ArticlePloS one2024
A method for comparing MRI sequences of the knee for segmentation based on morphological features.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundIn magnetic resonance imaging (MRI) segmentation research, the choice of sequence influences the segmentation accuracy. This study introduces a method to compare sequences. By aligning sequences with specific segmentation objectives, we provide an example of a comparative analysis of various sequences for knee images.
methodsBased on the profile information of virtual rays, we devised metrics to compute the edge sharpness and contrast. Edge analysis was performed in five edges (EBB: between cancellous 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). Subsequently, profiles were extracted from the virtual ray that traversed the defined edge. Finally, edge characteristics were compared in each sequence using the computed metrics.
resultsIn the case of sharpness, T1-weighted (T1) showed the highest at EBB, ECF, and EBT (all, p < .05). The fat-suppressed 3D spoiled gradient-echo (SPGR) was the highest at EBC, and proton density fat-saturated (PDFS) was the highest at ECM (all, p < .005). Depending on each sequence, the knee structures showed different edge characteristics. Also, it was confirmed that the edge properties of the structure depend on the adjacent materials.
conclusionsThe ultimate goal of this study is to present a methodology for selecting the most appropriate MRI sequence for segmentation, which can be applied to images of other parts in addition to the knee images used in the study. The method we present quantitatively evaluates the edge characteristics, and experimental results show that our method shows consistent results according to the edge. Our method will provide additional information for MRI sequence selection for segmentation.
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