Automatic registration of pre- and intraoperative data for long bones in Minimally Invasive Surgery

Automatic registration of pre- and intraoperative data for long bones in Minimally Invasive Surgery

The Minimally Invasive Procedures (MIP) in orthopedics have grown rapidly worldwide, as clinical results indicate that patients who undergo MIP typically experience minimized blood loss, smaller incision and shorter hospital stays. For most MIP, a preoperative 3D model of the patient anatomy is usua...

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Bibliographic Details
Published in:2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2014; pp. 5575 - 5578
Main Authors: Fakhfakh, H. E., Llort-Pujol, G., Hamitouche, C., Stindel, E.
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01-01-2014
Subjects:
Algorithms
Automation
Bone and Bones - diagnostic imaging
Bone and Bones - surgery
Bones
Computed tomography
Humans
Image Processing, Computer-Assisted - methods
Image reconstruction
Intraoperative Care
Minimally Invasive Surgical Procedures - methods
Phantoms
Phantoms, Imaging
Preoperative Care
Radius - diagnostic imaging
Radius - surgery
Reproducibility of Results
Surface reconstruction
Surgery
Three-dimensional displays
Ultrasonography
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Summary:The Minimally Invasive Procedures (MIP) in orthopedics have grown rapidly worldwide, as clinical results indicate that patients who undergo MIP typically experience minimized blood loss, smaller incision and shorter hospital stays. For most MIP, a preoperative 3D model of the patient anatomy is usually generated in order to plan the surgery. The challenge in MIP consists in finding the correspondence between the preoperative model and the actual position of the patient in the operating room, also known as image-to-patient registration. This paper proposes a real-time solution based on ultrasound (US) images: the patient anatomy is scanned by an US probe. Then, the segmentation and the extraction of bone contours from US images result in a 3D point cloud. The Poisson surface reconstruction method provides a 3D surface from 2D US data which will be registered with the preoperative model (CT volume) using the principal axes of inertia and the Iterative Closest Point robust (ICPr) algorithm. We present quantitative and qualitative results on both phantom and clinical data and show a mean registration accuracy of 0.66 mm for clinical radius scan. The promising registration results show the possible use of the proposed registration algorithm in clinical procedures.
ISSN:1094-687X
1557-170X
1558-4615
DOI:10.1109/EMBC.2014.6944890