Share this content in WeChat
Technical Article
Value of MUSE technique in improving diffusion tensor imaging quality of glioma
YU Yang  ZHAO Cheng  LI Qiongge  QI Zhigang  ZHANG Miao  WU Tao  LU Jie 

Cite this article as: Yu Y, Zhao C, Li QG, et al. Value of MUSE technique in improving diffusion tensor imaging quality of glioma[J]. Chin J Magn Reson Imaging, 2022, 13(10): 164-168. DOI:10.12015/issn.1674-8034.2022.10.025.

[Abstract] Objective To investigate the application of multiplexed sensitivity encoding diffusion tensor imaging (MUSE DTI) in improving the image quality of the peripheral area of intracranial gliomas and displaying the peritumoral white matter fiber bundles.Materials and Methods Twenty-one patients with glioma confirmed by surgical pathology in our hospital from 2021 January to June were prospectively enrolled. MUSE DTI sequence and conventional single shot echo planner imaging diffusion tensor imaging (SS-EPI DTI) sequence imaging were scanned respectively, and the whole-brain white matter fiber tract reconstruction was proceeded on the DTI images of the two groups. The image quality of glioma peripheral area scanned by MUSE DTI and SS-EPI DTI was double-blind evaluated by two senior radiologists. The evaluation includes image definition, distortion degree and magnetic susceptibility artifact, whether the reconstructed white matter fiber was consistent with the structural images. Then, the signal to noise ratio (SNR) and fiber density index (FDi) of the surrounding area of the tumor were measured quantitatively. The comprehensive quality score of the two groups of images was analyzed by Mann Whitney U test. The SNR and FDi of glioma peripheral areas in the two groups were analyzed by paired t-test (when P<0.05, it was considered statistically significant).Results The image quality score of MUSE DTI and SS-EPI DTI were 4 (4, 5), 3 (2, 3) [M (P25, P75), Z=0.87, P<0.01]. The SNR of the peritumoral area was 65.43±32.91 and 23.41±21.21 (P<0.01). The FDi values were 0.83±0.87, 0.68±0.71 (P<0.05). The comprehensive quality scores of the two groups of images, SNR and FDi values of the surrounding areas of the tumor had statistically differences.Conclusions MUSE DTI can enhance the image quality of the area around glioma, improve the visualization accuracy of white matter fiber bundles around the tumor, and provide accurate imaging evidence for clinical diagnosis and treatment.
[Keywords] glioma;white matter fiber tracts;fiber tract density;multivariate sensitivity coding technique;diffusion tensor imaging;magnetic resonance imaging

YU Yang1, 2   ZHAO Cheng1, 2   LI Qiongge1, 2   QI Zhigang1, 2   ZHANG Miao1, 2   WU Tao3   LU Jie1, 2*  

1 Department of Radiology and Nucler Medicine, Beijing 100053, China

2 Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Xuanwu Hospital Capital Medical University, Beijing 100053, China

3 MR Clinical Marketing Department of General Electric Medical (China) Co., Ltd, Beijing 100176, China

Lu J, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS  ANKNOWLEDGMENTS Leading Talents Project from Huizhi Ascent Support Plan of Xuanwu Hospital HZ2021ZCLJ005
Received  2022-06-08
Accepted  2022-10-13
DOI: 10.12015/issn.1674-8034.2022.10.025
Cite this article as: Yu Y, Zhao C, Li QG, et al. Value of MUSE technique in improving diffusion tensor imaging quality of glioma[J]. Chin J Magn Reson Imaging, 2022, 13(10): 164-168. DOI:10.12015/issn.1674-8034.2022.10.025.

