## 4 Discussion and Conclusions
The major limitation of the proposed method is that, although the clustering approach is computationally efficient, the overall approach suffers from high computational load and time because of the Bayesian estimation. Compared to the previously proposed CAR [15], the computational time is not increased by the presence of different weights, but it is anyway high. A further limitation lies in the reduced number of \(b\)-values samplings that were considered in the tests. Though, in our results, the performance improvement seemed independent of the \(b\)-values, a future work might be focused on evaluating the impact of \(b\)-values choice on the performance. Finally, the proposed method was evaluated on a limited number of real cases, looking only at two body districts and at a reduced number of structures; therefore, its application to a larger dataset would allow a better evaluation of its impact from a clinical perspective. In conclusion, this paper described a novel IVIM fitting approach, based on embedding the \(k\)-means clustering approach in the Bayesian CAR method. The proposed method provided more accurate and robust parametric maps with respect to traditional voxelwise approaches, even at the tissue/tissue and tissue/background interfaces. The tool was proven to be effective in both simulated and real cases, and represents a valuable alternative to fit the bi-exponential IVIM model in biomedical applications. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12041907/s1](https://www.mdpi.com/article/10.3390/app12041907/s1)]([https://www.mdpi.com/article/10.3390/app12041907/s1](https://www.mdpi.com/article/10.3390/app12041907/s1)), Table S1: Median error \(\varepsilon\) for each simulated image at SNR10, SNR25 and SNR50 using the three different methods. HN: Head-and-Neck simulations; P: pelvis simulations; Table S2: Median error \(\varepsilon\) for each simulated image at SNR100, SNR150 and SNR200 using the three different methods. HN: Head-and-Neck simulations; P: pelvis simulations; Table S3: \(CV\) for each simulated image at SNR10, SNR25 and SNR50 using the three different methods. HN: Head-and-Neck simulations; HN: Head-and-Neck simulations; P: pelvis simulations. Conceptualization, E.S., A.M., G.R. and E.L.; methodology, E.S., A.M. and E.L.; formal analysis, E.S. and E.L.; data curation, E.S. ; writing--original draft preparation, E.S. and E.L.; writing--review and editing, E.S., A.M., G.R. and E.L.; supervision, G.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Ethics Committee of Regina Elena Scientific Institute, Rome, Italy (RS 266/12). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Part of the data presented in this study are openly available in Zenodo at [[https://doi.org/10.5281/zenodo.5924820](https://doi.org/10.5281/zenodo.5924820)]([https://doi.org/10.5281/zenodo.5924820](https://doi.org/10.5281/zenodo.5924820)), accessed on 9 February 2022. Restrictions apply to the availability of the clinical datasets used in this paper. Data were obtained from Regina Elena Scientific Institute, Rome, Italy and are available from the corresponding author with the permission of Regina Elena Scientific Institute. The authors acknowledge [PERSON] and [PERSON] from the Regina Elena National Cancer Institute, Rome, Italy, for having provided the images of real cases, already used in previous studies. The authors also acknowledge [PERSON] and [PERSON], for their support in the implementation of the clustering approach. The authors declare no conflict of interest.