Anouk van Rijn^{1}, Alexander Leemans^{1}, Geert Jan Biessels^{2}, and Alberto de Luca ^{1}

^{1}Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, ^{2}Department of Neurology, University Medical Center Utrecht Brain Center, Utrecht, Netherlands

DTI metrics are often used without assessing the goodness of fit of the estimation model. We investigated local changes in fit residuals induced by pathology. To this end, a template of expected normalized residuals was created with 10 healthy controls, then voxel-wise comparisons were performed against the residuals of subjects affected by dementia. Results show that the residuals of infarcted regions are significantly different as compared to healthy tissue, whereas no differences were observed in hyperintensities. The fit residuals of the DTI model can be used to complement the information of DTI metrics at detecting microstructural changes in brain lesions.

with K being the number of DW volumes, DWI

DWI

1. Kristoffersen, A. (2011). Statistical
assessment of non‐Gaussian diffusion models. *Magnetic resonance in medicine*, 66(6),
1639-1648.

2. Leemans, A. J. B. S. J. J. D. K., Jeurissen, B., Sijbers, J., & Jones, D. K. (2009, April). ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In Proc Intl Soc Mag Reson Med (Vol. 17, p. 3537).

3.
Leemans, A.,
& Jones, D. K. (2009). The B‐matrix must be
rotated when correcting for subject motion in DTI data. *Magnetic
Resonance in Medicine: An Official Journal of the International Society for
Magnetic Resonance in Medicine*, 61(6), 1336-1349.

4.
Tax, C. M., Otte, W. M., Viergever, M. A.,
Dijkhuizen, R. M., & Leemans, A. (2015). REKINDLE: robust extraction of kurtosis INDices with linear
estimation. *Magnetic Resonance in Medicine*, 73(2),
794-808.

5.
Klein, S., Staring, M., Murphy, K., Viergever,
M. A., & Pluim, J. P. (2009). Elastix: a
toolbox for intensity-based medical image registration. *IEEE
transactions on medical imaging*, 29(1), 196-205.

6.
Collins, D. L.,
Neelin, P., Peters, T. M., & Evans, A. C. (1994). Automatic 3D intersubject
registration of MR volumetric data in standardized Talairach space. *Journal of
computer assisted tomography*, 18(2), 192-205.

7. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), 289-300.

Figure 1: A schematic overview of a part of the image processing
pipeline including the normalization of the residuals and the voxel-wise
comparison of residuals between controls and a patient.

Figure 2: Two axial slices showing the mean
residuals (first column), FA (second column), and the MD (third column), per
tissue class. The upper example slice contains a cortical infarct, while the
lower example slice contains white matter hyperintensities.

Figure 3: The upper row shows the white matter hyperintensity
burden as annotated by a specialist. The mean residuals, FA, and MD significant
differences in a patient with Alzheimer as compared to controls are shown in
row two till four. The background is the T1w image in MNI space and the colored
regions indicate the significant differences of a z-test, comparing an atlas of healthy controls
and a patient (FDR corrected p-values of p<0.0004, p<0.0006, p<0.006
respectively).

Figure 4: The upper row shows the ROIs as
annotated by a specialist (red is the cortical infarct, blue is the
hyperintensity). Mean residuals, FA, and MD significant differences in a patient
with a cortical infarct and MCI are shown in row two till four. The background
is the T1w image in MNI space and the colored regions indicate the significant
differences of a z-test, comparing an atlas of healthy controls and a patient (FDR
corrected p-values of p<0.0004, p<0.0006, p<0.006 respectively).

Figure 5: Statistical distribution of the mean residuals, FA and MD for one
patient as violin plots for different ROIs. The
x-axis shows the grey matter (GM), white matter (WM), cerebrospinal fluid (CSF),
white matter hyperintensity (WMH), lacunar infarct (LI), and cortical infarct
(CI).