# Comparison of Algorithms to Compute Relaxation Time Maps in Magnetic Resonance Imaging
## 5 Conclusions  
The examples presented in the study demonstrate that the RD-NLS algorithm outperforms other commonly used algorithms, such as Levenberg-Marquardt or Nelder-Mead, in fitting relaxation time maps. The study's results suggest that the RD-NLS algorithm can provide a more accurate estimate of the relaxation time, resulting in a higher degree of accuracy in the relaxation time maps. As seen in test 1, T1 maps fitted using different algorithms show differences in average T1, which can be as large as 14 ms. This shows that the choice of fitting algorithm can potentially affect T1 quantification. The study's authors recommend using the RD-NLS algorithm for obtaining relaxation time maps, as it is more effective than other algorithms in fitting image data to models. This algorithm can lead to more precise and accurate results, which is particularly important in medical imaging, where precise measurements are critical to the diagnosis and treatment of various conditions. Additionally, the authors highlight that the RD-NLS algorithm has several advantages, such as that it is relatively simple to use, can provide solutions even in cases where the cost function has multiple local minima, and does not require initial estimates. In conclusion, the use of the RD-NLS algorithm for obtaining relaxation time maps is highly recommended by the authors due to its superior performance in fitting image data to models. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13074083/s1](https://www.mdpi.com/article/10.3390/app13074083/s1)]([https://www.mdpi.com/article/10.3390/app13074083/s1](https://www.mdpi.com/article/10.3390/app13074083/s1)), Figure S1: Bland-Altman plot corresponding to Test 1. It is evident that the T1 obtained by [PERSON]'s fit is greater than that obtained by RD-NLS. Figure S2: Bland-Altman plot corresponding to Test 2. T1 obtained by Matlab's fit is slightly greater than the one attained by RD-NLS. Figure S3: Bland-Altman plot corresponding to Test 3. T1 obtainedby Matlab's fit is similar to the one obtained by RD-NLS. Figure S4: Bland-Altman plot corresponding to Test 4. T1 obtained by Matlab's fit is similar to the one obtained by RD-NLS. Figure S5: Bland-Altman plot corresponding to Test 5. Note that the T1 obtained by Matlab's fit is lower than the one obtained by RD-NLS. Figure S6: Bland-Altman plot corresponding to Test 6. T1 obtained by Matlab's fit is similar to the one obtained by RD-NLS. Figure S7: Bland-Altman plot corresponding to Test 7. Note that the T1 obtained by Matlab's fit is lower than the one obtained by RD-NLS. Figure S8: Bland-Altman plot corresponding to Test 8. The T1 values show an important dispersion. The results show that the T1 obtained by Matlab's fit is greater than the one obtained by RD-NLS. Figure S9: Bland-Altman plot corresponding to Test 9. T1 values show an important dispersion. The figure shows that the T1 obtained by Matlab's fit is greater than the one obtained by RD-NLS. Figure S10: Bland-Altman plot corresponding to Test 10. T1 values show an important dispersion. The T1 obtained by Matlab's fit is similar to the one computed by RD-NLS. Figure S11: Bland-Altman plot corresponding to Test 11. T2 values show a very low dispersion, except for two outlier points. Note that the T2 calculated by [PERSON]'s fit is similar to the one determined by RD-NLS. Figure S12: Bland-Altman plot corresponding to Test 12. T2 values show a very low dispersion, except for one outlier point. The T2 attained by Matlab's fit is almost similar to the one obtained by RD-NLS. Figure S13: Bland-Altman plot corresponding to Test 13. T2 values show a very low dispersion. The T2 calculated by Matlab's fit is almost the same as the one determined by RD-NLS. Figure S14: Bland-Altman plot corresponding to Test 14. T2 values show a very low dispersion. Note the similarity between the T2 values obtained by Matlab's and RD-NLS fits. Table S1: Summary of parameters used for Model I.