Strzelecki Michał, Piórkowski Adam, Obuchowicz Rafał
Institute of Electronics, Lodz University of Technology, 90-924 Lodz, Poland.
Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland.
J Clin Med. 2022 Apr 30;11(9):2526. doi: 10.3390/jcm11092526.
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.
矩阵大小的选择是磁共振成像（MRI）过程中的一个重要因素，并且对采集到的图像质量有重大影响。常用于评估MR图像质量的信噪比存在其局限性。因此，为此我们提出一种新颖的方法：使用纹理分析作为对矩阵大小变化敏感的图像质量指标。生物医学图像中的图像纹理代表通过诸如MRI等医学成像模态可视化的组织和器官结构。分析在使用不同矩阵大小采集的图像中可视化的相同组织所确定的纹理参数之间的相关性，以辅助评估最佳矩阵大小的选择。在保持相同视野的同时，使用五种不同的矩阵大小采集肩部的T2加权冠状图像；考虑了三个感兴趣区域（骨骼、脂肪和肌肉）。针对为每个矩阵评估的310元素纹理特征向量的所有可能对计算了林氏相关系数。结合在使用较小矩阵采集的图像中可见的图像噪声和模糊效应来讨论获得的结果。考虑到这些现象，提出了用于MRI成像的矩阵大小选择建议。
