C Siversson1, A Akhondi-Asl2, S Bixby3, Y-J Kim4, S K Warfield5. 1. Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Medical Radiation Physics, Lund University, Malmö, Sweden. Electronic address: carl.siversson@childrens.harvard.edu. 2. Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: alireza.akhondi-asl@childrens.harvard.edu. 3. Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: sarah.bixby@childrens.harvard.edu. 4. Department of Orthopaedic Surgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: young-jo.kim@childrens.harvard.edu. 5. Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: simon.warfield@childrens.harvard.edu.
Abstract
OBJECTIVE: The quantitative interpretation of hip cartilage magnetic resonance imaging (MRI) has been limited by the difficulty of identifying and delineating the cartilage in a three-dimensional (3D) dataset, thereby reducing its routine usage. In this paper a solution is suggested by unfolding the cartilage to planar two-dimensional (2D) maps on which both morphology and biochemical degeneration patterns can be investigated across the entire hip joint. DESIGN: Morphological TrueFISP and biochemical delayed gadolinium enhanced MRI of cartilage (dGEMRIC) hip images were acquired isotropically for 15 symptomatic subjects with mild or no radiographic osteoarthritis (OA). A multi-template based label fusion technique was used to automatically segment the cartilage tissue, followed by a geometric projection algorithm to generate the planar maps. The segmentation performance was investigated through a leave-one-out study, for two different fusion methods and as a function of the number of utilized templates. RESULTS: For each of the generated planar maps, various patterns could be seen, indicating areas of healthy and degenerated cartilage. Dice coefficients for cartilage segmentation varied from 0.76 with four templates to 0.82 with 14 templates. Regional analysis suggests even higher segmentation performance in the superior half of the cartilage. CONCLUSIONS: The proposed technique is the first of its kind to provide planar maps that enable straightforward quantitative assessment of hip cartilage morphology and dGEMRIC values. This technique may have important clinical applications for patient selection for hip preservation surgery, as well as for epidemiological studies of cartilage degeneration patterns. It is also shown that 10-15 templates are sufficient for accurate segmentation in this application.
OBJECTIVE: The quantitative interpretation of hip cartilage magnetic resonance imaging (MRI) has been limited by the difficulty of identifying and delineating the cartilage in a three-dimensional (3D) dataset, thereby reducing its routine usage. In this paper a solution is suggested by unfolding the cartilage to planar two-dimensional (2D) maps on which both morphology and biochemical degeneration patterns can be investigated across the entire hip joint. DESIGN: Morphological TrueFISP and biochemical delayed gadolinium enhanced MRI of cartilage (dGEMRIC) hip images were acquired isotropically for 15 symptomatic subjects with mild or no radiographic osteoarthritis (OA). A multi-template based label fusion technique was used to automatically segment the cartilage tissue, followed by a geometric projection algorithm to generate the planar maps. The segmentation performance was investigated through a leave-one-out study, for two different fusion methods and as a function of the number of utilized templates. RESULTS: For each of the generated planar maps, various patterns could be seen, indicating areas of healthy and degenerated cartilage. Dice coefficients for cartilage segmentation varied from 0.76 with four templates to 0.82 with 14 templates. Regional analysis suggests even higher segmentation performance in the superior half of the cartilage. CONCLUSIONS: The proposed technique is the first of its kind to provide planar maps that enable straightforward quantitative assessment of hip cartilage morphology and dGEMRIC values. This technique may have important clinical applications for patient selection for hip preservation surgery, as well as for epidemiological studies of cartilage degeneration patterns. It is also shown that 10-15 templates are sufficient for accurate segmentation in this application.
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