Table 2 ANOVAs’ results describing the significance of findings over the test configurations in comparison with our previous method4.

From: Adaptive simulation of 3D thermometry maps for interventional MR-guided tumor ablation using Pennes’ bioheat equation and isotherms

Variable

df

F

p

Sig.

\(\eta ^2\)

Accuracy

Algorithm for all phantoms (overall)

2

23.97

<0.001

*

0.48

Algorithm for perfusion phantoms

2

6.82

0.001

*

0.43

Algorithm for homogeneous phantoms

2

77.69

<0.001

*

0.89

Robustness

Local threshold

84

0.96

0.44

 

0.02

Global threshold

84

1.03

0.42

 

0.02

Reference configuration

12

10.38

0.007

*

0.21

Test configuration 1

12

7.86

0.016

*

0.14

Test configuration 2

12

5.95

0.031

*

0.15

Test configuration 3

12

8.12

0.015

*

0.07

Test configuration 4

12

10.09

0.008

*

0.23

Test configuration 5

12

11.14

0.006

*

0.12

Test configuration 6

12

7.05

0.021

*

0.16

Test configuration 7

12

5.5

0.037

*

0.13

  1. The configurations are separated in accuracy and robustness tests. To ensure reproducibility of the p-values the following values are reported: Df = degrees of freedom, F = F-value, p = probability of the data given the null hypothesis, Sig. = p-values less than the traditional \(\alpha\) <0.05 are marked with a “*”, \(\eta ^2\) = Generalized Eta-Squared measure of effect size.
Back to article page