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Technical Article
Application value of intelligent quick magnetic resonance technique in magnetic resonance scanning of cervical vertebra
XU Min  WU Yu  LIU Jian  WANG Rongpin  XU Rui  ZENG Xianchun 

Cite this article as: XU M, WU Y, LIU J, et al. Application value of intelligent quick magnetic resonance technique in magnetic resonance scanning of cervical vertebra[J]. Chin J Magn Reson Imaging, 2023, 14(10): 111-115. DOI:10.12015/issn.1674-8034.2023.10.019.


[Abstract] Objective To explore the clinical value of fast intelligent quick magnetic resonance (IQMR) in cervical MRI.Materials and Methods In this study, 50 patients with suspected cervical spondylosis were collected retrospectively and included in T2-weighted (T2WI) conventional, IQMR original, and IQMR reconstructed images. ANOVA test was used to compare the differences among the objective scores of the three groups of images signal strength (SI), average background standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Multiple rank sum (Kruskal-WallisH) test was used to evaluate the subjective scores. The focus detection and scanning time differences among the three groups were compared.Results The average scanning time of IQMR sagittal T2WI sequence was about 1 min 17 s, about 57% shorter than conventional scanning. Compared with the original image, the SDbackground of the image reconstructed by IQMR decreased by 21%, and CNR increased by 28%. Compared with the conventional image, the SD of the image reconstructed by IQMR decreased by 43%, and the CNR increased by 68%. There was no statistical difference in SIspinal cord among the three groups of images. There were significant differences in cerebrospinal fluid signals among the three groups. The cerebrospinal fluid signals of conventional images were lower than those of IQMR original images and IQMR reconstruction images.Conclusions IQMR technology can reduce noise, and improve SNR and CNR in cervical MRI, thus improving image quality. In the case of ensuring image quality, it has the potential to reduce scanning time and improve the efficiency of clinical MR cervical spine scanning.
[Keywords] cervical vertebrae;signal-to-noise ratio;contrast-to-noise ratio;intelligent quick magnetic resonance;magnetic resonance imaging

XU Min1, 2   WU Yu1, 2   LIU Jian1, 2   WANG Rongpin2   XU Rui2   ZENG Xianchun2*  

1 Department of Graduate School, Zunyi Medical University, Zunyi 563000, China

2 Department of Medical Imaging, Guizhou Provincial People's Hospital, Guiyang 550002, China

Corresponding author: ZENG X C, E-mail: zengxianchun04@foxmail.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82060314); Natural Science Foundation of Guizhou Province (No. Qian Kehe Jichu-ZK〔2022〕YB263).
Received  2023-03-05
Accepted  2023-09-25
DOI: 10.12015/issn.1674-8034.2023.10.019
Cite this article as: XU M, WU Y, LIU J, et al. Application value of intelligent quick magnetic resonance technique in magnetic resonance scanning of cervical vertebra[J]. Chin J Magn Reson Imaging, 2023, 14(10): 111-115. DOI:10.12015/issn.1674-8034.2023.10.019.

