Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

An integrated approach for reconstructing surface models of the proximal femur from sparse input data for surgical navigation

A patient-specific surface model of the proximal femur plays an important role in planning and supporting various computer-assisted surgical procedures including total hip replacement, hip resurfacing, and osteotomy of the proximal femur. The common approach to derive 3D models of the proximal femur is to use imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). However, the high logistic effort, the extra radiation (CT-imaging), and the large quantity of data to be acquired and processed make them less functional. In this paper, we present an integrated approach using a multi-level point distribution model (ML-PDM) to reconstruct a patient-specific model of the proximal femur from intra-operatively available sparse data. Results of experiments performed on dry cadaveric bones using dozens of 3D points are presented, as well as experiments using a limited number of 2D X-ray images, which demonstrate promising accuracy of the present approach.

Computed tomography-based surgical navigation for hip arthroplasty

Component malpositioning and postoperative leg length discrepancy are the most common technical problems associated with total hip arthroplasty (THA). Surgical navigation offers the potential to reduce the incidence of these problems. We reviewed 317 patients (344 hips) that underwent THA using computed tomography-based surgical navigation, including 112 THAs using a simplified method of measuring leg length. Guided by the navigation system, cups were placed in 40.8 degrees +/- 2 degrees of operative abduction (range, 35 degrees -50 degrees) and 30.8 degrees +/- 3.2 degrees (range, 19 degrees -43 degrees) of operative anteversion. We subsequently measured radiographic abduction on plain anteroposterior pelvic radiographs and calculated abduction and anteversion. Radiographically, 97.1 % of the cups were in the safe zone for abduction and 92.4% for anteversion. The mean incision length was less than 8 cm for 327 of the 344 hips. Leg length change measured intraoperatively was 6.6 +/- 4.1 mm (range, -2-22), similar to measurements from the pre- and postoperative magnification-corrected radiographs. Computer assistance during THA increased the consistency of component positioning and allowed reliable measurement of leg length change during surgery.

CT data-based navigation for post-mortem biopsy-a feasibility study

INTRODUCTION: Recent advances in medical imaging have brought post-mortem minimally invasive computed tomography (CT) guided percutaneous biopsy to public attention. AIMS: The goal of the following study was to facilitate and automate post-mortem biopsy, to suppress radiation exposure to the investigator, as may occur when tissue sampling under computer tomographic guidance, and to minimize the number of needle insertion attempts for each target for a single puncture. METHODS AND MATERIALS: Clinically approved and post-mortem tested ACN-III biopsy core needles (14 gauge x 160 mm) with an automatic pistol device (Bard Magnum, Medical Device Technologies, Denmark) were used for probe sampling. The needles were navigated in gelatine/peas phantom, ex vivo porcine model and subsequently in two human bodies using a navigation system (MEM centre/ISTB Medical Application Framework, Marvin, Bern, Switzerland) with guidance frame and a CT (Emotion 6, Siemens, Germany). RESULTS: Biopsy of all peas could be performed within a single attempt. The average distance between the inserted needle tip and the pea centre was 1.4mm (n=10; SD 0.065 mm; range 0-2.3 mm). The targets in the porcine liver were also accurately punctured. The average of the distance between the needle tip and the target was 0.5 mm (range 0-1 mm). Biopsies of brain, heart, lung, liver, pancreas, spleen, and kidney were performed on human corpses. For each target the biopsy needle was only inserted once. The examination of one body with sampling of tissue probes at the above-mentioned locations took approximately 45 min. CONCLUSIONS: Post-mortem navigated biopsy can reliably provide tissue samples from different body locations. Since the continuous update of positional data of the body and the biopsy needle is performed using optical tracking, no control CT images verifying the positional data are necessary and no radiation exposure to the investigator need be taken into account. Furthermore, the number of needle insertions for each target can be minimized to a single one with the ex vivo proven adequate accuracy and, in contrast to conventional CT guided biopsy, the insertion angle may be oblique. Navigation for minimally invasive tissue sampling is a useful addition to post-mortem CT guided biopsy.