Li BQ, Wang QS, Yao R, et al. Research on the connectivity method of human and macaque brain regions based on DTI[J]. Chin J Magn Reson Imaging, 2022(3): 43-48. DOI: 10.12015/issn.1674-8034.2022.03.009.
Rao G. Intraoperative MRI and maximizing extent of resection[J]. Neurosurg Clin N Am, 2017, 28(4): 477-485. DOI: 10.1016/
Yu Y, Li QG, Zhao C, et al. Application of high resolution diffusion weighted imaging in high-field intraoperation magnetic resonance imaging of brain tumor[J]. China Med Devices, 2021, 36(2): 81-84. DOI: 10.3969/j.issn.1674-1633.2021.02.020.
Henderson F, Abdullah KG, Verma R, et al. Tractography and the connectome in neurosurgical treatment of gliomas: the premise, the progress, and the potential[J/OL]. Neurosurg Focus, 2020, 48(2) [2022-06-07]. DOI: 10.3171/2019.11.FOCUS19785.
Seow P, Hernowo AT, Narayanan V, et al. Neural fiber integrity in high- versus low-grade glioma using probabilistic fiber tracking[J]. Acad Radiol, 2021, 28(12): 1721-1732. DOI: 10.1016/j.acra.2020.09.007.
Jiang RF, Hu XM, Deng KJ, et al. Neurite orientation dispersion and density imaging in evaluation of high-grade glioma-induced corticospinal tract injury[J/OL]. Eur J Radiol, 2021 [2022-06-07]. DOI: 10.1016/j.ejrad.2021.109750.
El-Serougy L, Abdel Razek AA, Ezzat A, et al. Assessment of diffusion tensor imaging metrics in differentiating low-grade from high-grade gliomas[J]. Neuroradiol J, 2016, 29(5): 400-407. DOI: 10.1177/1971400916665382.
Li Y, Zhang WY. Quantitative evaluation of diffusion tensor imaging for clinical management of glioma[J]. Neurosurg Rev, 2020, 43(3): 881-891. DOI: 10.1007/s10143-018-1050-1.
Ding DL, Zhao AL, Wang ZY, et al. Application of intraoperative magnetic resonance imaging combined with neuronavigation for operation of tha-lamic gliomas[J]. Chin J Pract Nerv Dis, 2019, 22(3): 253-259. DOI: 10.12083/SYSJ.2019.03.049.
Ge M, Li SW, Wang L, et al. The role of diffusion tensor tractography in the surgical treatment of pediatric optic chiasmatic gliomas[J]. J Neurooncol, 2015, 122(2): 357-366. DOI: 10.1007/s11060-015-1722-4.
Xiong JJ, Yu T. Progress of preoperative and intraoperative MR functional imaging in glioma[J]. Chin J Magn Reson Imaging, 2018, 9(5): 391-395. DOI: 10.12015/issn.1674-8034.2018.05.013.
Conti Nibali M, Rossi M, Sciortino T, et al. Preoperative surgical planning of glioma: limitations and reliability of fMRI and DTI tractography[J]. J Neurosurg Sci, 2019, 63(2): 127-134. DOI: 10.23736/S0390-5616.18.04597-6.
Fu Y, Cao FQ, Wang BH,et al. Application research of diffusion tensor imaging tracer technology in normal adult midbrain fiber tracts[J]. J Pract Radiol, 2021, 37(6): 877-880. DOI: 10.3969/j.issn.1002-1671.2021.06.001.
Shim E, Lee E, Lee JW, et al. Feasibility of postoperative 3-tesla diffusion tensor imaging in cervical spondylotic myelopathy: a comparison of single-shot EPI and multi-shot EPI[J/OL]. Eur J Radiol, 2020 [2022-06-07]. DOI: 10.1016/j.ejrad.2019.108751.
Wu WC, Miller KL. Image formation in diffusion MRI: a review of recent technical developments[J]. J Magn Reson Imaging, 2017, 46(3): 646-662. DOI: 10.1002/jmri.25664.