[1]
BRINJIKJI W, LUETMER P H, COMSTOCK B, et al. Systematic literature review of imaging features of spinal degeneration in asymptomatic populations[J]. AJNR Am J Neuroradiol, 2015, 36(4): 811-816. DOI: 10.3174/ajnr.A4173.
[2]
THEODORE N. Degenerative cervical spondylosis[J]. N Engl J Med, 2020, 383(2): 159-168. DOI: 10.1056/NEJMra2003558.
[3]
YI J S, CHA J G, HAN J K, et al. Imaging of herniated discs of the cervical spine: inter-modality differences between 64-slice multidetector CT and 1.5-T MRI[J]. Korean J Radiol, 2015, 16(4): 881-888. DOI: 10.3348/kjr.2015.16.4.881.
[4]
HU B. Comparative study on the clinical effect of MRI and CT in the diagnosis of disc herniation[J]. China Med Device Inf, 2022, 28(10): 93-95. DOI: 10.15971/j.cnki.cmdi.2022.10.036.
[5]
LI B. Comparison of MRI and CT in the diagnosis of lumbar disc herniation[J]. World Latest Med Inf, 2017, 17(79): 139. DOI: 10.19613/j.cnki.1671-3141.2017.79.119.
[6]
LIANG F, ZHANG Z H, DENG Y X, et al. Comparison of infrared thermal imaging and CT/MRI in the diagnosis of lumbar disc herniation in outpatient department[J]. Chin J Mod Drug Appl, 2017, 11(23): 56-57. DOI: 10.14164/j.cnki.cn11-5581/r.2017.23.032.
[7]
YANG A C, KRETZLER M, SUDARSKI S, et al. Sparse reconstruction techniques in magnetic resonance imaging: methods, applications, and challenges to clinical adoption[J]. Invest Radiol, 2016, 51(6): 349-364. DOI: 10.1097/RLI.0000000000000274.
[8]
DO W J, SEO S, HAN Y, et al. Reconstruction of multicontrast MR images through deep learning[J]. Med Phys, 2020, 47(3): 983-997. DOI: 10.1002/mp.14006.
[9]
KANEMARU N, TAKAO H, AMEMIYA S, et al. The effect of a post-scan processing denoising system on image quality and morphometric analysis[J]. J De Neuroradiol, 2022, 49(2): 205-212. DOI: 10.1016/j.neurad.2021.11.007.
[10]
WANG S, JIANG Z W, YANG H L, et al. MRI-based medical image recognition: identification and diagnosis of LDH[J/OL]. Comput Intell Neurosci, 2022, 2022: 5207178 [2023-03-04]. https://pubmed.ncbi.nlm.nih.gov/36120698/. DOI: 10.1155/2022/5207178.
[11]
STER C L, GAMBAROTA G, LASBLEIZ J, et al. Breath-hold MR measurements of fat fraction, T1, and T2* of water and fat in vertebral bone marrow[J]. J Magn Reson Imaging, 2016, 44(3): 549-555. DOI: 10.1002/jmri.25205.
[12]
LAREDO J D, VUILLEMIN-BODAGHI V, BOUTRY N, et al. SAPHO syndrome: MR appearance of vertebral involvement[J]. Radiology, 2007, 242(3): 825-831. DOI: 10.1148/radiol.2423051222.
[13]
YOUSAF T, DERVENOULAS G, POLITIS M. Advances in MRI methodology[J/OL]. Int Rev Neurobiol, 2018, 141: 31-76 [2023-03-04]. https://pubmed.ncbi.nlm.nih.gov/30314602/. DOI: 10.1016/bs.irn.2018.08.008.
[14]
BYANJU R, KLEIN S, CRISTOBAL-HUERTA A, et al. Time efficiency analysis for undersampled quantitative MRI acquisitions[J/OL]. Med Image Anal, 2022, 78: 102390 [2023-03-04]. https://pubmed.ncbi.nlm.nih.gov/35217453/. DOI: 10.1016/j.media.2022.102390.
[15]
ZHANG X L, GUO D, HUANG Y M, et al. Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI[J/OL]. Med Image Anal, 2020, 63: 101687 [2023-03-04]. https://pubmed.ncbi.nlm.nih.gov/32294605/. DOI: 10.1016/j.media.2020.101687.
[16]
OBAMA Y, OHNO Y, YAMAMOTO K, et al. MR imaging for shoulder diseases: effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging[J/OL]. Magn Reson Imaging, 2022, 94: 56-63 [2023-03-04]. https://pubmed.ncbi.nlm.nih.gov/35934207/. DOI: 10.1016/j.mri.2022.08.004.
[17]
IKEDA H, OHNO Y, MURAYAMA K, et al. Compressed sensing and parallel imaging accelerated T2 FSE sequence for head and neck MR imaging: comparison of its utility in routine clinical practice[J/OL]. Eur J Radiol, 2021, 135: 109501 [2023-03-04]. https://pubmed.ncbi.nlm.nih.gov/33395594/. DOI: 10.1016/j.ejrad.2020.109501.
[18]
SARTORETTI T, REISCHAUER C, SARTORETTI E, et al. Common artefacts encountered on images acquired with combined compressed sensing and SENSE[J]. Insights Imaging, 2018, 9(6): 1107-1115. DOI: 10.1007/s13244-018-0668-4.
[19]
LUSTIG M, DONOHO D, PAULY J M. 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.
[20]
LIU K, CHEN C Z, WEN X X, et al. Comparison of compressed sensing and parallel imaging applied to contrastGenhanced MRI of liver[J]. J Pract Radiol, 2019, 35(10): 1665-1667, 1701. DOI: 10.3969/j.issn.1002-1671.2019.10.028.
[21]
DELATTRE B M A, 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.
[22]
LI B B, ZHANG H N, FANG X, et al. Impacts of compressed sensing acceleration factors on imaging quality of head 3D-T1WI and voxel-based morphometry quantitative parameters[J]. Chin J Med Imag Technol, 2022, 38(11): 1730-1734. DOI: 10.13929/j.issn.1003-3289.2022.11.031.
[23]
LI J, WU L H, XU M Y, et al. Improving image quality and reducing scan time for synthetic MRI of breast by using deep learning reconstruction[J/OL]. Biomed Res Int, 2022, 2022: 3125426 [2023-03-04]. https://pubmed.ncbi.nlm.nih.gov/36060133/. DOI: 10.1155/2022/3125426.
[24]
SUBHAS N. Editorial comment: accelerated joint MRI with deep learning-based reconstruction-a promising approach to increasing imaging speed without compromising image quality[J/OL]. AJR Am J Roentgenol, 2022, 218(3): 516 [2023-03-04]. https://pubmed.ncbi.nlm.nih.gov/34585614/. DOI: 10.2214/AJR.21.26880.
[25]
TURKBEY B. Better image quality for diffusion-weighted MRI of the prostate using deep learning[J]. Radiology, 2022, 303(2): 382-383. DOI: 10.1148/radiol.212078.
[26]
YAN X H, LUO Y, RAN L P, et al. Clinical application of high resolution myocardial T2-weighted dark blood sequence based on artificial intelligence assisted compressed sensing technique in myocardial edema[J]. Chin J Magn Reson Imag, 2022, 13(6): 76-80, 97. DOI: 10.12015/issn.1674-8034.2022.06.015.
[27]
KATSCHER U, BÖRNERT P. Parallel magnetic resonance imaging[J]. Neurotherapeutics, 2007, 4(3): 499-510. DOI: 10.1016/j.nurt.2007.04.011.
[28]
DEL GRANDE F, RASHIDI A, LUNA R, et al. Five-minute five-sequence knee MRI using combined simultaneous multislice and parallel imaging acceleration: comparison with 10-minute parallel imaging knee MRI[J]. Radiology, 2021, 299(3): 635-646. DOI: 10.1148/radiol.2021203655.
[29]
HAMILTON J, FRANSON D, SEIBERLICH N. Recent advances in parallel imaging for MRI[J/OL]. Prog Nucl Magn Reson Spectrosc, 2017, 101: 71-95 [2023-03-04]. https://pubmed.ncbi.nlm.nih.gov/28844222/. DOI: 10.1016/j.pnmrs.2017.04.002.
[30]
PIERRE E Y, GRODZKI D, AANDAL G, et al. Parallel imaging-based reduction of acoustic noise for clinical magnetic resonance imaging[J]. Invest Radiol, 2014, 49(9): 620-626. DOI: 10.1097/RLI.0000000000000062.
[31]
PADOLE A, SINGH S, ACKMAN J B, et al. Submillisievert chest CT with filtered back projection and iterative reconstruction techniques[J]. AJR Am J Roentgenol, 2014, 203(4): 772-781. DOI: 10.2214/AJR.13.12312.
[32]
MEISTER R L, GROTH M, JÜRGENS J H W, et al. Compressed SENSE in pediatric brain tumor MR imaging: assessment of image quality, examination time and energy release[J]. Clin Neuroradiol, 2022, 32(3): 725-733. DOI: 10.1007/s00062-021-01112-3.
[33]
MA Y M, LI M, ZHANG Y J. Value of compressed sensing technique in fetal brain magnetic resonance imaging[J]. Chin J Clin Res, 2021, 34(5): 615-619. DOI: 10.13429/j.cnki.cjcr.2021.05.009.

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