Share：
Share this content in WeChat
X
[Chinese][PDF]583100
• Technical Article •
The clinical application value of constellation shuttling imaging technology based on T1W-3D sequence
MA Yuanyuan  LI Yan  ZHANG Xuekun  YAN Fuhua  SONG Qi 

Cite this article as: Ma YY, Li Y, Zhang XK, et al. The clinical application value of constellation shuttling imaging technology based on T1W-3D sequence[J]. Chin J Magn Reson Imaging, 2021, 12(11): 52-56. DOI:10.12015/issn.1674-8034.2021.11.011.


[Abstract] Objective To explore the clinical application value of constellation shuttling imaging technology in cranial magnetic resonance scanning.Materials and Methods: Brain imaging of 20 healthy adult volunteers on a United Imaging uMR780 3.0 T superconducting magnetic resonance, with 24-channel phased array coils, from July to August 2020, this study recruited without contraindications to magnetic resonance scanning, including 10 males and 10 females, aged 22—69 (40.2±13.3) year old. Two radiologists with an attending physician level or above perform subjective image quality assessment without knowing the image sequence information: based on visual assessment, comprehensive signal-to-noise ratio, artifacts, and the definition of gray matter borders, they perform Likert 5-level scoring. The data results were tested by kappa. Place the region of interest (ROI) in the gray matter (GM,) and white matter (WM) and background noise regions corresponding to the anatomical structure, and repeat the intensity of the gray matter, white matter signal and noise signal (noise signal), take the average of the three measurements and calculate the SNR and CNR. Using the voxel-based morphometrics CAT-12 software to obtain the total brain parenchymal volume, total gray matter volume, total white matter volume, and total cerebrospinal fluid volume. All of the above uses the statistical software version SPSS 23.0 to perform Paired t test, and the difference is statistically significant with P＜0.05.Results The image quality of the included study Likert grade 5 score (4—5 points), the test score result: Kappa value is 0.801, P value is less than 0.001, it is considered that the image quality evaluation results of the two doctors are statistically consistent. For SNR and CNR, a t test was performed to compare the two sets of data. The results showed that the signal-to-noise ratio between the two was statistically different, and the light shuttle imaging technology group was higher than the parallel acquisition technology group. Using the voxel-based morphometrics CAT-12 software to obtain the total brain parenchymal volume, total gray matter volume, total white matter volume, and total cerebrospinal fluid volume, paired t-test was performed, and the P values ​​were 0.98, 0.25, 0.50, 0.11, respectively. Both are greater than 0.05. There is no significant difference between the two groups of different scanning methods based on the voxel-based morphometric results in statistical significance. The scanning time of the constellation shuttling imaging technology is reduced by 116 s, which is reduced to 40%.Conclusions Compared with the traditional parallel acquisition technology, the constellation shuttling imaging technology has a better image signal-to-noise ratio in the skull T1W sequence than the parallel acquisition technology, and can significantly shorten the scanning time, which has important clinical application value.
[Keywords] magnetic resonance imaging；constellation shuttling imaging technology；compressed sensing；parallel acquisition technology；three-dimensional T1 weighted sequences

MA Yuanyuan   LI Yan   ZHANG Xuekun   YAN Fuhua   SONG Qi*  

Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University of Medicine, Shanghai 200025, China

Song Q, E-mail: songqi_718@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Shanghai Science and Technology Committee Scientific Research Project (No. 18DZ1930103).
Received  2021-05-25
Accepted  2021-09-18
DOI: 10.12015/issn.1674-8034.2021.11.011
Cite this article as: Ma YY, Li Y, Zhang XK, et al. The clinical application value of constellation shuttling imaging technology based on T1W-3D sequence[J]. Chin J Magn Reson Imaging, 2021, 12(11): 52-56. DOI:10.12015/issn.1674-8034.2021.11.011.

