Share：
Share this content in WeChat
X
[Chinese][PDF]157883
• CLINICAL ARTICLE •
The preliminary study on the parameter optimization of IVIM-DWI pulse sequence in gastric cancer patients with 3.0 T MR
ZHAI Yanhui  SUN Nannan  WANG Junxin  ZHANG Min  LI Ying  ZHOU Tao  CHEN Ying  JIA Shouqiang 

Cite this article as: Zhai YH, Sun NN, Wang JX, et al. The preliminary study on the parameter optimization of IVIM-DWI pulse sequence in gastric cancer patients with 3.0 T MR[J]. Chin J Magn Reson Imaging, 2021, 12(2): 34-37, 48. DOI:10.12015/issn.1674-8034.2021.02.008.


[Abstract] Objective To investigate the optimal selection of scan parameters in intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) gastric cancer patients with 3.0 T MR. Materials andMethods Forty patients with gastric cancer confirmed by gastroscopy pathology from December 2018 to October 2019 in our hospital were collected prospectively for preoperative MRI examination. Patients were divided into A, B two groups with the random digital table method. Group A increased the number of signal average and extended repetition time (TR) and echo time (TE) appropriately. Group B reduced the number of signal average and reduced the TR and TE. The other parameters of the two groups are the same. The scanning time of A, B two groups was 14 min and 7 min 12 s respectively. The signal-to-noise ratio (SNR), contrast noise ratio (CNR), and the ADCslow, ADCfast, f value of the lesion were measured for each patient's IVIM-DWI sequence (b values equal to 1200 t) images. The differences of SNR, CNR, ADCslow, ADCfast, f value were compared by independent sample t test, and the image quality was evaluated subjectively by double-blind method.Results The SNR values of A and B group were 56.60±34.64, 53.50±20.21. And the CNR values were 44.95±18.52, 41.38±31.72, and the ADCslow values were 0.635±0.274, 0.818±0.305, the ADCfast values were 6.100±1.075, 6.471±1.549, the f values were 0.419±0.184, 0.402±0.193. There was no significant difference in SNR, CNR, ADCslow, ADCfast, f values between A and B groups (P＞0.05). The subjective scores of images between A and B groups were statistically significant (P＜0.05).Conclusions Not only the scanning time is obviously shortened, but also the image quality can meet the diagnostic requirements by reducing the number of signal average and shortening the TR, TE scanning scheme appropriately. Thus improve the feasibility of MR examination for gastric cancer patients.
[Keywords] gastric cancer；magnetic resonance imaging；intravoxel incoherent motion；diffusion weighted imaging；parameter optimization；image quality

ZHAI Yanhui   SUN Nannan   WANG Junxin   ZHANG Min   LI Ying   ZHOU Tao   CHEN Ying   JIA Shouqiang*  

Department of Imaging, Ji'nan People's Hospital Affiliated to Shandong First Medical University, Ji'nan 271199, China

Jia SQ, E-mail: jshqlw@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS This work was part of Key R & D Projects in Shandong Province (No.2018GSF118077).
Received  2020-09-01
Accepted  2021-01-12
DOI: 10.12015/issn.1674-8034.2021.02.008
Cite this article as: Zhai YH, Sun NN, Wang JX, et al. The preliminary study on the parameter optimization of IVIM-DWI pulse sequence in gastric cancer patients with 3.0 T MR[J]. Chin J Magn Reson Imaging, 2021, 12(2): 34-37, 48. DOI:10.12015/issn.1674-8034.2021.02.008.

