Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging

被引:112
作者
Barratt, Dean C. [1 ,2 ]
Chan, Carolyn S. K. [1 ,2 ]
Edwards, Philip J. [2 ,3 ]
Penney, Graeme P. [1 ,2 ]
Slomczykowski, Mike [4 ]
Carter, Timothy J. [1 ,2 ]
Hawkes, David J. [1 ,2 ]
机构
[1] UCL, Ctr Med Image Comp, Dept Med Phys & Bioengn, London WC1E 6BT, England
[2] Kings Coll London, Guys Kings & St Thomas Sch Med, Guys Hosp, Dept Imaging Sci, London SE1 9RT, England
[3] Univ London Imperial Coll Sci Technol & Med, St Marys Hosp, Dept Biosurg & Surg Technol, London W2 1NY, England
[4] Johnson & Johnson Co, DePuy Int Ltd, iOrthopaed, Leeds LS11 0EA, W Yorkshire, England
基金
英国工程与自然科学研究理事会;
关键词
image registration; orthopaedics; statistical shape model; ultrasound;
D O I
10.1016/j.media.2007.12.006
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Statistical shape modelling potentially provides a powerful tool for generating patient-specific, 3D representations of bony anatomy for computer-aided orthopaedic surgery (CAOS) without the need for a preoperative CT scan. Furthermore, freehand 3D ultrasound (US) provides a non-invasive method for digitising bone surfaces in the operating theatre that enables a much greater region to be sampled compared with conventional direct-contact (i.e., pointer-based) digitisation techniques. In this paper, we describe how these approaches can be combined to simultaneously generate and register a patient-specific model of the femur and pelvis to the patient during surgery. In our implementation, a statistical deformation model (SDM) was constructed for the femur and pelvis by performing a principal component analysis on the B-spline control points that parameterise the freeform deformations required to non-rigidly register a training set of CT scans to a carefully segmented template CT scan. The segmented template bone surface, represented by a triangulated surface mesh, is instantiated and registered to a cloud of US-derived surface points using an iterative scheme in which the weights corresponding to the first five principal modes of variation of the SDM are optimised in addition to the rigid-body parameters. The accuracy of the method was evaluated using clinically realistic data obtained on three intact human cadavers (three whole pelves and six femurs). For each bone, a high-resolution CT scan and rigid-body registration transformation, calculated using bone-implanted fiducial markers, served as the gold standard bone geometry and registration transformation, respectively. After aligning the final instantiated model and CT-derived surfaces using the iterative closest point (ICP) algorithm, the average root-mean-square distance between the surfaces was 3.5 mm over the whole bone and 3.7 mm in the region of surgical interest. The corresponding distances after aligning the surfaces using the marker-based registration transformation were 4.6 and 4.5 mm, respectively. We conclude that despite limitations on the regions of bone accessible using US imaging, this technique has potential as a cost-effective and non-invasive method to enable surgical navigation during CAOS procedures, without the additional radiation dose associated with performing a preoperative CT scan or intraoperative fluoroscopic imaging. However, further development is required to investigate errors using error measures relevant to specific surgical procedures. (C) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码:358 / 374
页数:17
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