High-density objects, such as metal prostheses or surgical clips, generate streak-like artifacts in CT images. We designed a radial adaptive filter, which directly operates on the corrupted reconstructed image, to effectively and efficiently reduce such artifacts. The filter adapts to the severity of local artifacts to preserve spatial resolution as much as possible. The widths and direction of the filter are derived from the local structure tensor. Visual inspection shows that this novel radial adaptive filter is superior with respect to existing methods in the case of mildly distorted images. In the presence of strong artifacts we propose a hybrid approach. An image corrected with a standard method, which performs well on images with regions of severe artifacts, is fused with an adaptively filtered clone to combine the strengths of both methods.
A number of image analysis tasks of the heart region have to cope
with both the problem of respiration and heart contraction. While
the heart contraction status can be estimated based on the ECG,
respiration status estimation must be based on the images themselves, unless additional devices for respiration measurements
are used. Since diaphragm motion is closely linked to respiration,
we describe a method to detect and track the diaphragm in x-ray
projections. We model the diaphragm boundary as being approximately
circular. Diaphragm detection is then based on edge detection
followed by a Hough transform for circles. To avoid that the
detection algorithm is misled by high frequency image content, we
first apply a morphological multi-scale top hat operator. A Canny
edge detector is then applied to the top hat filtered images. In the
edge images, the circle corresponding to the diaphragm boundary is
found by the Hough transform. To restrict the search in the 3D Hough
parameter space (parameters are circle center coordinates and
radius), prior anatomical knowledge about position and size of the
diaphragm for the given image acquisition geometry is taken into
account. In subsequent frames, diaphragm position and size are
predicted from previous detection and tracking results. For each
detection result, a confidence measure is computed by analyzing the
Hough parameter space with respect to the goodness of the peak
giving the circle parameters and by analyzing the coefficient of
variation of the pixel that form the circle described by the maximum
in Hough parameter space. If the confidence is not sufficiently high
-- indicating a poor fit between the Hough circle and true diaphragm
boundary -- the detection result is optionally refined by an active
Percutaneous Transluminal Coronary Angioplasty is currently the preferred method for coronary artery disease treatment. Angiograms depict residual lumen, but lack information about plaque characteristics and exact geometry. During instrument positioning, intracoronary characterization at the current instrument location is desirable. By pulling back an intravascular ultrasound (IVUS) probe through a stenosis, cross-sections of the artery are acquired. These images can provide the desired characterization if they are properly registered to diagnostic angiograms or interventional fluoroscopies. The method we propose acquires fluoroscopy frames at the beginning, end, and optionally during a constant speed pullback. The IVUS probe is localized and registered to previously acquired angiograms using a compensation algorithm for heartbeat and respiration. Then, for each heart phase, the pullback path is interpolated and the corresponding IVUS frames are positioned. During the intervention the instrument is localized and registered onto the pullback path. Thus, each IVUS frame can be registered with a position on an angiogram or to an instrument location and during subsequent steps of the intervention the appropriate IVUS frames can be displayed as if an IVUS probe were present at the instrument position. The method was tested using a phantom featuring respiratory and contraction movement and an automatic pullback with constant speed. The IVUS acquisition was replaced by fibre optics and the phantom was imaged in angiographic and fluoroscopic modes. The study showed that for the phantom case it is indeed possible to register the IVUS cross-section to the interventional instrument positions to an accuracy of less than 2mm.
The fusion of information in medical imaging relies on accurate registration of the image content coming often from different sources. One of the strongest influences on the movement of organs is the patient’s respiration. It is known, that respiration status can be measured by comparing the projection images of the chest. Since the diaphragm compresses the soft tissue above, the level of similarity to a reference projection image in extremely inhaled or exhaled status gives an indication of the patient’s respiration status. If the images to be registered are generated under different conditions the similarity with a common reference image is calculated on different scales and therefore cannot be compared directly. The proposed solution uses two reference images acquired in extremely inhaled and exhaled position. By comparing the images with two references and by combining the similarity results, changes in respiration depth between acquisitions can be detected. With normal breathing, the similarity to one of the reference images increases while the similarity to the other one decreases over time or vice versa. If the patient’s respiration exceeds the respiration span of the reference images, the similarity to both reference images decreases. By using not only the similarity values but also their derivatives over time, changes in respiration depth therefore can be detected and the image fusion algorithm can act accordingly e.g. by removing images that exceed the valid respiration span.
