# Image Quality Assessment to Emulate Experts' Perception in Lumbar MRI Using Machine Learning  
[PERSON]  
1 Escuela de Ingenieria C. Biomedica, Universidad de Valparaiso, Valparaiso 2362905, Chile;  
[EMAIL_ADDRESS] (I.M. ); [PERSON]@alumnos.uvcl (G.H. );  
carolina.saavedra@uvcl (S.C.); alejandro.volez@uvcl (A.V. );  
1  
[PERSON]  
1 Escuela de Ingenieria C. Biomedica, Universidad de Valparaiso, Valparaiso 2362905, Chile;  
[EMAIL_ADDRESS] (I.M. ); [PERSON]@alumnos.uvcl (G.H. );  
[PERSON]@uvcl (S.C.);  
[PERSON]@uvcl (J.S.C. ); [PERSON]@uvcl (R.S. );  
and [PERSON]  
1 Escuela de Ingenieria C. Biomedica, Universidad de Valparaiso, Valparaiso 2362905, Chile;  
[EMAIL_ADDRESS] (I.M. ); [PERSON]@alumnos.uvcl (G.H. );  
[PERSON]@uvcl (S.C.);  
[PERSON]@uvcl (J.S.C. ); rodrig  
characteristics, not much can be done. However, if the image quality is affected by an under-optimal acquisition protocol or by the equipment state, one might need to take some time to analyze the issue and to make the corresponding adjustments. Besides quality control realized with phantoms, image quality checking can be divided into two categories: a subjective assessment based on human judgment, and objective assessment, which is computed with mathematical algorithms on the resulting images [1]. A subjective assessment gives the results that are closest to the expert appreciation--in our case, a radiologist interpreting a medical image--but it consumes the highly valued time of an expert and, therefore, it is not practical to implement as a regular image quality control. An example can be found in [3], where a set of anatomical criteria for MR images of knee joints is proposed based on expert criteria. On the other hand, the objective assessment can be divided into two main categories: considering a comparison with respect to a reference image or considering no reference at all [4; 5; 6]. Image quality assessment based on a reference image is very useful when working on problems such as lossy compression, but in the context of medical image acquisition, usually, no reference is available. No reference image quality assessment (NR-IQA) is a challenging task, mainly based on strategies such as signal-to-noise ratio (SNR) estimation, entropy or different families of mathematical measures that are far-off from the human perception itself [7; 8; 9; 10; 11; 12; 13; 14; 15]. Several works have explored the possibility of modeling natural images [16], and the estimation of natural scenes statistic features [17]. Yet medical images are not purely natural scenes. An interesting solution has been proposed in the last few years, to evaluate the image quality not directly from the acquired images but through the evaluation of the result of an automatic processing pipeline [18; 19; 20]. This can be done, however, for a specific subset of applications, such as anatomical brain images, where a processing pipeline is consolidated. Image quality assessment has also been applied through the evaluation of diagnostic performance using the receiver operating characteristic curve (ROC) [21]. The purpose of this work is to propose a method to classify the image quality perceived by radiologists in magnetic resonance (MR) images. To be more specific, the focus was set on MR lumbar images, as they are one of the most common images acquired on MRI presenting quality issues, according to our local radiologists. We aim at qualifying the MR lumbar image quality emulating the expert perception. In a future step, this evaluation of \"good\" or \"bad\" quality images reflecting what radiologists would judge, could then be obtained automatically at the moment of image acquisition and serve as a \"traffic light\" indication. A recurrent \"bad\" quality image would support actions to re-evaluate either the acquisition protocol, or the magnet maintenance or further analysis of what would cause reduced image quality.