Organ motion during radiotherapy is one of causes of uncertainty in dose delivery. To cope with this, the planned target
volume (PTV) has to be larger than needed to guarantee full tumor irradiation. Existing methods deal with the problem by
performing tumor tracking using implanted fiducial markers or magnetic sensors. In this work, we investigate the feasibility
of using x-ray based real time 2D/3D registration for non-invasive tumor motion tracking during radiotherapy. Our method
uses purely intensity based techniques, thus avoiding markers or fiducials. X-rays are acquired during treatment at a rate
of 5.4Hz. We iteratively compare each x-ray with a set of digitally reconstructed radiographs (DRR) generated from the
planning volume dataset, finding the optimal match between the x-ray and one of the DRRs. The DRRs are generated using
a ray-casting algorithm, implemented using general purpose computation on graphics hardware (GPGPU) programming
techniques using CUDA for greater performance. Validation is conducted off-line using a phantom and five clinical patient
data sets. The registration is performed on a region of interest (ROI) centered around the PTV. The phantom motion
is measured with an rms error of 2.1 mm and mean registration time is 220 ms. For the patient data sets, a sinusoidal
movement that clearly correlates to the breathing cycle is seen. Mean registration time is always under 105 ms which
is well suited for our purposes. These results demonstrate that real-time organ motion monitoring using image based
markerless registration is feasible.
Setting up a reliable and accurate reference coordinate system is a crucial part in computer assisted navigated
surgery. As the use of splints is a well established technique for this purpose and any change in its geometry
directly influences the accuracy of the navigation, a regular monitoring of such deformations should occur as a
means of quality control.
This work presents a method to quantify such deformations based on computed tomography images of a splint
equipped with fiducial markers. Point-to-point registration is used to match the two data sets and some markers
near to the navigation field are used to estimate the registration error. The Hausdorff Distance, describing the
maximum of all minimal distances between two point sets in general, is applied to the surfaces of the models,
being a measure for the overall change in geometry.
Finally this method for quantification is demonstrated using a computed tomography data set of such a splint
together with an artificially modified one, being an initial step to a study examining the influence of the Sterrad
sterilisation system on acrylic splints.
KEYWORDS: Image registration, Image segmentation, Magnetic resonance imaging, Image fusion, Medical imaging, Visualization, Visual process modeling, Current controlled current source, In vivo imaging, Kinematics
A method for studying the in vivo kinematics of complex joints is presented.
It is based on automatic fusion of single slice cine MR images capturing the dynamics and a static MR volume.
With the joint at rest the 3D scan is taken. In the data the anatomical compartments are identified and segmented resulting in a 3D volume of each individual part. In each of the cine MR images the joint parts are segmented and their pose and position are derived using a 2D/3D slice-to-volume registration to the volumes.
The method is tested on the carpal joint because of its complexity and the small but complex motion of its compartments.
For a first study a human cadaver hand was scanned and the method was evaluated with
artificially generated slice images. Starting from random initial positions of about 5 mm translational and 12°
rotational deviation, 70 to 90 % of the registrations converged successfully to a deviation better than 0.5 mm
and 5°.
First evaluations using real data from a cine MR were promising.
The feasibility of the method was demonstrated. However we experienced difficulties with the segmentation
of the cine MR images.
We therefore plan to examine different parameters for the image acquisition in future
studies.
Nowadays, radiation therapy systems incorporate kV imaging units which allow for the real-time acquisition
of intra-fractional X-ray images of the patient with high details and contrast. An application of this technology
is tumor motion monitoring during irradiation. For tumor tracking, implanted markers or position sensors
are used which requires an intervention. 2D/3D intensity based registration is an alternative, non-invasive
method but the procedure must be accelerate to the update rate of the device, which lies in the range of 5
Hz. In this paper we investigate fast CT to a single kV X-ray 2D/3D image registration using a new porcine
reference phantom with seven implanted fiducial markers. Several parameters influencing the speed and accuracy
of the registrations are investigated. First, four intensity based merit functions, namely Cross-Correlation,
Rank Correlation, Mutual Information and Correlation Ratio, are compared. Secondly, wobbled splatting and
ray casting rendering techniques are implemented on the GPU and the influence of each algorithm on the
performance of 2D/3D registration is evaluated. Rendering times for a single DRR of 20 ms were achieved.
Different thresholds of the CT volume were also examined for rendering to find the setting that achieves the best
possible correspondence with the X-ray images. Fast registrations below 4 s became possible with an inplane
accuracy down to 0.8 mm.
In this paper, we propose a new gold standard data set for the validation of 2D/3D image registration algorithms for
image guided radiotherapy. A gold standard data set was calculated using a pig head with attached fiducial markers. We
used several imaging modalities common in diagnostic imaging or radiotherapy which include 64-slice computed
tomography (CT), magnetic resonance imaging (MRI) using T1, T2 and proton density (PD) sequences, and cone beam
CT (CBCT) imaging data. Radiographic data were acquired using kilovoltage (kV) and megavoltage (MV) imaging
techniques. The image information reflects both anatomy and reliable fiducial marker information, and improves over
existing data sets by the level of anatomical detail and image data quality. The markers of three dimensional (3D) and
two dimensional (2D) images were segmented using Analyze 9.0 (AnalyzeDirect, Inc) and an in-house software. The
projection distance errors (PDE) and the expected target registration errors (TRE) over all the image data sets were found
to be less than 1.7 mm and 1.3 mm, respectively. The gold standard data set, obtained with state-of-the-art imaging
technology, has the potential to improve the validation of 2D/3D registration algorithms for image guided therapy.
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