An integrated approach for reconstructing a surface model of the proximal femur from sparse input data and a multi-resolution point distribution model: an in vitro study.
- Zheng G ARTORG Center for Biomedical Engineering Research, University of Bern, Stauffacherstrasse 78, 3014 Bern, Switzerland. guoyan.zheng@ieee.org
- Schumann S
- González Ballester MA
- 2009-12-25
Published in:
- International journal of computer assisted radiology and surgery. - 2010
Femur
Femur Head
Humans
Image Processing, Computer-Assisted
Models, Anatomic
Pattern Recognition, Automated
Radiographic Image Enhancement
Radiographic Image Interpretation, Computer-Assisted
Reproducibility of Results
Surgery, Computer-Assisted
English
BACKGROUND
Accurate reconstruction of a patient-specific surface model of the proximal femur from preoperatively or intraoperatively available sparse data plays an important role in planning and supporting various computer-assisted surgical procedures.
METHODS
In this paper, we present an integrated approach using a multi-resolution point distribution model (MR-PDM) to reconstruct a patient-specific surface model of the proximal femur from sparse input data, which may consist of sparse point data or a limited number of calibrated X-ray images. Depending on the modality of the input data, our approach chooses different PDMs. When 3D sparse points are used, which may be obtained intraoperatively via a pointer-based digitization or from a calibrated ultrasound, a fine level point distribution model (FL-PDM) is used in the reconstruction process. In contrast, when calibrated X-ray images are used, which may be obtained preoperatively or intraoperatively, a coarse level point distribution model (CL-PDM) will be used.
RESULTS
The present approach was verified on 31 femurs. Three different types of input data, i.e., sparse points, calibrated fluoroscopic images, and calibrated X-ray radiographs, were used in our experiments to reconstruct a surface model of the associated bone. Our experimental results demonstrate promising accuracy of the present approach.
CONCLUSIONS
A multi-resolution point distribution model facilitate the reconstruction of a patient-specific surface model of the proximal femur from sparse input data.
Accurate reconstruction of a patient-specific surface model of the proximal femur from preoperatively or intraoperatively available sparse data plays an important role in planning and supporting various computer-assisted surgical procedures.
METHODS
In this paper, we present an integrated approach using a multi-resolution point distribution model (MR-PDM) to reconstruct a patient-specific surface model of the proximal femur from sparse input data, which may consist of sparse point data or a limited number of calibrated X-ray images. Depending on the modality of the input data, our approach chooses different PDMs. When 3D sparse points are used, which may be obtained intraoperatively via a pointer-based digitization or from a calibrated ultrasound, a fine level point distribution model (FL-PDM) is used in the reconstruction process. In contrast, when calibrated X-ray images are used, which may be obtained preoperatively or intraoperatively, a coarse level point distribution model (CL-PDM) will be used.
RESULTS
The present approach was verified on 31 femurs. Three different types of input data, i.e., sparse points, calibrated fluoroscopic images, and calibrated X-ray radiographs, were used in our experiments to reconstruct a surface model of the associated bone. Our experimental results demonstrate promising accuracy of the present approach.
CONCLUSIONS
A multi-resolution point distribution model facilitate the reconstruction of a patient-specific surface model of the proximal femur from sparse input data.
- Language
-
- English
- Open access status
- green
- Identifiers
-
- DOI 10.1007/s11548-009-0386-y
- PMID 20033508
- Persistent URL
- https://folia.unifr.ch/global/documents/1256
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