Influence of Acquisition Time on MR Image Quality Estimated with Nonparametric Measures Based on Texture Features

Abstract

Correlation of parametrized image texture features (ITF) analyses conducted in different regions of interest (ROIs) overcomes limitations and reliably reflects image quality. The aim of this study is to propose a nonparametrical method and classify the quality of a magnetic resonance (MR) image that has undergone controlled degradation by using textural features in the image. Images of 41 patients, 17 women and 24 men, aged between 23 and 56 years were analyzed. T2-weighted sagittal sequences of the lumbar spine, cervical spine, and knee and T2-weighted coronal sequences of the shoulder and wrist were generated. The implementation of parallel imaging with the use of GRAPPA2, GRAPPA3, and GRAPPA4 led to a substantial reduction in the scanning time but also degraded image quality. The number of degraded image textural features was correlated with the scanning time. Longer scan times correlated with markedly higher ITF image persistence in comparison with images computed with reduced scan times. Higher ITF preservation was observed in images of bones in the spine and femur as compared to images of soft tissues, i.e., tendons and muscles. Finally, a nonparametrized image quality assessment based on an analysis of the ITF, computed for different tissues, correlating with the changes in acquisition time of the MR images, was successfully developed. The correlation between acquisition time and the number of reproducible features present in an MR image was found to yield the necessary assumptions to calculate the quality index.

Shape and Enhancement Analysis as a Useful Tool for the Presentation of Blood Hemodynamic Properties in the Area of Aortic Dissection

“…Furthermore, extraction of centerline for the vessel reconstruction is used [15]. However, none of the imaging techniques allow the prediction blood hemodynamic after surgical intervention [25][26][27]. The image processing of medical data allows the provision of realistic in vivo conditions for patient specific analysis, e.g., reliable anatomical 3D geometries of human cardiovascular system [28].…”

Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter

“…More sophisticated approaches additionally analyze local changes in pixel intensities (e.g., Gray-level Co-occurrence Matrix); others try to mimic the way the human visual system works (e.g., Gray Tone Difference Matrix) [ 31 , 32 ]. Since the texture operator has already proved its sensitiveness for tiny changes resulting from noise introduction [ 33 ], it is believed the pollution recorded on images may also be visible. Moreover, in the presented research, a texture operator, which returns a short vector of values that could describe the image quality, was needed (which in assumption should vary depending on air condition).…”

Effect of Matrix Size Reduction on Textural Information in Clinical Magnetic Resonance Imaging

“…The resulting image, besides having a specific brightness, contrast, and resolution, is also often characterized by a certain texture [15]. This is a source of visual information about physical objects obtained by mapping their structures in an image.…”

“…To the best of our knowledge, influence of matrix size on image quality was studied based on phantom acquisition using the variable SNR value as a reference. However, important drawbacks of SNR as a parametric quality measure are known [15]. The SNR describes the amount of signal in relation to the background noise but does not reflect changes and the preservation of a signal in the image parts which represents anatomical structures.…”

“…Magnitudes of Gabor transform (GAB) calculated in four directions: vertical, horizontal, 45 The feature values of the texture parameters were calculated for each patient and acquisition matrix; the results were obtained for all patients and correlated in pairs for any matrix. Following the methodology described by [15,17], Lin's concordance correlation coefficient (ρ c ) was estimated between the textural features obtained for different acquisition matrices. In each analysis, the sets of textural features (obtained for all patients, for which the given structures were visualized) were considered.…”