The skills required for obtaining informative x-ray fluoroscopy images are currently acquired while trainees
provide clinical care. As a consequence, trainees and patients are exposed to higher doses of radiation. Use of
simulation has the potential to reduce this radiation exposure by enabling trainees to improve their skills in a
safe environment prior to treating patients. We describe a low cost, high fidelity, fluoroscopy simulation system.
Our system enables operators to practice their skills using the clinical device and simulated x-rays of a virtual
patient. The patient is represented using a set of temporal Computed Tomography (CT) images, corresponding
to the underlying dynamic processes. Simulated x-ray images, digitally reconstructed radiographs (DRRs), are
generated from the CTs using ray-casting with customizable machine specific imaging parameters. To establish
the spatial relationship between the CT and the fluoroscopy device, the CT is virtually attached to a patient
phantom and a web camera is used to track the phantom’s pose. The camera is mounted on the fluoroscope’s
intensifier and the relationship between it and the x-ray source is obtained via calibration. To control image
acquisition the operator moves the fluoroscope as in normal operation mode. Control of zoom, collimation and
image save is done using a keypad mounted alongside the device’s control panel. Implementation is based on
the Image-Guided Surgery Toolkit (IGSTK), and the use of the graphics processing unit (GPU) for accelerated
image generation. Our system was evaluated by 11 clinicians and was found to be sufficiently realistic for training
All 2D/3D anatomy based rigid registration algorithms are iterative, requiring an initial estimate of the 3D data pose. Current initialization methods have limited applicability in the operating room setting, due to the constraints imposed by this environment or due to insufficient accuracy. In this work we use the Microsoft Kinect device to allow the surgeon to interactively initialize the registration process. A Kinect sensor is used to simulate the mouse-based operations in a conventional manual initialization approach, obviating the need for physical contact with an input device. Different gestures from both arms are detected from the sensor in order to set or switch the required working contexts. 3D hand motion provides the six degree-of-freedom controls for manipulating the pre-operative data in the 3D space. We evaluated our method for both X-ray/CT and X-ray/MR initialization using three publicly available reference data sets. Results show that, with initial target registration errors of 117:7 ± 28:9 mm a user is able to achieve final errors of 5:9 ± 2:6 mm within 158 ± 65 sec using the Kinect-based approach, compared to 4:8±2:0 mm and 88±60 sec when using the mouse for interaction. Based on these results we conclude that this method is sufficiently accurate for initialization of X-ray/CT and X-ray/MR registration in the OR.
The performance of most segmentation and registration algorithms depends on the values of internal parameters.
Most often, these are set empirically. This is a trial-and-error process in which the developer modifies the values
in an attempt to improve performance. This is an implicit form of optimization. In this paper, we present a
more intuitive and systematic framework for this type of problem. We then use it to estimate optimal parameter
values of a common registration problem. We formulate the performance of the registration problem as a
function of its internal parameters, and use optimization techniques to search for an optimal value for these
parameters. Registration quality is evaluated using a set of training images in which the anatomy of interest
was segmented and comparing the overlap between the segmentations as induced by the registration. As a
large number of computationally complex registrations are performed during the optimization, a cluster of MPI-enabled
computers are used collaboratively to reduce the computation time. We evaluated the proposed method
using ten CT images of the liver from five patients, and evaluated three optimization algorithms. The results
showed that, compared with the empirical values suggested in the published literature, our technique was able
to obtain parameter values that are tuned for particular applications in a more intuitive and systematic way.
In addition, the proposed framework can potentially be used to tune system parameter values appropriate for
specific input types.
We present a parallel implementation of a statistical shape model registration to 3D ultrasound images of the
lumbar vertebrae (L2-L4). Covariance Matrix Adaptation Evolution Strategy optimization technique, along
with Linear Correlation of Linear Combination similarity metric have been used, to improve the robustness and
capture range of the registration approach. Instantiation and ultrasound simulation have been implemented on
a graphics processing unit for a faster registration. Phantom studies show a mean target registration error of 3.2
mm, while 80% of all the cases yield target registration error of below 3.5 mm.
We describe a method to guide the surgical fixation of distal radius fractures. The method registers the fracture fragments to a volumetric intensity-based statistical anatomical atlas of distal radius, reconstructed from human cadavers and patient data, using a few intra-operative X-ray fluoroscopy images of the fracture. No pre-operative Computed Tomography (CT) images are required, hence radiation exposure to patients is substantially reduced. Intra-operatively, each bone fragment is roughly segmented from the X-ray images by a surgeon, and a corresponding segmentation volume is created from the back-projections of the 2D segmentations. An optimization procedure positions each segmentation volume at the appropriate pose on the atlas, while simultaneously deforming the atlas such that the overlap of the 2D projection of the atlas with individual fragments in the segmented regions is maximized. Our simulation results shows that this method can accurately identify the pose of large fragments using only two X-ray views, but for small fragments, more than two X-rays may be needed. The method does not assume any prior knowledge about the shape of the bone and the number of fragments, thus it is also potentially suitable for the fixation of other types of multi-fragment fractures.
In this paper, we propose a new method for 2D/3D registration and report its experimental results. The method employs
the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm to search for an optimal transformation that
aligns the 2D and 3D data. The similarity calculation is based on Digitally Reconstructed Radiographs (DRRs), which
are dynamically generated from the 3D data using a hardware-accelerated technique - Adaptive Slice Geometry Texture
Mapping (ASGTM). Three bone phantoms of different sizes and shapes were used to test our method: a long femur, a
large pelvis, and a small scaphoid. A collection of experiments were performed to register CT to fluoroscope and DRRs
of these phantoms using the proposed method and two prior work, i.e. our previously proposed Unscented Kalman Filter
(UKF) based method and a commonly used simplex-based method. The experimental results showed that: 1) with
slightly more computation overhead, the proposed method was significantly more robust to local minima than the
simplex-based method; 2) while as robust as the UKF-based method in terms of capture range, the new method was not
sensitive to the initial values of its exposed control parameters, and has also no special requirement about the cost
function; 3) the proposed method was fast and consistently achieved the best accuracies in all compared methods.