# Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data
## 3 Results  
Figure 3 illustrates five short-axis representative slices of late gadolinium enhancement PSIR (LGE-PSIR), native T1, and the corresponding ECV maps along with b = 0 s/mm\({}^{2}\) and b = 350 s/mm\({}^{2}\) images of an infarcted swine heart. Figure 3: LGE-PSIR, native T1, ECV maps, b = 0 s/mm\({}^{2}\), and b = 350 s/mm\({}^{2}\) images for five representative short-axis slices of the same infarcted swine subject. LGE-PSIR is a contrast-based imaging technique, and native T1 and diffusion images (NDWI and DWI) are non-contrast-based techniques. LGE-PSIR images, along with their corresponding cDTI label maps and diffusion tensor quantities maps for the same five representative short-axis slices, are reported in Figure 4. As described in the Method section, the label maps were used to subdivide the corresponding FA, MD, e\({}_{1}\), e\({}_{2}\), e\({}_{3}\), and RD maps in remote, border, and infarct regions. The border and infarct zones are distinguishable from the remote myocardium due to their markedly hyper-enhanced nature in the LGE-PSIR images. In the following tensor invariants and eigenvalue maps, the infarct and border zones exhibit a higher MD, e\({}_{1}\), e\({}_{2}\), e\({}_{3}\), RD and a lower FA compared to the remote myocardium. Figure 4: LGE-PSIR and corresponding label maps registered to cDTI along with fractional anisotropy (FA), mean diffusivity (MD), primary eigenvalue (e\({}_{1}\)), secondary eigenvalue (e\({}_{2}\)), tertiary eigenvalue (e\({}_{3}\)), and radial diffusivity (RD) maps for five representative short-axis slices in an infarcted subject. In Figure 5, raincloud and box plots illustrate the distributions and quantitative differences of tensor invariants, eigenvalues, radial diffusivity, ECV, and native T1 across infarct, border, and remote regions. The 1 st quartile (25 th percentile) and the 3 rd quartile (75 th percentile) are marked by the lower and upper edges of the boxplot, respectively. The bottom and top whiskers mark the smallest and largest values within 1.5 times the interquartile range measured from the 25 th and 75 th percentiles, respectively. A summary of the mean, median, 1 st, and 3 rd quartiles over all subjects for all measured quantities are listed in Table 1.  
Among native T1, diffusion tensor invariants, eigenvalues, and RD, the largest percent differences are observed for e\({}_{2}\) (e\({}_{2}\) increases by 19.2% and 31.9% from remote to border and infarct zones, respectively), e\({}_{3}\) (e\({}_{3}\) increases by 16.1% and 33.4% from remote to border and infarct zones, respectively), and RD (RD increases by 18% and 32.3% from remote to border and infarct zones, respectively). Overall percentage changes in median values for native T1, ECV, diffusion tensor invariants, eigenvalues, and RD between infarct, border, and remote regions are detailed in Table 2.  
The computed \(p\) values were less than 0.01 for the statistical analyses carried out by grouping together the data across all subjects (Table 2), therefore showing that the observations in remote, border, and infarct regions did not originate from the same distribution. However, when the same statistical analyses were performed subject-wise, the resultant \(p\) values were greater than 0.01 in a few cases, especially in the native T1 distributions between infarct and border regions. All \(p\) values resulting from the pairwise non-parametric Kruskal-Wallis test for each subject are listed in Table 3.