Kai Eck

Nonlinear multiresolution gradient adaptive filter for medical images

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.

Segmentation-aided adaptive filtering for metal artifact reduction in radio-therapeutic CT images

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.

Fast Detection and Processing of Arbitrary Contrast Agent Injections in Coronary Angiography and Fluoroscopy

Percutaneous transluminal coronary angioplasty (PTCA) requires both pre-interventional cine-angiograms showing the contrasted vessel tree over several heart cycles, and live X-ray monitoring (fluoroscopy) during the catheterization. Navigation during the intervention can be facilitated by fusing the automatically synchronized cineangiogram with the interventional images, e.g. by overlaying the synchronized angiogram over the interventional images. Clearly, this fusion should be limited to those frames of the angiogram which show the full contrasted vessel tree. Conversely, if contrast agent appears in the fluoroscopy images, overlay is not required and should be switched off. To these ends, we describe approaches for the detection and processing of contrast agent injections in cardiac X-ray image sequences.

Absolute alignment of breathing states using image similarity derivatives

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.

Fast and robust diaphragm detection and tracking in cardiac x-ray projection images

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 contour algorithm.

Statistical-model-based identification of complete vessel-tree frames in coronary angiograms

Coronary angiograms are pre-interventionally recorded moving X-ray images of a patients 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.

Algorithmic solutions for live device-to-vessel match

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.

Mutual information based respiration detection

Abstract Percutaneous Transluminal Coronary Angioplasty (PTCA) is currently the preferred method for the treatment of coronary artery disease in vivo. During the intervention, images of the coronary arteries filled with a radiopaque contrast agent are acquired (angiography) in order to localize the lesion. One of these images is then displayed on a monitor. It serves as a roadmap that is afterwards utilised to navigate the interventional instruments to the lesion under constant low-dose X-ray surveillance (fluoroscopy). The fluoroscopic live images are displayed in real-time on a second monitor next to the roadmap monitor. It is desirable to provide for each fluoroscopic image an angiographic roadmap that exhibits a similar position and shape of the coronary vessels. To accomplish this goal, the respiration status in every (pre-recorded) angiographic and (live) fluoroscopic image has to be quantified in order to display angiographic roadmaps matching to the live fluoroscopic image. We present a method that estimates the respiration status of a patient from grey-level X-ray fluoroscopic images based on their similarity to a reference frame applying a mutual information (MI) similarity measure.

A radial adaptive filter for metal artifact reduction

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.

Vessel Segmentation for Angiographic Enhancement and Analysis

Angiography is a widely used method of vessel imaging for the diagnosis and treatment of pathological manifestations as well as for medical research. Vessel segmentation in angiograms is useful for analysis but also as a means to enhance the vessels. Often the vessel surface has to be quantified to evaluate the success of certain drugs treatment (e.g. aimed at angiogenesis in the case of transplanted skin) or to gain insight into different pathological manifestations (e.g. proliferative diabetic retinopathy). In this paper we describe algorithms for automatic vessel segmentation in angiograms. We first enhance likely vessel regions to obtain a vessel map which is then segmented. To remove false positives we accept in a second step only those regions showing branchings and bifurcations which are typical for a vessel tree.