Information filtering for ultrasound-based real-time registration.

This paper presents methods based on information filters for solving matching problems with emphasis on real time, or effectively real-time applications. Both applications discussed in this paper deal with ultrasound-based rigid registration in computer-assisted orthopedic surgery. In the first application, the usual workflow of rigid registration is reformulated such that registration algorithms would iterate while the surgeon is acquiring ultrasound images of the anatomy to be operated. Using this effectively real-time approach to registration, the surgeon would then receive feedback in order to better gauge the quality of the final registration outcome. The second application considered in this paper circumvents the need to attach physical markers to bones for anatomical referencing. Experiments using anatomical objects immersed in water are performed in order to evaluate and compare the different methods presented herein, using both 2-D as well as real-time 3-D ultrasound.

Automated bone contour detection in ultrasound B-mode images for minimally invasive registration in computer-assisted surgery-an in vitro evaluation.

BACKGROUND: Minimally invasive surgical interventions performed using computer-assisted surgery (CAS) systems require reliable registration methods for pre-operatively acquired patient anatomy representations that are compatible with the minimally invasive paradigm. The use of brightness-mode ultrasound seems to be promising, if associated devices work in a computationally efficient and fully automatic manner. METHODS: This paper presents a rapid and fully automatic segmentation approach for ultrasound B-mode images capable of detecting echoes from bony structures. The algorithm focuses on the precise and rapid detection of bone contours usable for minimally invasive registration. The article introduces the image-processing scheme and a set-up enabling a direct comparison between manually digitized reference points and the segmented bone contours. The segmentation accuracy was assessed using cadaveric material. RESULTS: The experimental evaluation revealed results in the same order of magnitude as a pointer-based surface digitization procedure. CONCLUSION: The suggested segmentation approach provides a reliable means of detecting bony surface patches in ultrasound images.

Statistical deformable bone models for robust 3D surface extrapolation from sparse data.

A majority of pre-operative planning and navigational guidance during computer assisted orthopaedic surgery routinely uses three-dimensional models of patient anatomy. These models enhance the surgeon's capability to decrease the invasiveness of surgical procedures and increase their accuracy and safety. A common approach for this is to use computed tomography (CT) or magnetic resonance imaging (MRI). These have the disadvantages that they are expensive and/or induce radiation to the patient. In this paper we propose a novel method to construct a patient-specific three-dimensional model that provides an appropriate intra-operative visualization without the need for a pre or intra-operative imaging. The 3D model is reconstructed by fitting a statistical deformable model to minimal sparse 3D data consisting of digitized landmarks and surface points that are obtained intra-operatively. The statistical model is constructed using Principal Component Analysis from training objects. Our deformation scheme efficiently and accurately computes a Mahalanobis distance weighted least square fit of the deformable model to the 3D data. Relaxing the Mahalanobis distance term as additional points are incorporated enables our method to handle small and large sets of digitized points efficiently. Formalizing the problem as a linear equation system helps us to provide real-time updates to the surgeons. Incorporation of M-estimator based weighting of the digitized points enables us to effectively reject outliers and compute stable models. We present here our evaluation results using leave-one-out experiments and extended validation of our method on nine dry cadaver bones.

Evaluation and initial validation studies of anatomical structure morphing.

Anatomical Structure Morphing is the process of estimating the patient-specific 3D shape of a given anatomy from a few digitized surface points. This provides an appropriate riate intra-operative 3D visualization without pre oroperativeintra- perative imaging. Our method fits a statistical deformable model to the digitized landmarks and bone surface points which are usually sparse. The statistical deformable model is constructed using Principal Component Analysis (PCA) from an appropriate training set of objects. Our proposed technique extrapolates the 3D shape by computing a Mahalanobis distance weighted least-squares fit of this model to the minimal sparse 3D data. In this paper we present evaluation and initial validation studies of our morphing technique on 9 dry cadaver femur bones. The influence of size of the initial training set on the morphing performance is also evaluated by repeating our experiments on two different training sets of varying sizes.

A comparison study assessing the feasibility of ultrasound-initialized deformable bone models.

This article presents a feasibility and evaluation study for using 2D ultrasound in conjunction with our statistical deformable bone model within the scope of computer-assisted surgery. The final aim is to provide the surgeon with enhanced 3D visualization for surgical navigation in orthopedic surgery without the need for preoperative CT or MRI scans. We unified our earlier work to combine several automatic methods for statistical bone shape prediction and ultrasound segmentation and calibration to provide the intended rapid and accurate visualization. We compared the use of a tracked digitizing pointer and ultrasound for acquiring landmarks and bone surface points for the estimation of two cast proximal femurs.