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

Abstract

The selection of the matrix size is an important element of the magnetic resonance imaging (MRI) process, and has a significant impact on the acquired image quality. Signal to noise ratio, often used to assess MR image quality, has its limitations. Thus, for this purpose we propose a novel approach: the use of texture analysis as an index of the image quality that is sensitive for the change of matrix size. Image texture in biomedical images represents tissue and organ structures visualized via medical imaging modalities such as MRI. The correlation between texture parameters determined for the same tissues visualized in images acquired with different matrix sizes is analyzed to aid in the assessment of the selection of the optimal matrix size. T2-weighted coronal images of shoulders were acquired using five different matrix sizes while maintaining the same field of view; three regions of interest (bone, fat, and muscle) were considered. Lin’s correlation coefficients were calculated for all possible pairs of the 310-element texture feature vectors evaluated for each matrix. The obtained results are discussed considering the image noise and blurring effect visible in images acquired with smaller matrices. Taking these phenomena into account, recommendations for the selection of the matrix size used for the MRI imaging were proposed.

Advances in Musculoskeletal Imaging and Their Applications

“…Such parameters, if properly selected and repeatable, are very useful for reliable characterization of internal human organs independently of the image modality used. It has been demonstrated that texture analysis can be usefully implemented for classifying median nerves in carpal tunnel syndrome in echo images [3], diagnosis of cysts and granulomas in intra-oral radiographs [4] or quality assessment of MR images [5]. There is also free software available for texture feature evaluation, such as MaZda [6] and its successor qMaZda [7], widely used by many researchers to facilitate texture analysis in many medical image processing tasks.…”