberle, et al., In vitro evaluation of a 4-leaf coil design for magnetic stimulation of peripheral nerve, Electroencephalography and Clinical Neurophysiology Evoked Potentials Section, 93(1994) 68-74.

[6] X. Wang, W. Hu, Y. Yang, et al., Study on focusing of coil induced electric field in magnetic stimulation, Beijing biomedical engineering, 1(2005) 33-35.

[7] J. Liu, K. Huang, W. Hua, Design of magnetic focusing coil array and calculation of field distribution based on genetic algorithm, Journal of Chengdu University of Technology (Natural Science Edition), 4(2004) 412-416.

[8] M. Yan, C. Jiang, C. Yao, et al., Development of focusing magnetic field coil in pulsed magnetic field generator for tumor treatment, High Voltage Technology, 39(2013) 141-148.

[9] J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2015, 3431-3440.

[10] O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, Cham, 2015, 234-241.

[11] O. Oktay, J. Schlemper, L. Folgoc, et al., Attention u-net: Learning where to look for the pancreas, 2018, arXiv: 1804.03999.

[12] X. Xiao, S. Lian, Z. Luo, et al., Weighted res-unet for high-quality retina vessel segmentation, The 9th international conference on Information Technology in Medicine and Education, 2018, 327-331.

[13] F. Isensee, J. Petersen, A. Klein, et al., Self-adapting framework for u-net-based medical image segmentation, 2018, arXiv: 1809.10486.

[14] Z. Zhou, M. Rahman Siddiquee, N. Tajbakhsh, et al., A nested u-net architecture for medical image segmentation, Deep learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, Cham, 2018, 3-11.

[15] Ö. Çiçek, A. Abdulkadir, S. Lienkamp, et al., 3D U-Net: learning dense volumetric segmentation from sparse annotation, International conference on Medical Image Computing and Computer-assisted Intervention, Springer, Cham, 2016, 424-432.

[16] S. Bakas, H. Akbari, A. Sotiras, et al., Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features, Scientific Data, 4(2017) 1-13.

[17] J. Egger, T. Kapur, A. Fedorov, et al., GBM volumetry using the 3D Slicer medical image computing platform, Scientific Reports, 3(2013) 1-7.

[18] Y. Peng, Y. Wu, Z. Li, et al., Application of 3D Slicer virtual reality technology in preoperative planning of brain tumors in neurosurgery, Chinese Journal of Minimally Invasive Neurosurgery, 23(2018) 107-110.

[19] H. Song, Y. Huang, Z. Deng, et al., COMSOL analysis of magnetic field and gradient of several groups of permanent magnets with special shapes, College Physics Experiment, 26(2013) 3-7.