Chen XY, Zhang Y, Cao Y, et al. A feasible study on using multiplexed sensitivity-encoding to reduce geometric distortion in diffusion-weighted echo planar imaging[J]. Magn Reson Imaging, 2018, 54: 153-159. DOI: 10.1016/j.mri.2018.08.022.
Porter DA, Heidemann RM. High resolution diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging and a two-dimensional navigator-based reacquisition[J]. Magn Reson Med, 2009, 62(2): 468-475. DOI: 10.1002/mrm.22024.
Chen NK, Guidon A, Chang HC, et al. A robust multi-shot scan strategy for high-resolution diffusion weighted MRI enabled by multiplexed sensitivity-encoding (MUSE)[J]. Neuroimage, 2013, 72: 41-47. DOI: 10.1016/j.neuroimage.2013.01.038.
Zhang Z, Huang F, Ma XD, et al. Self-feeding MUSE: a robust method for high resolution diffusion imaging using interleaved EPI[J]. Neuroimage, 2015, 105: 552-560. DOI: 10.1016/j.neuroimage.2014.10.022.
Holdsworth SJ, O'Halloran R, Setsompop K. The quest for high spatial resolution diffusion-weighted imaging of the human brain in vivo[J/OL]. NMR Biomed, 2019, 32(4) [2022-06-07]. DOI: 10.1002/nbm.4056.
Johansson J, Lagerstrand K, Ivarsson L, et al. Brain diffusion MRI with multiplexed sensitivity encoding for reduced distortion in a pediatric patient population[J]. Magn Reson Imaging, 2022, 87: 97-103. DOI: 10.1016/j.mri.2022.01.003.
Chang HC, Sundman M, Petit L, et al. Human brain diffusion tensor imaging at submillimeter isotropic resolution on a 3Tesla clinical MRI scanner[J]. NeuroImage, 2015, 118: 667-675. DOI: 10.1016/j.neuroimage.2015.06.016.
Jin W, Li YH, Li B, et al. Research development of MR diffusion tensor imaging and track density imaging in central nervous system[J]. Chin J Med Instrum, 2019, 43(5): 352-354, 368. DOI: 10.3969/j.issn.1671-7104.2019.05.011.
Romano A, Fasoli F, Ferrante M, et al. Fiber density index, fractional anisotropy, adc and clinical motor findings in the white matter of patients with glioblastoma[J]. Eur Radiol, 2008, 18(2): 331-336. DOI: 10.1007/s00330-007-0740-9.
Roberts TPL, Liu F, Kassner A, et al. Fiber density index correlates with reduced fractional anisotropy in white matter of patients with glioblastoma[J]. AJNR Am J Neuroradiol, 2005, 26(9): 2183-2186.
Fekonja LS, Wang ZQ, Aydogan DB, et al. Detecting corticospinal tract impairment in tumor patients with fiber density and tensor-based metrics[J/OL]. Front Oncol, 2020 [2022-06-07]. DOI: 10.3389/fonc.2020.622358.
JFeng T, Zhao SJ, Nie BB, et al. A review for fiber tracking algorithms for diffusion magnetic resonance imaging[J]. Chin J Med Imaging, 2019, 27(5): 393-396, 400. DOI: 10.3969/j.issn.1005-5185.2019.05.018.
Zhang H, Wang CY, Chen WB, et al. Deep learning based multiplexed sensitivity-encoding (DL-MUSE) for high-resolution multi-shot DWI[J]. Neuroimage, 2021, 244: 118632. DOI: 10.1016/j.neuroimage.2021.118632.
Li HY, Liang ZF, Zhang CY, et al. SuperDTI: Ultrafast DTI and fiber tractography with deep learning[J]. Magn Reson Med, 2021, 86(6): 3334-3347. DOI: 10.1002/mrm.28937.

PREV Proton exchange rate quantification-based lesion detection in ischemic stroke using chemical exchange saturation transfer imaging
NEXT MRI features of a fetal floor of the mouth epidermoid cyst: One case report

Tel & Fax: +8610-67113815    E-mail: