title: Multi-contrast MR for enhanced bone imaging and segmentation.
abstract: Musculoskeletal applications of MRI are increasing rapidly but a major challenge for researchers is the ability to efficiently and accurately segment structures of interest, such as bone, which is typically required to perform further quantitative analyses. Manual tracing is extremely time consuming and introduces problematic user variability. Automated segmentation is usually preferred; however, the accuracy and robustness of current methods still suffer from significant limitations. In this paper, we propose a novel approach for simplifying such segmentation tasks by optimizing MR protocols specifically for bone data acquisition. We present multi-contrast MR bone data acquired using short-TR T1W and fat suppression scans and demonstrate how this data can be used within an automated segmentation framework in order to improve accuracy of bone segmentation. Validation was performed on knee joint data with quantitative segmentation results on our multi-contrast data showing superior performance compared to results obtained using conventional single-contrast data. Improvements in contrast to noise ratio of 39.24 and in sensitivity and specificity of 4.09% and 4.17%, respectively, for the tibia, and 4.4% and 5.74% for the femur, were achieved.
authors.affiliation: Department of Electrical & Computer Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
authors.forename: Rupin
authors.initials: R
authors.lastname: Dalvi
authors.forename: Rafeef
authors.initials: R
authors.lastname: Abugharbieh
authors.forename: Derekc
authors.initials: D
authors.lastname: Wilson
authors.forename: David R
authors.initials: DR
authors.lastname: Wilson
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001842:Bone and Bones / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity