# Image Quality Assessment to Emulate Experts' Perception in Lumbar MRI Using Machine Learning
## 5 Conclusions  
In conclusion, a method is presented here, where feasibility of the emulation of expert perception of image quality in three types of lumbar MR images is shown. Good accuracy is obtained in the set of images used, Sagittal \(T_{1}\), Sagittal \(T_{2}\), and Axial \(T_{1}\), of 75 \(\pm\) 2.4% on average in the testing condition, using a Support Vector Machine to construct the classifier. Using a non linear combination of quality features extracted from the images, an emulation is obtained of the combined views of three different experts, whose agreement on image quality varies between fair and substantial. Even though the actual implementation still relies on user interaction to extract certain features from the images, the results are promising with respect to a potential implementation in monitoring image quality online with the image acquisition process.  
Future works could include further features, such as block kurtosis of DCT coefficients [47], dominant eigenvalues of the covariance matrix [48] or kernel ridge regression [42] for instance. Other machine learning techniques, closer to deep neural networks [49], might also improve the performance of image assessment. The method in its essence can be applied to other kind of images, while modifying the definition of localization of ROI in agreement with the organs observed. All other features proposed in Table 2 can be extracted for different kinds of medical images.  
Further work is needed to confirm the observations in other experimental conditions and to other types of images, using for instance MR images in other anatomical area, or computed tomography images. The automatic assessment of medical image quality is probably an issue that will occur more frequently as many artificial intelligence systems are developed based on large-scale databases, whose quality has been questioned [50]. Moreover, this study could be extended by increasing the number of subjective evaluators and introducing multi-criteria decision-making techniques [51] to support the variability among the users.  
The contributions of each author to the research carried out are listed below: conceptualization, S.C., P.C., R.R., J.V. and R.S.; methodology, S.C., P.C., R.R., J.V., M.Q., C.S., A.V. and R.S.; validation, S.C., P.C., R.R., J.V. and R.S.; formal analysis, S.C., J.S.C., L.M., C.S., A.V. and R.S.; investigation, S.C., J.S.C., C.S. and A.V.; data curation, L.M., Gamaliei Huerta; writing--original draft preparation, S.C., J.S.C., M.Q. and R.S.; writing--review and editing, L.M., P.C., R.R., J.V., C.S., M.Q. and A.V.; funding acquisition, S.C. and R.S. All authors have read and agreed to the published version of the manuscript.  
This publication has received funding from Millennium Science Initiative of the Ministry of Economy, Development and Tourism, grant Nucleus for Cardiovascular Magnetic Resonance.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of SERVICIO DE SALUD VALPARAISO SAN ANTONIO (protocol code 50/2016, date of approval 19 October 2016).  
As data was extracted from a previously anonymized database in the hospital, with no possibility for the investigators or other person to access the identity of the individual, with the aim of the study focused on the technical quality of images, the Ethics Committee granted the authorization of waiving patient consent.  
The ethical committee did not authorize the release of the data publicly. However, we have used public dataset 1 from the SpineWeb database available at [[http://spineweb.digitalimaginggroup.ca/Index.php?n=Main.Datasets](http://spineweb.digitalimaginggroup.ca/Index.php?n=Main.Datasets)]([http://spineweb.digitalimaginggroup.ca/Index.php?n=Main.Datasets](http://spineweb.digitalimaginggroup.ca/Index.php?n=Main.Datasets)).  
The authors declare no conflict of interest.