1
Franca M, Marti-Bonmati L, Alberich-Bayarri A, et al. Evaluation of fibrosis and inflammation in diffuse liver diseases using intravoxel incoherent motion diffusion-weighted MR imaging[J]. Abdom Radiol(NY), 2017, 42(2): 468-477. DOI: 10.1007/s00261-016-0899-0.
2
Pieper CC, Meyer C, Sprinkart AM, et al. The value of intravoxel incoherent motion model-based diffusion-weighted imaging for outcome prediction in resin-based radioembolization of breast cancer liver metastases[J]. Onco Targets Ther, 2016, 9: 4089-4098. DOI: 10.2147/OTT.S104770.
3
Ai Z, Han QJ, Liang JK, et al. Evaluation of image quality of normal liver using intraventricular incoherent motion imaging based on ECG gated technique[J]. Chin Med Imaging Technol, 2019, 35(6): 909-913. DOI: 10.13929/j.1003-3289.201810146.
4
Luciani A, Vignaud A, Cavet M, et al. Liver cirrhosis: intravoxel in?coherent motion MR imaging pilot study[J]. Radiology, 2008, 249(3): 891-899.
5
Meng YK, Zhang CD, Zhang HM, et al. Preliminary study on high b value optimization of magnetic resonance intraventricular incoherent motion imaging for rectal cancer [J]. J Clin Radiol, 2017, 36(6): 822-826. DOI: 10.13437/j.cnki.jcr.2017.06.017.
6
Fujiwara Shunrou, Mori Yuki, Daniela Martinez de la Mora, et al. Feasibility of IVIM parameters from diffusion-weighted imaging at 11.7 T MRI for detecting ischemic changes in common carotid artery occlusion rats[J]. Scientific Reports, 2020, 10(1): 8404. DOI: 10.1038/s41598-020-65310-8.
7
Oliver J, Gurney-Champion, Susanne S, et al. Optimal acquisition scheme for flow-compensated intravoxel incoherent motion diffusion-weighted imaging in the abdomen: An accurate and precise clinically feasible protocol[J]. Magn Reson Med, 2020, 83(3): 1003-1015. DOI: 10.1002/mrm.27990.
8
Le Bihan D, Breton E, Lallemand D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging[J]. Radiology, 1988, 168(2): 497-505. DOI: 10.1148/radiology.168.2.3393671.
9
Zhang L. The value of intra voxel incoherent motion diffusion weighted imaging in the diagnosis and efficacy prediction of abdominal tumors[J]. Inter J Med Radiol, 2019, 42(3): 317-321. DOI: 10.19300/j.2019.z6570.
10
Guiu B, Petit JM, Capitan V, et al. Intravoxel incoherent motion diffusion- weighted imaging in nonalcoholic fatty liver disease: a 3.0 T MR study[J]. Radiology, 2012, 265(1): 96-103.
11
Alexander DC, Moira CS, Mark DH, et al. The effect of low b-values on the intravoxel incoherent motion derived pseudodiffusion parameter in liver[J]. Magn Reson Med, 2015, 73: 306-311.
12
Jiang JZ, Li ZL, Li F, et al. Preliminary study on optimized b value of DWI for lung cancer with 1.5 T MR[J]. J Pract Radiol, 2014, 30(4): 664-666, 682. DOI: 10.3969/j.issn.1002-1671.2014.04.032.
13
Moulin K, Aliotta E, Ennis DB. Effect of flow-encoding strength on intravoxel incoherent motion in the liver[J]. Magn Reson Med, 2019, 81(3): 1521-1533. DOI: 10.1002/mrm.27490.
14
Meng YF, Zhou CH, He D, et al. Diffusion weighted imaging of different b values in patients with TIA [J]. J Brain Neurological Dis, 2017, 25(5): 281-286. DOI: CNKI:SUN:LYSJ.0.2017-05-006.
15
Deng QM, Qiu WJ, Zhou ZP, et al. The value of DWI in differential diagnosis of benign and malignant lung lesions under different b values[J]. Chin Med Imaging Technol, 2012, 28(8): 1537-1540. DOI: CNKI:SUN:ZYXX.0.2012-08-040.
16
Jeff LZ, Eric ES, Henry R, et al. Optimization of b-value sampling for diffusion-weighted imaging of the kidney[J]. Magn Reson Med, 2012, 67(1): 89-97. DOI: 10.1002/mrm.22982.
17
Xie LF, Liu ZY, Liang CH. Effect of gadolinium contrast agent on incoherent motion magnetic resonance imaging of abdominal solid organs[J]. Chin J Med Imaging, 2015, 23(2): 114, 119. DOI: 10.3969/j.issn.1005-5185.2015.02.009.
18
Chen P, Tan C, Li YH, et al. Optimization of DWI scanning parameters in patients with acute ischemic stroke[J]. Inter J Med Radiol, 2019, 42(4): 391-394. DOI: 10.19300/j.2019.l6761.
19
Guo YM, Yang J, Jiang T, et al. Pay attention to the research of MRI parallel acquisition technology[J]. Chin J Radiol, 2007, 41: 2-4. DOI: 10.3760/jissn:1005-1201.2007.01.002.
20
While PT, Teruel JR, Vidić I, et al. Relative enhanced diffusivity: noise sensitivity, protocol optimization, and the relation to intravoxel incoherent motion[J]. MAGMA, 2018, 31(3): 425-438. DOI: 10.1007/s10334-017-0660-x.
21
Wang DD, Yin HJ, Liu WL, et al. Comparative analysis of the diagnostic values of T2 mapping and diffusion-weighted imaging for sacroiliitis in ankylosing spondylitis[J]. Skeletal Radiol, 2020, 49(10): 1597-1606. DOI: 10.1007/s00256-020-03442-8.
22
Hans-Jonas M, Andreas W, Alexey S. ADC values of benign and high grade meningiomas and associations with tumor cellularity and proliferation-A systematic review and meta-analysis[J]. J Neurological Sci, 2020, 415: 116975. DOI: 10.1016/j.jns.2020.116975.

PREV Radiomics features based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging for identifying triple negative breast cancer
NEXT Texture analysis and machine learning based on T2 weighted image in distinguishing renal angiomyolipoma without visible fat and renal cell carcinoma
  


LINK


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