A Method for Selecting Optimal MRI Sequence for Automatic Segmentation Based on Edge Analysis

Abstract

AbstractIn segmentation research using magnetic resonance imaging (MRI) images, the sequence is an important factor affecting segmentation performance. Therefore, a selection method is required to find the best-suited sequence according to a segmentation target. This study presents a method for finding the most suitable MRI sequence for automatic segmentation. Based on profile information of virtual rays, we devised metrics to compute the edge sharpness and contrast. The analysis was proceeded using three sequences (T1: T1-weighted, PD: proton density-weighted, and SPGR: fat-suppressed 3D spoiled gradient-echo) from five defined edges (EBB: between cancellous bone and cortical bone, EBC: between cortical bone and cartilage, ECF: between cartilage and fat, ECM: between cartilage and meniscus, EBT: between cortical bone and tissue). The edge characteristics were compared in the three sequences using the proposed metrics, and the inter-subject variability was evaluated as well. In the case of sharpness, T1 showed the highest at the EBB, ECF, and EBT(p < .05). SPGR was the highest at the EBC, and PD was the highest at the ECM(p < .005). For contrast, T1 was the highest at the EBBand EBT(p < .05). SPGR was the highest at the ECF(p < .005), and PD was the highest at the ECM(p < .005). PD and SPGR had similar contrast values at the EBC(PD ≈ SPGR > T1). It was confirmed that the edge properties of the structure depend on the type of adjacent materials. The presented method showed consistent results according to the edge, and it was confirmed that new metrics were suitable for finding the most suitable sequence for segmentation. The method and metrics we present quantitatively evaluate the edge characteristics, which will be a useful way for finding the most suitable MRI sequence for segmentation study.