Ashley M Stokes^{1}, Jack T Skinner^{2}, Laura C Bell^{1}, Adrienne N Dula^{3}, Thomas E Yankeelov^{3}, and C. Chad Quarles^{1}

The purpose of this study is to investigate the influence of post-processing method on the reproducibility of brain diffusion metrics, including apparent diffusion coefficients (ADCs) and intra-voxel incoherent motion (IVIM) parameters, in healthy controls and to apply these results in a cohort of brain tumor patients undergoing treatment. ADC was highly reproducible for all methods. The IVIM diffusion and perfusion fraction showed the highest reproducibility using constrained fitting, while IVIM pseudo-diffusion showed limited reproducibility. By establishing limits of repeatability for ADC and IVIM metrics, these methods can be applied in neuropathology to determine significant changes related to treatment effects.

1. Chenevert TL, Stegman LD, Taylor JMG, et al. Diffusion Magnetic Resonance Imaging: an Early Surrogate Marker of Therapeutic Efficacy in Brain Tumors. Journal of the National Cancer Institute 2000;92(24):2029-2036.

2. Padhani AR, Liu G, Mu-Koh D, et al. Diffusion-Weighted Magnetic Resonance Imaging as a Cancer Biomarker: Consensus and Recommendations. Neoplasia 2009;11(2):102-125.

3. Grech-Sollars M, Hales PW, Miyazaki K, et al. Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain. Nmr Biomed 2015;28(4):468-485.

4. Meeus EM, Novak J, Withey SB, Zarinabad N, Dehghani H, Peet AC. Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue. Journal of Magnetic Resonance Imaging 2016.

5. Galbraith SM, Lodge MA, Taylor NJ, et al. Reproducibility of dynamic contrast-enhanced MRI in human muscle and tumours: comparison of quantitative and semi-quantitative analysis. Nmr Biomed 2002;15(2):132-142.

6. Cohen AD, LaViolette PS, Prah M, et al. Effects of perfusion on diffusion changes in human brain tumors. Journal of magnetic resonance imaging : JMRI 2013;38(4):868-875.

Table 1: Mono-exponential ADC and bi-exponential
IVIM fitting methods.

Figure 1a-d: DWI signals and example fits for ADC_{Method1} (a,b) and
IVIM_{Method1} (c,d) in WM (a,c) and GM (b,d) ROIs in a representative
healthy control at 3 time points (blue: time 0, magenta: 1 week, green: 4
week). The mean (± SD) across time is shown within each plot. e: Representative maps for ADC_{Method1} and IVIM_{Method1} across
time.

Figure 2: Boxplots showing influence of post-processing method on ADC, IVIM-*D*, IVIM-*f*_{p}, and IVIM-*D**
for all controls in WM and GM over time (T0, T1, and T4).

Figure 3: Bland-Altman mean-difference plots for Method 1 ADC (standard method)
and Method 2 IVIM (constrained fit). It should be noted that Method 2 IVIM-*D* is equivalent to Method 3 ADC and is
insensitive to perfusion effects. The dotted lines indicate repeatability
limits for healthy control WM and GM (black and gray lines, respectively, n =
9). Tumor mean-difference (red diamonds, n = 6) demonstrated several 1-week
changes in ADC, IVIM-*D*, and IVIM-*f*_{p} outside of the WM and GM
repeatability limits. The wide repeatability limits for IVIM-*D** precluded significant changes for
most tumors.