# Image Quality Assessment to Emulate Experts' Perception in Lumbar MRI Using Machine Learning
## 4 Discussion  
The agreement that is observed between the three radiologists, in Table 3, vary from fair to substantial. Even though it is usually easy to be in agreement to establish a diagnosis, to reach agreement on subjective quality perception led to more discussions. Two examples can be found in [34,35], where agreement among experts was moderate in subjective determination of glaucomatous visual field profession, and in subjective evaluation of sublingual microcirculation images, respectively. In our understanding, the observed differences in subjective evaluations could reflect the different manners that the neuro-radiologists use in their interactions with the images. Each of them was trained in a different school, some began practicing neurosurgery before neuro-radiology which might reflect different subjective expectation on image quality. The training radiologists receive has been shown to influence their behavior in reading images [36]. Differences in perceptions also could come from their differences in experience, such as [36,37] emphasize. In our understanding, the differences in subjective perceptions seen in this work reflect the reality of the existence of a range of experts evaluations. The system proposed here includes the variety of experts perceptions, in a way that would be more potentially more robust and more generalizable in future works, than one that reflects only the subjective evaluation of one expert, be it the most experienced perception or not. The experts' perception of image quality is emulated with good accuracy, 75.3 \(\pm\) 2.4% on average in the testing condition, using the Support Vector Machine. A wide range of features was extracted either from the entire image or from specific user-defined ROI in relation to lumbar anatomy. Features include characteristics known to influence image perception, such as signal to noise ratio or spatial resolution, but other less \"intuitive\" parameters were also taken into account such as spectral flatness or entropy. The image quality evaluation obtained from the non-linear combination of these characteristics, shown in Table 4, is in better agreement with the experts' view than each of the features taken individually, as depicted in Figure 4. Comparing the results obtained with the literature is complex since there is no similar model based on the same set of image type and machine learning technique used. However, it can be mentioned that the performance obtained is lower than reported by [PERSON] et al. [38], who evaluated the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA), obtaining an AUC of 0.96 and a kappa index for the agreement between automated and visual IQ assessment of 0.67. Similarly, the performance obtained is lower than that reported by [PERSON] et al. [39], who proposed a new machine-learning-based reference-free MR image quality assessment framework, including the concept of active learning and applying classifiers such as SVM and Deep Neural Networks. Although these authors report a high percentage of accuracy (93.7%), they did not perform an evaluation of concordance with experts, making this comparison difficult. However, performance results were closer to reported by [PERSON] et al. [40], who applied an SVM \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **SVM vs. NR\({}_{1}\)** & **SVM vs. NR\({}_{2}\)** & **SVM vs. NR\({}_{3}\)** & \\ \hline Sagittal \(T_{1}\) & 0.53 & 0.37 & 0.56 & 0.49 \(\pm\) 0.10 \\ Sagittal \(T_{2}\) & 0.52 & 0.49 & 0.42 & 0.48 \(\pm\) 0.05 \\ Axial \(T_{1}\) & 0.38 & 0.41 & 0.33 & 0.37 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Kappa index of agreement between SVM results and each neuro-radiologist (NR). algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house and obtaining an accuracy of 80%.