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Magnetic resonance diffusion imaging: Clinical research progress of IVIM and DKI in breast lesions
KE Cheng-lu  LI Jing 

DOI:10.12015/issn.1674-8034.2018.02.015.


[Abstract] Diffusion weighted imaging (DWI) is a technology to reflect the molecular diffusion movement through quantitative water molecular diffusion movement. However the diffusion information from DWI has a certain degree of deviation, in order to more accurately describe the body diffusion movements and microstructure organization, the intravoxel incoherent motion imaging(IVIM) based on the capillary microcirculation perfusion and the diffusion kurtosis imaging(DKI) based on the non-gaussian diffusion is put forward and the relevant clinical application research is the hotspot currently. This article mainly introduce the theoretical basis of IVIM and DKI and the research progresses in breast lesions.
[Keywords] Intravoxel incoherent motion imaging；Diffusion kurtosis imaging；Breast diseases；Magnetic resonance imaging；Diffusion weighted imaging

KE Cheng-lu Department of Imaging Diagnosis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

LI Jing* Department of Imaging Diagnosis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

*Corresponding to: Li J, E-mail: dr.lijing@163.com

Conflicts of interest   None.

Received  2017-10-07
Accepted  2017-12-19
DOI: 10.12015/issn.1674-8034.2018.02.015
DOI:10.12015/issn.1674-8034.2018.02.015.