A data-driven algorithmic structure on a standard PC was developed for a block-based motion compensated temporal filtering in real time. The major time limiting factor of the algorithm was identified as the irregular memory access mainly caused by the layered multi-resolution representation of the input frames. As a result, data is transferred from main memory to cache multiple times leading to memory-dominated critical paths in execution. In order to improve the cache utilization, the computations have been rearranged to process the complete signal on the cached subset of data. The input frames are now divided into super-lines, which are subsets of data containing the relevant information to calculate one line of motion vectors and to filter the corresponding image lines. Only when a set of data is no longer used nor for motion vector analysis nor for filtering the images themselves it is replaced by data of different layers or lines. Due to these data-driven techniques the cache capacity miss rate is reduced to less than 0.8%. As a result, images are processed at a rate of more than 44 fps on a standard PC (Intel dual-processor Xeon, 1.8 GHz), as opposed to 1 fps in the standard implementation.
Coronary angiograms are pre-interventionally recorded moving X-ray images of a patient's beating heart, where the coronary arteries are made visible by a contrast medium. They serve to diagnose, e.g., stenoses, and as roadmaps during the intervention itself. Covering about three to four heart cycles, coronary angiograms consist of three underlying states: inflow, when the contrast medium flows into the vessels, filled state, when the whole vessel tree is visible and outflow, when the contrast medium is washed out. Obviously, only that part of the sequence showing the full vessel tree is useful as a roadmap. We therefore describe methods for automatic identification of these frames. To this end, a vessel map with enhanced vessels and compressed background is first computed. Vessel enhancement is based on the observation that vessels are the locally darkest oriented structures with significant motion. The vessel maps can be regarded as containing two classes, viz. (bright) vessels and (dark)background. From a histogram analysis of each vessel map image, a time-dependent feature curve is computed in which the states inflow, filled state and outflow can already visually be distinguished. We then describe two approaches to segment the feature curve into these states: the first method models the observations in each state by a polynomial, and seeks the segmentation which allows the best fit of three polynomials as measured by a Maximum-Likelihood criterion. The second method models the state sequence by a Hidden Markov model, and estimates it using the Maximum a Posteriori (MAP)-criterion. We will
present results for a number of angiograms recorded in clinical routine.
Minimally-invasive interventions are an important domain of medical real-time imaging modalities. Image processing algorithms that enhance interventional images run within hard real-time and latency constraints due to the required hand-eye coordination of physicians which perform the intervention. To support research activities,
we present a flexible software architecture that allows to transfer image enhancement algorithms from research to clinical validation. The software architecture especially pays regard to multimodality interventional scenarios where an intervention runs in close succession to the acquisition of diagnostic data. Including the additional information of such diagnostic acquisitions enables content-based image enhancement. The proposed software
architecture administers threads for a graphical user interface, data acquisition, offline preparation of diagnostic data, and the context-based real-time enhancement itself. Using this architecture, it is possible to run arbitrary complex content-based image analysis in real-time with only 9% computational overhead during the latency introducing algorithm run time. The proposed architecture is exemplified with an application for navigation support in cardiac CathLab interventions where diagnostic exposure acquisitions and interventional fluoroscopy can alternate in close succession.
An overlay of diagnostic angiograms and interventional fluoroscopy during minimally invasive cathlab interventions can support navigation but suffers from artifacts due to mismatch of vessels and interventional devices. Here, weak image features and strict real-time constraints do not allow for standard multi-modality registration
techniques. In the presented method, diagnostic angiograms are filtered to extract the imaged vessel structure. A distance-transform of the extracted vessels allows for fast matching with interventionally imaged devices which are extracted with fast local filters only. Competing vessel and object filters are tested on 10 diagnostic angiograms and 25 fluoroscopic frames showing a guidewire. Their performance is tested in comparison to manual segmentations. A newly presented directional stamping-filter based on anisotropic diffusion of local image patches offers the best results for vessel extraction and also improves the guidewire detection. Using these filters, the device-to-vessel match succeeds in 92% of the tested frames. This rate decreases to 75% for an initial mismatch
of 16 pixels.
