## 4 Discussion  
We have studied the use of different DL models to estimate PDFF in the pancreas. NLV-Net demonstrated better agreement with the reference PDFF estimates, outperforming U-Net and MDWF-Net in terms of quantitative agreement at the pancreas head and body for six echoes. Only U-Net showed statistically significant differences for the pancreas head. This finding suggests that using different decoders for \(R_{2}^{*}\) and field inhomogeneity \(\phi\) improves the accuracy of PDFF. Furthermore, by using the DL model only to solve the non-linear part of the water-fat separation problem, as in the NLV-Net, the estimation of PDFF is improved. This is a significant advantage, as the network size can be reduced compared to MDWF-Net since water and fat signals do not have to be estimated by the DL mode, which facilitates its convergence. For three echoes, the results were not conclusive to address if one particular method was better than the other ones. For the three-echo images, U-Net demonstrated the strongest correlation to the six-echo reference in PDFF for the pancreatic head. However, NLV-Net had the best correlation for the pancreatic body. The Bland-Altman plots revealed that all methods had similar LoA, but MDWF-Net had the smallest bias for the pancreatic head. For the pancreatic body, all methods had similar LoA as well, but MDWF-Net exhibited the largest bias. Previous results from these networks in the liver, using three echoes, have shown promising performance compared to the standard reference. However, further investigation is needed to evaluate how these DL methods can improve their accuracy, particularly in cases of low fat content, as observed in the pancreas among the group of volunteers in this study. A few outliers were detected in the Bland-Altman analysis. Upon investigating these outliers across all methods, we found no specific reason for their presence. Interestingly, an outlier observed in one method was not present in the others. Since the exact same ROI position and area were used for each volunteer across all methods, we chose not to remove any outliers. Doing so could have introduced external bias into the results, potentially favoring or disadvantanging a particular method. This study also investigated the relationship between the amount of fat on the liver and pancreas from subjects with suspected MSFLD, using CSE-MRI images and DL models. Our finding using both the graph-cut method and NLV-Net showed the same results and was in contradiction to some previous studies [3,5,9]. We found a poor correlation between the PDFF of the right hepatic lobe and that of the pancreatic head and body in three- and six-echo MR images, reinforcing the concept that MASLD and pancreatic steatosis are not inherently related [8,9]. The relationship between pancreatic and hepatic steatosis has been a contentious area in previous studies, and some authors proposed a significant relation between pancreatic steatosis and MASLD. [PERSON] et al. [5] suggested that there was a significant correlation between pancreas steatosis and MASLD but found no correlation between MASLD and BMI or diabetes. In addition, [PERSON] et al. [3] suggested a significant association between the two conditions with BMI and obesity, possibly also with insulin resistance. Although [PERSON] et al. [9] discovered a correlation between the two conditions, they suggested this is likely due to their common dysmetabolic drivers and they still have separate pathophysiological processes. In our study, we considered the results obtained with NLV-Net and graph cut from six-echo MR images when assessing the correlation between the PDFF of liver and pancreas. We found differences between hepatic and pancreatic PDFF measurements and a poor correlation between them. There was no evidence to support the correlation between pancreatic steatosis and MASLD from our results. MASLD is one of the significant risk factors for pancreatic steatosis, yet not the only contributor [23].