M Brehler1, G Thawait2, W Shyr1, J Ramsay3, J H Siewerdsen1,2, W Zbijewski1. 1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA. 2. Department of Radiology, Johns Hopkins University, Baltimore, MD USA. 3. Natick Soldier Research, Development and Engineering Center (NSRDEC), Natick, MA USA.
Abstract
PURPOSE: Anatomical metrics of the tibiofemoral joint support assessment of joint stability and surgical planning. We propose an automated, atlas-based algorithm to streamline the measurements in 3D images of the joint and reduce user-dependence of the metrics arising from manual identification of the anatomical landmarks. METHODS: The method is initialized with coarse registrations of a set of atlas images to the fixed input image. The initial registrations are then refined separately for the tibia and femur and the best matching atlas is selected. Finally, the anatomical landmarks of the best matching atlas are transformed onto the input image by deforming a surface model of the atlas to fit the shape of the tibial plateau in the input image (a mesh-to-volume registration). We apply the method to weight-bearing volumetric images of the knee obtained from 23 subjects using an extremity cone-beam CT system. Results of the automated algorithm were compared to an expert radiologist for measurements of Static Alignment (SA), Medial Tibial Slope (MTS) and Lateral Tibial Slope (LTS). RESULTS: Intra-reader variability as high as ~10% for LTS and 7% for MTS (ratio of standard deviation to the mean in repeated measurements) was found for expert radiologist, illustrating the potential benefits of an automated approach in improving the precision of the metrics. The proposed method achieved excellent registration of the atlas mesh to the input volumes. The resulting automated measurements yielded high correlations with expert radiologist, as indicated by correlation coefficients of 0.72 for MTS, 0.8 for LTS, and 0.89 for SA. CONCLUSIONS: The automated method for measurement of anatomical metrics of the tibiofemoral joint achieves high correlation with expert radiologist without the need for time consuming and error prone manual selection of landmarks.
PURPOSE: Anatomical metrics of the tibiofemoral joint support assessment of joint stability and surgical planning. We propose an automated, atlas-based algorithm to streamline the measurements in 3D images of the joint and reduce user-dependence of the metrics arising from manual identification of the anatomical landmarks. METHODS: The method is initialized with coarse registrations of a set of atlas images to the fixed input image. The initial registrations are then refined separately for the tibia and femur and the best matching atlas is selected. Finally, the anatomical landmarks of the best matching atlas are transformed onto the input image by deforming a surface model of the atlas to fit the shape of the tibial plateau in the input image (a mesh-to-volume registration). We apply the method to weight-bearing volumetric images of the knee obtained from 23 subjects using an extremity cone-beam CT system. Results of the automated algorithm were compared to an expert radiologist for measurements of Static Alignment (SA), Medial Tibial Slope (MTS) and Lateral Tibial Slope (LTS). RESULTS: Intra-reader variability as high as ~10% for LTS and 7% for MTS (ratio of standard deviation to the mean in repeated measurements) was found for expert radiologist, illustrating the potential benefits of an automated approach in improving the precision of the metrics. The proposed method achieved excellent registration of the atlas mesh to the input volumes. The resulting automated measurements yielded high correlations with expert radiologist, as indicated by correlation coefficients of 0.72 for MTS, 0.8 for LTS, and 0.89 for SA. CONCLUSIONS: The automated method for measurement of anatomical metrics of the tibiofemoral joint achieves high correlation with expert radiologist without the need for time consuming and error prone manual selection of landmarks.
Authors: Javad Hashemi; Naveen Chandrashekar; Brian Gill; Bruce D Beynnon; James R Slauterbeck; Robert C Schutt; Hossein Mansouri; Eugene Dabezies Journal: J Bone Joint Surg Am Date: 2008-12 Impact factor: 5.284
Authors: Stefan Klein; Marius Staring; Keelin Murphy; Max A Viergever; Josien P W Pluim Journal: IEEE Trans Med Imaging Date: 2009-11-17 Impact factor: 10.048
Authors: John A Carrino; Abdullah Al Muhit; Wojciech Zbijewski; Gaurav K Thawait; J Webster Stayman; Nathan Packard; Robert Senn; Dong Yang; David H Foos; John Yorkston; Jeffrey H Siewerdsen Journal: Radiology Date: 2013-11-18 Impact factor: 11.105
Authors: Mark L Brandon; Paul T Haynes; Joel R Bonamo; MaryIrene I Flynn; Gene R Barrett; Mark F Sherman Journal: Arthroscopy Date: 2006-08 Impact factor: 4.772
Authors: Javad Hashemi; Naveen Chandrashekar; Hossein Mansouri; Brian Gill; James R Slauterbeck; Robert C Schutt; Eugene Dabezies; Bruce D Beynnon Journal: Am J Sports Med Date: 2009-10-21 Impact factor: 6.202
Authors: Michael Brehler; Gaurav Thawait; Jonathan Kaplan; John Ramsay; Miho J Tanaka; Shadpour Demehri; Jeffrey H Siewerdsen; Wojciech Zbijewski Journal: J Med Imaging (Bellingham) Date: 2019-06-19
Authors: M Brehler; A Islam; L Vogelsang; D Yang; W Sehnert; D Shakoor; S Demehri; J H Siewerdsen; W Zbijewski Journal: Proc SPIE Int Soc Opt Eng Date: 2019-03-15