[1]
García Santos JM, Ordóñez C,Torres del Río S, et al. ADC measurements at low and high b values: insight into normal brain structure with clinical DWI. Magn Reson Imaging, 2008, 26(1): 35-44.
[2]
Yoshiura T, Mihara F, Tanaka A, et al. High b value diffusion-weighted imaging is more sensitive to white matter degeneration in Alzheimer's disease. Neuroimage, 2003, 20(1): 413-419.
[3]
Ichikawa T, Erturk SM, Motosugi U, et al. High-b value diffusion-weighted MRI for detecting pancreatic adenocarcinoma: preliminary results. AJR Am J Roentgenol, 2007, 188(2): 409-414.
[4]
Luciani A, Vignaud A, Cavet M, et al. Liver cirrhosis: intravoxel incoherent motion MR imaging-pilot study. Radiology, 2008, 249(3): 891-892.
[5]
Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med, 2005, 53(6): 1432-1440.
[6]
Tamura T, UsuiS, Murakami S, et al. Biexponential signal attenuation analysis of diffusion-weighted imaging of breast. Magn Reson Med Sci, 2010, 9(4): 195-207.
[7]
Le Bihan D, Breton E, Lallemand D, et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology, 1986, 161(2): 401-407.
[8]
Le Bihan D, Breton E, Lallemand D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology, 1988, 163(2): 497-505.
[9]
Takahara T, Kwee TC. Low b-value diffusion-weighted imaging: emerging applications in the body. J Magn Reson Imaging, 2012, 35(6): 1266-1273.
[10]
Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed, 2010, 23(7): 698-710.
[11]
Yuan J, Wong OL, Lo GG, et al. Statistical assessment of bi-exponential diffusion weighted signal characteristics induced by intravoxel incoherent motion in malignant breast tumors. Quant Imaging Med Surg, 2016, 6(4): 418-429.
[12]
Wang GY, Yang GZ, Dong HB, et al. The value of DWI based on intraVoxel incoherent motion in identifying benign and malignant breast lesions. J Clin Radiol, 2016, 35(3): 348-352.王高燕，杨光钊，董海波，等．基于体素内不相干运动DWI在乳腺良恶性病变鉴别中的价值．临床放射学杂志, 2016, 35(3): 348-352.
[13]
Dijkstra H, Dorrius MD, Wielema M, et al. Semi-automated quantitative intravoxel incoherent motion analysis and its implementation in breast diffusion-weighted imaging. J Magn Reson Imaging, 2016, 43(5): 1122-1131.
[14]
Iima M, Yano K, Kataoka M, et al. Quantitative non-gaussian diffusion and intravoxel incoherent motion magnetic resonance imaging: differentiation of malignant and benign breast lesions. Invest Radiol, 2015, 50(4): 205-211.
[15]
Wang Q, Guo Y, Zhang J, et al. Contribution of IVIM to conventional dynamic contrast-enhanced and diffusion-weighted MRI in differentiating benign from malignant breast masses. Breast Care (Basel), 2016, 11(4): 254-258.
[16]
Ma D, Lu F, Zou X, et al. Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions. Magn Reson Imaging, 2017, 36(2): 175-179.
[17]
Dijkstra H, Dorrius MD, Wielema M, et al. Quantitative DWI implemented after DCE-MRI yields increased specificity for BI-RADS 3 and 4 breast lesions. Chin J Magn Reson Imag, 2016, 44(6): 1642-1649.
[18]
Che SN, Cui XL, Li J, et al. The value of intravoxel incoherent motion model of diffusion weighted imaging in differentiating benign from malignant breast lesions. Chin J Magn Reson Imag, 2015, 6(7): 506-512.车树楠，崔晓琳，李静，等. MR扩散加权成像体素内不相干运动模型对于乳腺良恶性病变诊断价值的研究．磁共振成像, 2015, 6(7): 506-512.
[19]
Bokacheva L, Kaplan JB, Giri DD, et al. Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma. J Magn Reson Imaging, 2014, 40(4): 813-823.
[20]
Liu C, Liang C, Liu Z, et al. Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI. Eur J Radiol, 2013, 82(12): 782-789.
[21]
Lee YJ, Kim SH, Kang BJ, et al. Intravoxel incoherent motion (IVIM)-derived parameters in diffusion-weighted MRI: Associations with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging, 2017, 45(5): 1394-1406.
[22]
Liu C, Wang K, Chan Q, et al. Intravoxel incoherent motion MR imaging for breast lesions: comparison and correlation with pharmacokinetic evaluation from dynamic contrast-enhanced MR imaging. Eur Radiol, 2016, 26(11): 3888-3898.
[23]
Kim Y, Ko K, Kim D, et al. Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer: association with histopathological features and subtypes. Br J Radiol, 2016, 89(1063): 20160140.
[24]
Cho GY, Moy L, Kim SG, et al. Evaluation breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors. Eur Radiol, 2016, 26(8): 2547-2558.
[25]
Ostenson J, Pujara AC, Mikheev A, et al. Voxelwise analysis of simultaneously acquired and spatially correlated 18 F-fluorodeoxyglucose (FDG)-PET and intravoxel incoherent motion metrics in breast cancer. Magn Reson Med, 2016, 78(3): 1147-1156.
[26]
Che S, Zhao X, Ou Y, et al. Role of the intravoxel incoherent motion diffusion weighted imaging in the pre-treatment prediction and early response monitoring to neoadjuvant chemotherapy in locally advanced breast cancer. Medicine (Baltimore), 2016, 95(4): e2420.
[27]
Nogueira L, Brandão S, Matos E, et al. Application of the diffusion kurtosis model for the study of breast lesions. Eur Radiol, 2014, 24(6): 1197-1203.
[28]
Li Y, Ai T, Hu YQ, et al. The value of IVIM-DKI model in differentiating benign from malignant breast lesions. Radiology Practice, 2016, 31(12): 1191-1195.李嫣，艾涛，胡益祺，等．体素内不相干运动联合扩散峰度成像模型对乳腺良恶性病灶的鉴别诊断价值．放射学实践, 2016, 31(12): 1191-1195.
[29]
Lin Y, Huang Y, Lin WX, et al. Evaluation of diffusion kurtosis imaging and its combination with diffusion weighted imaging and proton MR spectroscopy in differentiation of breast lesions. Chin J Radiol, 2017, 51(5): 350-354.林艳，黄瑶，林伟洵，等．扩散峰度成像参数及其联合扩散加权成像与MR频谱参数鉴别乳腺良、恶性病变的价值．中华放射学杂志, 2017, 51(5): 350-354.
[30]
Sun K, Chen X, Chai W, et al. Breast cancer: diffusion kurtosis MR imaging-diagnostic accuracy and correlation with clinical-pathologic factors. Radiology, 2015, 277(1): 46-55.
[31]
Cheng F, Suo ST, Kang JW, et al. The study on the application value of DKI in the classification of invasive breast carcinoma and its correlation with prognostic factors. Chin J Magn Reson Imag, 2017, 8(3): 164-169.成芳，所世腾，康记文，等. MR扩散峰度成像在浸润性乳腺癌分级及与预后因素的相关性应用研究．磁共振成像, 2017, 8(3): 164- 169.
[32]
Wu D, Li G, Zhang J, et al. Characterization of breast tumors using diffusion kurtosis imaging (DKI). PLoS One, 2014, 11(9): e113240.
[33]
Christou A, Ghiatas A, Priovolos D, et al. Accuracy of diffusion kurtosis imaging in characterization of breast lesions. Br J Radiol, 2017, 90(1073): 20160873.
[34]
Panek R, Borri M, Orton M, et al. Evaluation of diffusion models in breast cancer. Med Phys, 2015, 42(8): 4833-4839.

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