Haydar Talib

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 surgeons 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.

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.

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.

A method for frame-by-frame us to CT registration in a joint calibration and registration framework

A method is presented for achieving robust joint calibration and registration in ultrasound (US) to CT registration for computer assisted orthopedic surgery. We propose using an effectively real-time frame- by-frame registration algorithm during US image acquisition. This approach provides more control to the surgeon, is more robust to initial conditions, and is computationally efficient. We then use the estimated registration of the frame-by-frame method to initialize a joint calibration and registration algorithm, and this is shown to produce over all more accurate and repeatable results. Experiments are performed using simulated US images of the lumbar vertebra and the distal femur as potential areas of interest for surgical applications.

CompAS: A new approach to commonality and variability analysis with applications in computer assisted orthopaedic surgery

In rapidly evolving domains such as Computer Assisted Orthopaedic Surgery (CAOS) emphasis is often put first on innovation and new functionality, rather than in developing the common infrastructure needed to support integration and reuse of these innovations. In fact, developing such an infrastructure is often considered to be a high-risk venture given the volatility of such a domain. We present CompAS, a method that exploits the very evolution of innovations in the domain to carry out the necessary quantitative and qualitative commonality and variability analysis, especially in the case of scarce system documentation. We show how our technique applies to the CAOS domain by using conference proceedings as a key source of information about the evolution of features in CAOS systems over a period of several years. We detect and classify evolution patterns to determine functional commonality and variability. We also identify non-functional requirements to help capture domain variability. We have validated our approach by evaluating the degree to which representative test systems can be covered by the common and variable features produced by our analysis.

DYNAMIC REGISTRATION USING ULTRASOUND FOR ANATOMICAL REFERENCING

This paper proposes methods to circumvent the need to attach physical markers to bones for anatomical referencing in computer-assisted orthopedic surgery. Using ultrasound, a bone could be non-invasively referenced, and so the problem is formulated as the need for dynamic registration. A method for correspondence establishment is presented, and the matching step is based on three least-squares algorithms: two that are typically used in registration methods such as ICP, and the third is a form of the Unscented Kalman filter that was adapted to work in this context. A simulation was developed in order to reliably evaluate and compare the dynamic registration methods

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 intra-operative 3D visualization without pre or intra-operative 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

INTRA-OPERATIVE FLUOROSCOPY AND ULTRASOUND FOR COMPUTER ASSISTED SURGERY

This documents presents the highlights and challenges of the ongoing research in the M.E.Muller Research Center for Orthopaedic Surgery in regard to the use of intra-operative fluoroscopic and ultrasound imaging for computer assisted surgery. Several applications mainly in the field of minimal or less invasive surgery profiting from the developed methods are presented. The virtual 2D fluoroscopy methods reduces X-ray exposure to patient and surgeon. The emerging 3D fluoroscopy technology(IsoC3D) along with novel segmentation methods allows for efficient intra-operative planning and enhanced surgical eye-navigation. Registration methods are developed to allow for an exact execution of pre-operative CT based planning.Also, Ultrasound (US) is a favorite imaging modality for minimal invasive surgery. We developed an automatic, efficient bone segmentation algorithm for 2D US. The contours are used for surface-to-point registration and provide in combination with a novel bone-morphing method an accurate estimation of the 3D patient anatomy. Efficient pre-, intraand postoperative assessment of the leg geometry can also be performed.

Assessing the feasibility of ultrasound-initialized deformable bone models

This paper presents a feasibility and evaluation study for using 2D ultrasound in conjunction with our statistical deformable bone model in the scope of computer-assisted surgery (CAS). The final aim is to provide the surgeon with an enhanced 3D visualization for surgical navigation in orthopaedic 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 from a sparse set of surface points, and ultrasound segmentation and calibration to provide the intended rapid and accurate visualization. We compared the use of a tracked digitizing pointer to ultrasound to acquire landmarks and bone surface points for the estimation of two cast proximal femurs, where two users performed the experiments 5-6 times per scenario. The concept of CT-based error introduced in the paper is used to give an approximate quantitative value to the best hoped-for prediction error, or lower-bound error, for a given anatomy. The conclusions of this work were that the pointer-based approach produced good results, and although the ultrasound-based approach performed considerably worse on average, there were several cases where the results were comparable to the pointer-based approach. It was determined that the primary factor for poor ultrasound performance was the inaccurate localization of the three initial landmarks, which are used for the statistical shape model.

Efficient segmentation of 3D fluoroscopic datasets from mobile C-arm

The emerging mobile fluoroscopic 3D technology linked with a navigation system combines the advantages of CT-based and C-arm-based navigation. The intra-operative, automatic segmentation of 3D fluoroscopy datasets enables the combined visualization of surgical instruments and anatomical structures for enhanced planning, surgical eye-navigation and landmark digitization. We performed a thorough evaluation of several segmentation algorithms using a large set of data from different anatomical regions and man-made phantom objects. The analyzed segmentation methods include automatic thresholding, morphological operations, an adapted region growing method and an implicit 3D geodesic snake method. In regard to computational efficiency, all methods performed within acceptable limits on a standard Desktop PC (30sec-5min). In general, the best results were obtained with datasets from long bones, followed by extremities. The segmentations of spine, pelvis and shoulder datasets were generally of poorer quality. As expected, the threshold-based methods produced the worst results. The combined thresholding and morphological operations methods were considered appropriate for a smaller set of clean images. The region growing method performed generally much better in regard to computational efficiency and segmentation correctness, especially for datasets of joints, and lumbar and cervical spine regions. The less efficient implicit snake method was able to additionally remove wrongly segmented skin tissue regions. This study presents a step towards efficient intra-operative segmentation of 3D fluoroscopy datasets, but there is room for improvement. Next, we plan to study model-based approaches for datasets from the knee and hip joint region, which would be thenceforth applied to all anatomical regions in our continuing development of an ideal segmentation procedure for 3D fluoroscopic images.