In CT imaging, high absorbing objects such as metal bodies may cause significant artifacts, which may, for example, result in dose inaccuracies in the radiation therapy planning process. In this work, we aim at reducing the local and global image artifact, in order to improve the overall dose accuracy. The key part f this approach is the correction of the original projection data in those regions, which feature defects caused by rays traversing the high attenuating objects in the patient. The affected regions are substituted by model data derived from the original tomogram deploying a segmentation method. Phantom and climnical studies demonstrate that the proposed method significantly reduces the overall artifacts while preserving the information content of the image as much as possible. The image quality improvements were quantified by determining the signal-to-noise ratio, the artifact level and the modulation transfer function. The proposed method is computationally efficient and can easily be integrated into commercial CT scanners and radiation therapy planning software.
In coronary x-ray angiographies, the vessels supplying the heart are imaged in a number of states uniquely determined
by a combination of the respiratory intake and the heart contraction of the patient. The angiographic frames of one
sequence represent not all possible combinations of respiration and heart contraction. A couple of applications need a
continuous and dense sampling of the state-space given by the two axes 'respiration' and 'contraction', e.g. background
removal or motion-compensated catheter navigation. We present a novel method of interpolating above the twodimensional
phase-space based on pairs of angiographic frames with similar contraction, but different respiration status.
First a hypothetical model of the respiration motion is formulated, e.g. rigid transformation or rigid translation. Then the
parameters that transform a single frame into another one with similar contraction status are calculated for a number of
frames. An iterative approach is used to reconstruct the generalized transformation function from the transformation
parameters of frame pairs. Using this function, angiographic frames of arbitrary respiration status can be generated. It is
shown that the synthesized angiographies closely match real angiographies acquired at the same combination of
contraction and respiration status.
We present a novel method for intra-frame image processing, which is applicable to a wide variety of medical imaging modalities, like X-ray angiography, X-ray fluoroscopy, magnetic resonance, or ultrasound. The method allows to reduce noise significantly - by about 4.5 dB and more - while preserving sharp image details. Moreover, selective amplification of image details is possible. The algorithm is based on a multi-resolution approach. Noise reduction is achieved by non-linear adaptive filtering of the individual band pass layers of the multi-resolution pyramid. The adaptivity is controlled by image gradients calculated from the next coarser layer of the multi-resolution pyramid representation, thus exploiting cross-scale dependencies. At sites with strong gradients, filtering is performed only perpendicular to the gradient, i.e. along edges or lines. The multi-resolution approach processes each detail on its appropriate scale so that also for low frequency noise small filter kernels are applied, thus limiting computational costs and allowing a real-time implementation on standard hardware. In addition, gradient norms are used to distinguish smoothly between “structure” and “noise only” areas, and to perform additional noise reduction and edge enhancement by selectively attenuating or amplifying the corresponding band pass coefficients.
To achieve significant noise reduction in medical images while at the same time preserving fine structures of diagnostic value, a non-linear filter called the multi-resolution gradient adaptive filter (MRGAF) was developed. Though the algorithm is well suited for its task of noise reduction in medical images, it is still limited to the application of offline processing in medical workstations due to its computational complexity. The aim of our study is to reach real-time processing of data from low-cost x-ray systems on a standard PC without additional hardware. One major drawback of the original MRGAF procedure is its irregular memory access behavior caused by the intermediate multi-resolution representation of the image (Laplacian pyramid). This is addressed by completely re-arranging the computation. The image is divided into super-lines carrying all relevant information of all pyramidal levels, which allow to apply the complete MRGAF procedure in a single pass. This way, the cache utilization is improved considerably, the total number of memory accesses is reduced, and the use of super-scalar processing capabilities of current processors is facilitated. The current implementation allows applying advanced multi-resolution non-linear noise reduction to images of 768 × 564 pixels at a rate of more than 30 frames per second on a workstation. This shows that high-quality real-time image enhancement is feasible from a technical as well as from an economical point of view.