LU Yi,CHEN Tao,PENG Wenwen, et al. Repeatability analysis of different mathematical models of magnetic resonance diffusion weighted imaging in rat brain[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2019, 49(7): 491-496.
Abstract:Objective: To evaluate the repeatability of different magnetic resonance diffusion weighted imaging (DWI) mathematical modelsin the brains of rats, such as the apparent diffusion coefficient (ADC) model, intravoxel incoherent motion (IVIM) model, diffusion kurtosis imaging (DKI) model and stretched exponential model. Methods: There were 14 male Sprague-Dawley rats experienced DWI scanning in 3.0T scanner twice, in 24-hours intervals. The data was analyzed with different diffusion mathematical models. The intra-subject coefficient of variation (intra-subject CV) and Bland-Altman analysis were used to evaluate the repeatability of the experiment. Results: In the ADC model, DKI model and stretched exponential model, the Cronbach’s alpha of these diffusion related parameters (ADC, Dapp and DDC) were less than 5%, intra-subject CV were less than 0.7, and LA were less than 0.15×10-3 mm2/s. Moreover, ADC had the smallest of LA, which was 0.086×10-3 mm2/s. Cronbach’s alpha of all the coefficients of the IVIM model were less than 0.5, intra-subject CV were more than 40%, and LA were greater than 0.4×10-3 mm2/s. Among different regions of Interest, minimum intra-subject CV and LA of ADC were in striatum, respectively: 2.00% and 0.034×10-3 mm2/s. And Corpus callosum had the highest intra-subject CV and LA of ADC, respectively 3.98% and 0.085×10-3 mm2/s. Conclusion: The repetitive analysis results of different diffusion mathematical models, which from the DWI of rat’s brain in a intervals of 24 hours, are significantly different. The repeatability of ADC model is the best, and IVIM model is the worst. In addition, the repeatability of ADC value in different brain ROIs of rats are also significantly different, which shows that striatum has a best repeatability, and corpus callosum is relatively poor.
[1] CHENEVERT T L, STEGMAN L D, TAYLOR J M, et al. Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors[J]. J Natl Cancer Inst, 2000, 92(24): 2029-2036.
[2] KAMEL I R, BLUEMKE D A, RAMSEY D, et al. Role of diffusion-weighted imaging in estimating tumor necrosis after chemoembolization of hepatocellular carcinoma[J]. AJR Am J Roentgenol, 2003, 181(3): 708-710.
[3] 陈中伟, 赵悠帆, 叶琼, 等. 计算扩散加权成像对脑肿瘤显示能力的评估[J]. 温州医科大学学报, 2018, 48(10): 769-772.
[4] IIMA M, LE B D. Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future[J]. Radiology, 2016, 278(1): 13-32.
[5] ROSENKRANTZ A B, PADHANI A R, CHENEVERT T L, et al. Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice[J]. J Magn Reson Imaging, 2015, 42(5): 1190-1202.
[6] KORAL K, MATHIS D, GIMI B, et al. Common pediatric cerebellar tumors: correlation between cell densities and apparent diffusion coefficient metrics[J]. Radiology, 2013, 268(2): 532-537.
[7] PADHANI A R, LIU G, KOH D M, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations[J]. Neoplasia, 2009, 11(2):102-125.
[8] BAE H, YOSHIDA S, MATSUOKA Y, et al. Apparent diffusion coefficient value as a biomarker reflecting morphological and biological features of prostate cancer[J]. Int Urol Nephrol, 2014, 46(3): 555-561.
[9] MAZAHERI Y, VARGAS H A, AKIN O, et al. Reducing the influence of b-value selection on diffusion-weighted imaging of the prostate: evaluation of a revised monoexponential model within a clinical setting[J]. J Magn Reson Imaging, 2012, 35(3): 660-668.
[10] YABLONSKIY D A, SUKSTANSKII A L. Theoretical models of the diffusion weighted MR signal[J]. NMR Biomed, 2010, 23(7): 661-681.
[11] KOH D M, BLACKLEDGE M, COLLINS D J, et al. Reproducibility and changes in the apparent diffusion coefficients of solid tumours treated with combretastatin A4 phosphate and bevacizumab in a two-centre phase I clinical trial[J]. Eur Radiol, 2009, 19(11):2728-2738.
[12] DORRIUS M D, DIJKSTRA H, OUDKERK M, et al. Effect of b value and pre-admission of contrast on diagnostic accuracy of 1.5-T breast DWI: a systematic review and meta-analysis[J]. Eur Radiol, 2014, 24(11): 2835-2847.
[13] KURU T H, ROETHKE M C, STIELTJES B, et al. Intravoxel incoherent motion (IVIM) diffusion imaging in prostate cancer-what does it add?[J]. J Comput Assist Tomogr, 2014, 38(4): 558-564.
[14] DöPFERT J, LEMKE A, WEIDNER A, et al. Investigation of prostate cancer using diffusion-weighted intravoxel incoherent motion imaging[J]. Magn Reson Imaging, 2011, 29(8): 1053-1058.
[15] MAZZONILN, LUCARINI S, CHITI S, et al. Diffusion-weighted signal models in healthy and cancerous peripheral prostate tissues: comparison of outcomes obtained at different b-values[J]. J Magn Reson Imaging, 2014, 39(3): 512-518.
[16] FOURNET G, LI J R, CERJANIC A M, et al. A two-pool model to describe the IVIM cerebral perfusion[J]. J Cereb Blood Flow Metab, 2017, 37(8): 2987-3000.
[17] LIU L, MA C, LI J, et al. Comparison of the diagnostic performances of three techniques of ROI placement for ADC measurements in pancreatic adenocarcinoma[J]. Acad Radiol, 2015, 22(11): 1385-1392.