In this paper we present a new approach for a personalized X-ray reconstruction of the proximal femur via a non-rigid registration of a 3D volumetric template to 2D calibrated C-arm images. The 2D-3D registration is done with a hierarchical two-stage strategy: the global scaled rigid registration stage followed by a regularized deformable b-spline registration stage. In both stages, a set of control points with uniform spacing are placed over the domain of the 3D volumetric template and the registrations are driven by computing updated positions of these control points, which then allows to accurately register the 3D volumetric template to the reference space of the C-arm images. Comprehensive experiments on simulated images, on images of cadaveric femurs and on clinical datasets are designed and conducted to evaluate the performance of the proposed approach. Quantitative and qualitative evaluation results are given, which demonstrate the efficacy of the present approach.
Surgical navigation systems visualize the positions and orientations of surgical
instruments and implants as graphical overlays onto a medical image of the operated
anatomy on a computer monitor. The orthopaedic surgical navigation systems could be
categorized according to the image modalities that are used for the visualization of
surgical action. In the so-called CT-based systems or 'surgeon-defined anatomy' based
systems, where a 3D volume or surface representation of the operated anatomy could be
constructed from the preoperatively acquired tomographic data or through
intraoperatively digitized anatomy landmarks, a photorealistic rendering of the surgical
action has been identified to greatly improve usability of these navigation systems.
However, this may not hold true when the virtual representation of surgical instruments
and implants is superimposed onto 2D projection images in a fluoroscopy-based
navigation system due to the so-called image occlusion problem. Image occlusion occurs
when the field of view of the fluoroscopic image is occupied by the virtual representation
of surgical implants or instruments. In these situations, the surgeon may miss part of the
image details, even if transparency and/or wire-frame rendering is used. In this paper, we
propose to use non-photorealistic rendering to overcome this difficulty. Laboratory
testing results on foamed plastic bones during various computer-assisted fluoroscopybased
surgical procedures including total hip arthroplasty and long bone fracture reduction and osteosynthesis are shown.
Constructing an accurate patient-specific 3D bone model from sparse point sets is a
challenging task. A priori information is often required to handle this otherwise ill-posed
problem. Previously we have proposed an optimal approach for anatomical shape
reconstruction from sparse information, which uses a dense surface point distribution
model (DS-PDM) as the a priori information and formulates the surface reconstruction
problem as a sequential three-stage optimal estimation process including (1) affine
registration; (2) statistical morphing; and (3) kernel-based deformation. Mathematically,
it is formulated by applying least-squares method to estimate the unknown parameters of
linear regression models (the first two stages) and nonlinear regression model (the last
stage). However, it is well-known that the least-squares method is very sensitive to
outliers. In this paper, we propose an important enhancement that enables to realize stable
reconstruction and robustly reject outliers. This is achieved by consistently employing
least trimmed squares approach in all three stages of the reconstruction to robustly
estimate unknown parameters of each regression model. Results of testing the new
approach on a simulated data are shown.
This paper describes a method for DRR generation as well as for volume gradients
projection using hardware accelerated 2D texture mapping and accumulation buffering
and demonstrates its application in 2D-3D registration of X-ray fluoroscopy to CT
images. The robustness of the present registration scheme are guaranteed by taking
advantage of a coarse-to-fine processing of the volume/image pyramids based on cubic
B-splines. A human cadaveric spine specimen together with its ground truth was used to
compare the present scheme with a purely software-based scheme in three aspects:
accuracy, speed, and capture ranges. Our experiments revealed an equivalent accuracy
and capture ranges but with much shorter registration time with the present scheme. More
specifically, the results showed 0.8 mm average target registration error, 55 second
average execution time per registration, and 10 mm and 10° capture ranges for the present
scheme when tested on a 3.0 GHz Pentium 4 computer.
Automated identification, pose and size estimation of cylindrical fragments from registered C-arm images is highly desirable in various computer-assisted, fluoroscopy-based applications including long bone fracture reduction and intramedullary nailing, where the pose and size of bone fragment need to be accurately estimated for a better treatment. In this paper, a RANSAC-based EM algorithm for robust detection and segmentation of cylindrical fragments from calibrated C-arm images is presented. By detection, we mean that the axes and the radii of the principal fragments will be automatically determined. And by segmentation, we mean that the contour of the fragment projection onto each image plane will be automatically extracted. Benefited from the cylindrical shape of the fragments, we formulate the detection problem as an optimal process for fitting parameterized three-dimensional (3D) cylinder model to images. A RANSAC-based EM algorithm is proposed to find the optimal solution by converting the fragment detection procedure to an iterative closest point (ICP) matching procedure. The outer projection boundary of the estimated cylinder model is then fed to a region-based active contour model to robustly extract the contour of the fragment projection. The proposed algorithm has been successfully applied to real patient data with/without external objects, yielding promising results.
KEYWORDS: 3D modeling, Bone, Fluoroscopy, Data modeling, 3D image processing, Surgery, Visualization, Navigation systems, Data acquisition, Computed tomography
In this paper, a computerized fluoroscopy with zero-dose image updates for femoral diaphyseal fracture reduction is proposed. It is achieved with a two-step procedure. Starting from a few (normally 2) calibrated fluoroscopic image, the first step, data preparation, automatically estimates the size and the pose of the diaphyseal fragments through three-dimensional morphable object fitting using a parametric cylinder model. The projection boundary of each estimated cylinder, a quadrilateral, is then fed to a region information based active contour model to extract the fragment contours from the input fluoroscopic images. After that, each point on the contour is interpolated relative to the four vertices of the corresponding quadrilateral, which resulted in four interpolation coefficients per point. The second step, image updates, repositions the fragment projection on each acquired image during bony manipulation using a computerized method. It starts with interpolation of the new position of each point on the fragment contour using the interpolation coefficients calculated in the first step and the new position of the corresponding quadrilateral. The position of the quadrilateral is updated in real time according to the positional changes of the associated bone fragments, as determined by the navigation system during fracture reduction. The newly calculated image coordinates of the fragment contour are then fed to a OpenGL® based texture warping pipeline to achieve a real-time image updates. The presented method provides a realistic augmented reality for the surgeon. Its application may result in great reduction of the X-ray radiation to the patient and to the surgical team.
KEYWORDS: Error analysis, Data modeling, 3D modeling, Reconstruction algorithms, Surgery, 3D image enhancement, Bone, Mahalanobis distance, 3D image processing, Databases
Constructing anatomical shape from sparse information is a challenging task. A priori information is often required to handle this otherwise ill-posed problem. In this paper, the problem is formulated as a three-stage optimal estimation process using an a priori dense surface point distribution model (DS-PDM). The dense surface point distribution model itself is constructed from an already-aligned training shape set using Loop subdivision. It provides a dense and smooth description of all a priori training shapes. Its application in anatomical shape reconstruction facilitates all three stages as follows. The first stage, registration, is to iteratively estimate the scale and the 6-dimensional (6D) rigid registration transformation between the mean shape of DS-PDM and the input points using the iterative closest point (ICP) algorithm. Due to the dense description of the mean shape, a simple point-to-point distance is used to speed up the searching for closest point pairs. The second stage, morphing, optimally and robustly estimates a dense patient-specific template surface from DS-PDM using Mahalanobis distance based regularization. The estimated dense patient-specific template surface is then fed to the third stage, deformation, which uses a newly formularized kernel-based regularization to further reduce the reconstruction error. The proposed method is especially useful for accurate and stable surface reconstruction from sparse information when only a small number of a priori training shapes are available. It has been successfully tested on anatomical shape reconstruction of femoral heads using only dozens of sparse points, yielding very promising results.
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