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Award Abstract #1229628

MRI: Development of a Near-Real-Time High-Accuracy Musculoskeletal System Measurement and Analysis Instrument (SKELETALMI)

Division Of Computer and Network Systems
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Initial Amendment Date: September 20, 2012
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Latest Amendment Date: September 20, 2012
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Award Number: 1229628
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Award Instrument: Standard Grant
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Program Manager: Rita V. Rodriguez
CNS Division Of Computer and Network Systems
CSE Direct For Computer & Info Scie & Enginr
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Start Date: October 1, 2012
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End Date: September 30, 2016 (Estimated)
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Awarded Amount to Date: $1,111,040.00
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Investigator(s): Dimitris Metaxas dnm@cs.rutgers.edu (Principal Investigator)
Vladimir Pavlovic (Co-Principal Investigator)
Kang Li (Co-Principal Investigator)
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Sponsor: Rutgers University New Brunswick
33 Knightsbridge Road
Piscataway, NJ 08854-3925 (848)932-0150
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Smart and Connected Health,
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Program Reference Code(s): 1189, 8018
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Program Element Code(s): 1189, 1714, 8018, 1640, 7495


This project, developing an instrument for near-real-time high-accuracy musculoskeletal system analysis named SKELETALMI, aims to enable timely and accurate measurement, analysis, and characterization of in vivo combined joint movement, whole body kinematics, skeletal muscle activity, and body reaction forces. The instrument consist of hardware components that include devices for fluoroscopic X-ray image acquisition, for measuring whole body movement, for skeletal muscle activity, for measuring lower and upper body reaction forces, and a high performance computer system; as well as software to analyze and characterize jointly in vivo joint kinematics, joint kinetics, and muscle activation.

SKELETALMI is expected to

- Allow real-time calibration through overlay between fluoroscopic images and optical images;

- Automatically reconstruct 3D bone models from CT and MRI scans in real-time;

- Automatically estimate 3D in vivo bone movement through 2D/3D registration;

- Automatically establish 3D bone coordinated systems and convert the 3D in vivo movement into 3D joint kinematics;

- Fuse the accurate joint kinematics, whole body kinematics, muscle activation, and body reaction forces and visualize all the information on digital human models.

This development has a strong multidisciplinary component that involves algorithms, biomechanics, biomedical imaging, HCI, and computer graphics. The project constitutes a joint collaborative effort of Rutgers with the State University of NJ and Kessler Foundation Research Center.

Broader Impacts:

The instrumentation impacts many application domains such as shoe design, athletic training, injury prevention, aging, design of movement-related medical device, and surgical/rehabilitation technique innovation. Underpinned by the capabilities of the instrument and the new related data collected, new courses will be developed in musculoskeletal biomechanics, graphics simulation, movement analysis, and biomedical image analysis. Consequently, the instrument also influences the educational programs at the institution that should generate graduates with a comprehensive knowledge of computational sciences, medical science, and product design.


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Yang Yu, Shaoting Zhang, Wei Liu, Kang Li, Dimitris Metaxas, Leon Axel. "Deformable Models with Sparse Regularization and Their Application to Cardiac Motion Analysis," Medical Image Analysis, 2014.

Arjun Jain, Chao Chen, Thorsten Thormählen, Dimitris Metaxas, Hans-Peter Seidel. "Multi-layer stencil creation from images," Computer & Graphics (C&G), v.48, 2015. 

Chao Chen, Han Liu, Dimitris Metaxas, Tianqi Zhao. "Mode Estimation for High Dimensional Discrete Tree Graphical Models," Advances in Neural Information Processing Systems (NIPS), 2014.

Chaowei Tan, Zhennan Yan, Kang Li, Dimitris Metaxas, Shaoting Zhang. "Laplacian Shape Editing with Local Patch Based Force Field for Interactive Segmentation," MICCAI workshop Patch-MI, 2015.

Soheil Ghafurian, Balazs Galdi, Virak Tan,Kang Li. "Computerized 3D morphological analysis of glenoid orientation," Journal of Orthopaedic Research, 2015. 

Zhennan Yan, Shaoting Zhang, Chaowei Tan, Hongxing Qin, Boubakeur Belaroussi, Hui Jing Yu, Colin Miller, Dimitris Metaxas:. "Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials," Computerized Medical Imaging and Graphics, 2014. 

Zhennan Yan, Yiqiang Zhan, Zhigang Peng, Shu Liao, Yoshihisa Shinagawa, Dimitris N. Metaxas, Xiang Sean Zhou. "Bodypart Recognition Using Multi-stage Deep Learning," IPMI, 2015.


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