Computed tomography is the modality of choice for poly-trauma patients to assess rapidly skeletal and vascular integrity of the whole body. Often several scans with and without contrast medium or with different spatial resolution are acquired. Efficient reading of the resulting extensive set of image data is vital, since it is often time critical to initiate the necessary therapeutic actions. A set of automatically found landmarks can facilitate navigation in the data and enables anatomy oriented viewing. Following this intention, we selected a comprehensive set of 17 skeletal and 5 aortic landmarks. Landmark localization models for the Discriminative Generalized Hough Transform (DGHT) were automatically created based on a set of about 20 training images with ground truth landmark positions. A hierarchical setup with 4 resolution levels was used. Localization results were evaluated on a separate test set, consisting of 50 to 128 images (depending on the landmark) with available ground truth landmark locations. The image data covers a large amount of variability caused by differences of field-of-view, resolution, contrast agent, patient gender and pathologies. The median localization error for the set of aortic landmarks was 14.4 mm and for the set of skeleton landmarks 5.5 mm. Median localization errors for individual landmarks ranged from 3.0 mm to 31.0 mm. The runtime performance for the whole landmark set is about 5s on a typical PC.
KEYWORDS: Visualization, 3D image processing, Arteries, Reconstruction algorithms, Angiography, Image segmentation, 3D image reconstruction, 3D acquisition, 3D visualizations, Image processing
High quality and high resolution three dimensional reconstruction of the coronary arteries from clinically obtained
rotational X-ray images during contrast injection has recently been attained through the use of advanced image
processing techniques, including gating, optimal heart phase selection, motion compensation, and iterative
reconstruction. While these strategies have produced excellent results despite severe angular under-sampling, the
volumes that result from these techniques contain artifact/background signal features which impede both the qualitative
as well as the quantitative analysis. This paper details a method for artifact removal from reconstructed 3D coronary
angiograms that uses a priori image content information to maximize the background removal while minimizing
influence on the reconstructed vessels. A variety of parameters are explored, and results indicate that this method can
greatly improve visualization for use in the catheterization laboratory as well as reduce the impact of the visualization
grey scale (window/level) on qualitative evaluation of the data.
The tomographic reconstruction of the beating heart requires dedicated methods. One possibility is gated
reconstruction, where only data corresponding to a certain motion state are incorporated. Another one is motioncompensated
reconstruction with a pre-computed motion vector field, which requires a preceding estimation of
the motion. Here, results of a new approach are presented: simultaneous reconstruction of a three-dimensional
object and its motion over time, yielding a fully four-dimensional representation. The object motion is modeled
by a time-dependent elastic transformation. The reconstruction is carried out with an iterative gradient-descent
algorithm which simultaneously optimizes the three-dimensional image and the motion parameters. The method
was tested on a simulated rotational X-ray acquisition of a dynamic coronary artery phantom, acquired on a
C-arm system with a slowly rotating C-arm. Accurate reconstruction of both absorption coefficient and motion
could be achieved. First results from experiments on clinical rotational X-ray coronary angiography data are
shown. The resulting reconstructions enable the analysis of both static properties, such as vessel geometry and
cross-sectional areas, and dynamic properties, like magnitude, speed, and synchrony of motion during the cardiac
cycle.
Three-dimensional (3D) reconstruction of the coronary arteries offers great advantages in the diagnosis and treatment of cardiovascular diseases, compared to two-dimensional X-ray angiograms. Besides improved roadmapping, quantitative analysis of vessel lesions is possible. To perform 3D reconstruction, rotational projection data of the selectively contrast agent enhanced coronary arteries are acquired with simultaneous ECG recording. For the reconstruction of one cardiac phase, the corresponding projections are selected from the rotational sequence by nearest-neighbor ECG gating. This typically provides only 5-10 projections per cardiac phase. The severe angular undersampling leads to an ill-posed reconstruction problem.
In this contribution, an iterative reconstruction method is presented which employs regularizations especially suited for the given reconstruction problem. The coronary arteries cover only a small fraction of the reconstruction volume. Therefore, we formulate the reconstruction problem as a minimization of the L1-norm of the reconstructed image, which results in a spatially sparse object. Two additional regularization terms are introduced: a 3D vesselness prior, which is reconstructed from vesselness-filtered projection data, and a Gibbs smoothing prior. The regularizations favor the reconstruction of the desired object, while taking care not to over-constrain the reconstruction by too detailed a-priori assumptions. Simulated projection data of a coronary artery software phantom are used to evaluate the performance of the method. Human data of clinical cases are presented to show the method's potential for clinical application.
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