# Image Quality Assessment to Emulate Experts' Perception in Lumbar MRI Using Machine Learning
## 4 Discussion
On the other hand, on natural images, correlation coefficients between subjective vs. predictive MOS have been obtained close to 80% [41]. When using reference images, correlation coefficients published are close to 95% [17] or 96% [42], using sparse representation and kernel ridge regression. Yet their implementation was applied to natural images, with the possibility of estimation of visual information fidelity. It would be interesting to apply these methodologies described in the state of the art to the lumbar MRI data set and compare their performance with the algorithm proposed in this research. However, some conditions prevent its realization. For example, in the case of the algorithm proposed by [PERSON] et al., it involved a Deep Learning model in its classifiers, which cannot be applied in our work due to the limited database. On the other hand, [PERSON] et al. proposed an automated estimation using specific features of CCTA, and the novelty of this method is largely in obtaining these features prior to the application of the Machine Learning model. Since the characteristics obtained in lumbar MRI are different, the method proposed by [PERSON] to our work does not have much scalability. Finally, [PERSON] et al. present a model very similar to the one developed in our research since they use the MRI image, extract its main characteristics and use the SVM as a classifier. Beyond the type of image used, a key step that differs between the two methods is the dimensionality reduction performed with the PCA in our work, so we consider it unnecessary to replicate what was developed by these authors. It is interesting to note that the results obtained here were obtained through the machine learning of the three experts, taking into account more than an individual point of view. In the case of this application, the agreement between experts was not always qualified as \"excellent\", and the machine must learn different points of view. This is a common problem faced in machine learning, and the results obtained here show good performance in general. The human judgment or decision are based in several variables that may seem reasonable, however there exists many unobserved information that cannot be captured. Meanwhile, machine learning methods rely only on the available data obtained from quantifiable features [43; 44; 45; 46]. These results are encouraging with respect to the possibility of developing an automated system that could monitor not only \"mathematical image quality\" but also image quality perceived by experts, who are the real final users of medical images. The method still needs to be fully automatized to avoid human interaction for the positioning of the Region-Of-Interest in the analysis. These are only preliminary results, as the proposed method was tested on three types of exams so far, and the number of exams, of sites and vendors needs to be increased. This would increase the number of observations of \"bad quality\" images obtained with no artificial manipulation, and therefore should refine the capacity of the method proposed here to discriminate between image qualities. One of the specificities of the images types that were selected here is that they were acquired with a spine MRI coil, which means that the signal within the images was not homogeneous. Working with these kinds of images does not represent conditions common to all kinds of MR images. There is a discussion on how to define image quality in medical applications. A crisp definition of good vs. bad quality was used in the present case. The use of a fuzzy definition of transition between types might bring the behavior of this system closer to that of the human experts. It is important to emphasize that this system should be apprehended as a constant monitoring solution and that the interest is not in detecting when one single exam was of poor quality but when, as a whole, the trend of the MR system is deteriorating from an image quality point of view.