## 4 Discussion
To achieve this, it is essential to use standardized protocols for MRI acquisition, including imaging parameters, patient positioning, and scanner calibration. 3. Automated segmentation: Accurate segmentation of bone and bone marrow regions in MRI images is essential for reliable TA analysis. Manual segmentation is subject to inter- and intra-observer variability, which can affect the reproducibility of TA features. Automated segmentation algorithms, such as thresholding or machine learning-based methods, can improve the accuracy and reproducibility of segmentation. 4. Standardized TA parameters: There are many different TA features that can be derived from MRI images, and the choice of features can affect the reliability and reproducibility of BMD assessment. To address this issue, it is important to use standardized TA parameters that have been validated in previous studies. 5. Calibration phantoms: Calibration phantoms can be used to ensure that the MRI images are consistent across different scanners and imaging protocols. These phantoms contain known concentrations of water and/or oil, and their signal intensity can be used to calibrate the MRI images. 6. Multi-site studies: Multi-site studies involving different MRI scanners and patient populations can help to validate the reliability and reproducibility of TA features. By comparing the results from different scanners and patient cohorts, the robustness and generalizability of TA features can be evaluated. In summary, several approaches can be used to increase the reliability of TA features derived from MRI for BMD assessment. These include image quality control, reproducible image acquisition, automated segmentation, standardized TA parameters, calibration phantoms, and multi-site studies [11]. Dual-energy X-ray absorptiometry (DXA) is the gold standard for BMD assessment, and it measures the attenuation of X-rays passing through the bone. DXA provides a two-dimensional image of the bone and reports the BMD as grams of mineral per square centimeter (g/cm\({}^{2}\)). DXA is widely used because it is reliable, non-invasive, and has a low radiation dose. Several studies have investigated the correlation between TA on MRI and DXA for BMD assessment. Overall, the results suggest that TA can provide additional information about bone quality beyond what DXA measures. For example, TA can quantify the heterogeneity of the trabecular bone and bone marrow, which may be important for predicting fracture risk. Some studies have reported a moderate to strong correlation between TA-derived parameters and DXA-derived BMD measurements. However, the strength of the correlation varies depending on the TA parameters and DXA sites analyzed. For instance, a study by [PERSON] et al. (2020) found a moderate correlation between TA parameters and DXA-derived BMD measurements at the lumbar spine [12]. This study reports comparable results; however, the correlation is stronger when analyzed with the appropriate model: machine learning can help greatly when dealing with many variables with unknown relationships. However, the appropriate model selection can impact the accuracy of the prediction [13]. In this study, the single-layer perceptron was used, for the relative simplicity of the architecture and the possibility to oversee the training and test phases, monitoring the gain/cost function, to help prevent overfitting [14]. According to [PERSON] et al. [4], GLNU and entropy hold significant value in predicting BMD from an opportunistic use of a conventional MRI scan. This study confirms this result when adopting the linear regression modelling; yet, in the multilayer perceptron approach, the NGLDM busyness appears to be more important. However, this feature describes a semantically similar property of the image, thus not deviating conceptually from the previously reported results. Moreover, the direct relationship between entropy and BMD is demonstrated by [PERSON] et al. [3].