[1]
Montalt-Tordera J, Muthurangu V, Hauptmann A, et al. Machine learning in Magnetic Resonance Imaging: Image reconstruction[J]. Phys Med, 2021, 83: 79-87. DOI: 10.1016/j.ejmp.2021.02.020.
[2]
Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging[J]. Magn Reson Med, 2007, 58(6): 1182-1195. DOI: 10.1002/mrm.21391.
[3]
Candes EJ, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions Information Theory, 2006, 52(2): 489-509. DOI: 10.1109/TIT.2005.862083.
[4]
Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging[J]. Magn Reson Med, 2007, 58(6): 1182-1195. DOI: 10.1002/mrm.21391.
[5]
Chen N, Qin W, Li KC, et al. The value of T1WI-3D-MP RAGE in brain scanning[J]. Chin Med Imaging Technol, 2005, 21(6): 974-977. DOI: 10.3321/j.issn:1003-3289.2005.06.047.
[6]
Qin L, Gu SJ, Chen CH, et al. Research on the best single-level image of coronary stent based on double-layer detector spectral CT[J]. Chin J Radiol, 2020, 54(6): 508-513. DOI: 10.3760/cma.j.cn112149-20191230-01018.
[7]
Vranic JE, Cross NM, Wang Y, et al. Compressed sensing-sensitivity encoding (CS-SENSE) accelerated brain imaging: reduced scan time without reduced image quality[J]. AJNR Am J Neuroradiol, 2019, 40(1): 92-98. DOI: 10.3174/ajnr.A5905.
[8]
Lin Q, Wang JJ, Sun MH, et al. Clinical application of cardiac magnetic resonance compressed sensing film sequence[J]. Chin J Med Imaging Technol, 2020, 36(2): 281-286. DOI: 10.13929/j.issn.1003-3289.2020.02.027.
[9]
Song CJ, Qi L, Zhu XM, et al. Clinical application value of compressed sensing technology based on 3D-MATRIX sequence in knee joint imaging[J]. J Nanjing Med University (Natural Sci Ed), 2019, 39(10): 1529-1532. DOI: 10.7655/NYDXBNS20191029.
[10]
Li Q, Lu SS, Sun T, et al. Application research of compressed sensing technology in cranial magnetic resonance angiography[J]. Chin Med Equip, 2020, 17(2): 66-70. DOI: 10.3969/J.ISSN.1672-8270.2020.02.018.
[11]
Delattre BMA, Boudabbous S, Hansen C, et al. Compressed sensing MRI of different organs: ready for clinical daily practice?[J]. Eur Radiol, 2020, 30(1): 308-319. DOI: 10.1007/s00330-019-06319-0.
[12]
Mönch S, Sollmann N, Hock A, et al. Magnetic resonance imaging of the brain using compressed sensing-quality assessment in daily clinical routine[J]. Clin Neuroradiol, 2020, 30(2): 279-286. DOI: 10.1007/s00062-019-00789-x.
[13]
Kozak BM, Jaimes C, Kirsch J, et al. MRI techniques to decrease imaging times in children[J]. Radiographics, 2020, 40(2): 485-502. DOI: 10.1148/rg.2020190112.
[14]
Cristobal-Huerta A, Poot DHJ, Vogel MW, et al. Compressed sensing 3D-GRASE for faster high-resolution MRI[J]. Magn Reson Med, 2019, 82(3): 984-999. DOI: 10.1002/mrm.27789.
[15]
Delattre BMA, Boudabbous S, Hansen C, et al. Compressed sensing MRI of different organs: ready for clinical daily practice?[J]. Eur Radiol, 2020, 30(1): 308-319. DOI: 10.1007/s00330-019-06319-0.
[16]
Eichinger P, Hock A, Schön S, et al. Acceleration of double inversion recovery sequences in multiple sclerosis with compressed sensing[J]. Invest Radiol, 2019, 54(6): 319-324. DOI: 10.1097/RLI.0000000000000550.
[17]
Feng L, Benkert T, Block KT, et al. Compressed sensing for body MRI[J]. J Magn Reson Imaging, 2017, 45(4): 966-987. DOI: 10.1002/jmri.25547.
[18]
Jaspan ON, Fleysher R, Lipton ML. Compressed sensing MRI: a review of the clinical literature[J]. Br J Radiol, 2015, 88(1056): 20150487. DOI: 10.1259/bjr.20150487.
[19]
Dieckmeyer M, Roy AG, Senapati J, et al. Effect of MRI acquisition acceleration via compressed sensing and parallel imaging on brain volumetry[J]. MAGMA. 2021, 34(4): 487-497. DOI: 10.1007/s10334-020-00906-9.
[20]
Blunck Y, Kolbe SC, Moffat BA, et al. Compressed sensing effects on quantitative analysis of undersampled human brain sodium MRI[J]. Magn Reson Med, 2020, 83(3): 1025-1033. DOI: 10.1002/mrm.27993.
[21]
Duan Y, Zhang J, Zhuo Z, et al. Accelerating brain 3D T1-weighted turbo field echo MRI using compressed sensing-sensitivity encoding (CS-SENSE). Eur J Radiol, 2020, 131: 109255. DOI: 10.1016/j.ejrad.2020.109255.
[22]
Küstner T, Gatidis S, Liebgott A, et al. A machine-learning framework for automatic reference-free quality assessment in MRI[J]. Magn Reson Imaging, 2018, 53: 134-147. DOI: 10.1016/j.mri.2018.07.003.
[23]
Sandino CM, Cheng JY, Chen F,et al. Compressed sensing: from research to clinical practice with deep neural networks[J]. IEEE Signal Process Mag, 2020, 37(1): 111-127. DOI: 10.1109/MSP.2019.2950433.
[24]
Han Y, Sunwoo L, Ye JC. k-Space deep learning for accelerated MRI[J]. IEEE Trans Med Imaging, 2020, 39(2): 377-386. DOI: 10.1109/TMI.2019.2927101.

PREV Correlative analysis of glycemic variability and brain stracture and cognitive function in type 2 diabetic patients
NEXT Application value of functional magnetic resonance in early diagnosing autoimmune encephalitis in and evaluating the prognosis in children
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn