Range imaging modalities, such as time-of-flight cameras (ToF), are becoming very popular for the acquisition of intra-operative
data, which can be used for registering the patient's anatomy with pre-operative data, such as 3D images generated
by computed tomographies (CT) or magnetic resonance imaging (MRI). However, due to the distortions that appear because
of the different acquisition principles of the input surfaces, the noise, and the deformations that may occur in the intra-operative
environment, we face different surface properties for points lying on the same anatomical locations and unreliable
feature points detection, which are crucial for most surface matching algorithms. In order to overcome these issues,
we present a method for automatically finding correspondences between surfaces that searches for minimally deformed
configurations. For this purpose, an error metric that expresses the reliability of a correspondence set based on its spatial
configuration is employed. The registration error is minimized by a combinatorial analysis through search-trees. Our
method was evaluated with real and simulated ToF and CT data, and showed to be reliable for the registration of partial
multi-modal surfaces with noise and distortions.
Proc. SPIE. 7962, Medical Imaging 2011: Image Processing
KEYWORDS: 3D acquisition, Cameras, Image processing, Image registration, Medical imaging, Algorithm development, In vitro testing, 3D image processing, 3D-TOF imaging, Current controlled current source
Time-of-flight (ToF) cameras are a novel, fast, and robust means for intra-operative 3D surface acquisition. They acquire surface information (range images) in real-time. In the intra-operative registration context, these surfaces must be matched to pre-operative CT or MR surfaces, using so called descriptors, which represent surface characteristics. We present a framework for local and global multi-modal comparison of surface descriptors and characterize the differences between ToF and CT data in an in vitro experiment. The framework takes into account various aspects related to the surface characteristics and does not require high resolution input data in order to establish appropriate correspondences. We show that the presentation of local and global comparison data allows for an accurate assessment of ToF-CT discrepancies. The information gained from our study may be used for developing ToF pre-processing and matching algorithms, or for improving calibration procedures for compensating systematic distance errors. The framework is available in the open-source platform Medical Imaging Interaction Toolkit (MITK).
The Iterative Closest Point (ICP) algorithm is a widely used method for geometric alignment of 3D models.
Given two roughly aligned shapes represented by two point sets, the algorithm iteratively establishes point
correspondences given the current alignment of the data and computes a rigid transformation accordingly. It
can be shown that the method converges to an at least local minimimum with respect to a mean-square distance
metric. From a statistical point of view, the algorithm implicitly assumes that the points are observed with
isotropic Gaussian noise. In this paper, we (1) present the first variant of the ICP that accounts for anisotropic
localization uncertainty in both shapes as well as in both steps of the algorithm and (2) show how to apply the
method for robust fine registration of surface meshes. According to an evaluation on medical imaging data,
the proposed method is better suited for fine surface registration than the original ICP, reducing the target
registration error (TRE) for a set of targets located inside or near the mesh by 80% on average.
Image-guided therapy systems generally require registration of pre-operative planning data with the patient's anatomy. One common approach to achieve this is to acquire intra-operative surface data and match it to surfaces extracted from the planning image. Although increasingly popular for surface generation in general, the novel Time-of-Flight (ToF) technology has not yet been applied in this context. This may be attributed to the fact that the ToF range images are subject to considerable noise. The contribution of this study is two-fold. Firstly, we present an adaption of the well-known bilateral filter for denoising ToF range images based on the noise characteristics of the camera. Secondly, we assess the quality of organ surfaces generated from ToF range data with and without bilateral smoothing using corresponding high resolution CT data as ground truth. According to an evaluation on five porcine organs, the root mean squared (RMS) distance between the denoised ToF data points and the reference computed tomography (CT) surfaces ranged from 3.0 mm (lung) to 9.0 mm (kidney). This corresponds to an error-reduction of up to 36% compared to the error of the original ToF surfaces.
One of the main challenges related to computer-assisted laparoscopic surgery is the accurate registration of
pre-operative planning images with patient's anatomy. One popular approach for achieving this involves intraoperative
3D reconstruction of the target organ's surface with methods based on multiple view geometry. The
latter, however, require robust and fast algorithms for establishing correspondences between multiple images of
the same scene. Recently, the first endoscope based on Time-of-Flight (ToF) camera technique was introduced.
It generates dense range images with high update rates by continuously measuring the run-time of intensity
modulated light. While this approach yielded promising results in initial experiments, the endoscopic ToF
camera has not yet been evaluated in the context of related work. The aim of this paper was therefore to
compare its performance with different state-of-the-art surface reconstruction methods on identical objects. For
this purpose, surface data from a set of porcine organs as well as organ phantoms was acquired with four
different cameras: a novel Time-of-Flight (ToF) endoscope, a standard ToF camera, a stereoscope, and a High
Definition Television (HDTV) endoscope. The resulting reconstructed partial organ surfaces were then compared
to corresponding ground truth shapes extracted from computed tomography (CT) data using a set of local and
global distance metrics. The evaluation suggests that the ToF technique has high potential as means for intraoperative
endoscopic surface registration.
Registration of multiple medical images commonly comprises the steps feature extraction, correspondences search and transformation computation. In this paper, we present a new method for a fast and pose independent search of correspondences using as features anatomical trees such as the bronchial system in the lungs or the vessel system in the liver. Our approach scores the similarities between the trees' nodes (bifurcations) taking into account both, topological properties extracted from their graph representations and anatomical properties extracted from the trees themselves. The node assignment maximizes the global similarity (sum of the scores of each pair of assigned nodes), assuring that the matches are distributed throughout the trees. Furthermore, the proposed method is able to deal with distortions in the data, such as noise, motion, artifacts, and problems associated with the extraction method, such as missing or false branches. According to an evaluation on swine lung data sets, the method requires less than one second on average to compute the matching and yields a high rate of correct matches compared to state of the art work.
Although the field of a navigated bronchoscopy gains increasing attention in the literature, robust guidance in the presence of respiratory motion and electromagnetic noise remains challenging.
The robustness of a previously introduced motion compensation approach was increased by taking into account the already traveled trajectory of the instrument within the lung. To evaluate the performance of the method a virtual environment, which accounts for respiratory motion and electromagnetic noise was used. The simulation is based on a deformation field computed from human computed tomography data. According to the results, the proposed method outperforms the original method and is suitable for lung motion compensation during electromagnetically guided interventions.