Simone Prummer

Image-based device tracking for the co-registration of angiography and intravascular ultrasound images

The accurate and robust tracking of catheters and transducers employed during image-guided coronary intervention is critical to improve the clinical workflow and procedure outcome. Image-based device detection and tracking methods are preferred due to the straightforward integration into existing medical equipments. In this paper, we present a novel computational framework for image-based device detection and tracking applied to the co-registration of angiography and intravascular ultrasound (IVUS), two modalities commonly used in interventional cardiology. The proposed system includes learning-based detections, model-based tracking, and registration using the geodesic distance. The system receives as input the selection of the coronary branch under investigation in a reference angiography image. During the subsequent pullback of the IVUS transducers, the system automatically tracks the position of the medical devices, including the IVUS transducers and guiding catheter tips, under fluoroscopy imaging. The localization of IVUS transducers and guiding catheter tips is used to continuously associate an IVUS imaging plane to the vessel branch under investigation. We validated the system on a set of 65 clinical cases, with high accuracy (mean errors less than 1.5mm) and robustness (98.46% success rate). To our knowledge, this is the first reported system able to automatically establish a robust correspondence between the angiography and IVUS images, thus providing clinicians with a comprehensive view of the coronaries.

Dynamic Layer Separation for Coronary DSA and Enhancement in Fluoroscopic Sequences

This paper presents a new technique of coronary digital subtraction angiography which separates layers of moving background structures from dynamic fluoroscopic sequences of the heart and obtains moving layers of coronary arteries. A Bayeisan framework combines dense motion estimation, uncertainty propagation and statistical fusion to achieve reliable background layer estimation and motion compensation for coronary sequences. Encouraging results have been achieved on clinically acquired coronary sequences, where the proposed method considerably improves the visibility and perceptibility of coronary arteries undergoing breathing and cardiac movements. Perceptibility improvement is significant especially for very thin vessels. Clinical benefit is expected in the context of obese patients and deep angulation, as well as in the reduction of contrast dose in normal size patients.

Coronary Tree Extraction Using Motion Layer Separation

Fluoroscopic images contain useful information that is difficult to comprehend due to the collapse of the 3D information into 2D space. Extracting the informative layers and analyzing them separately could significantly improve the task of understanding the image content. Traditional Digital Subtraction Angiography (DSA) is not applicable for coronary angiography because of heart beat and breathing motion. In this work, we propose a layer extraction method for separating transparent motion layers in fluoroscopic image sequences, so that coronary tree can be better visualized.. The method is based on the fact that different anatomical structures possess different motion patterns, e.g., heart is beating fast, while lung is breathing slower. A multiscale implementation is used to further improve the efficiency and accuracy. The proposed approach helps to enhance the visibility of the vessel tree, both visually and quantitatively.

Coronary DSA: Enhancing Coronary Tree Visibility through Discriminative Learning and Robust Motion Estimation

Digital subtraction angiography (DSA) is a well-known technique for improving the visibility and perceptibility of blood vessels in the human body. Coronary DSA extends conventional DSA to dynamic 2D fluoroscopic sequences of coronary arteries which are subject to respiratory and cardiac motion. Effective motion compensation is the main challenge for coronary DSA. Without a proper treatment, both breathing and heart motion can cause unpleasant artifacts in coronary subtraction images, jeopardizing the clinical value of coronary DSA. In this paper, we present an effective method to separate the dynamic layer of background structures from a fluoroscopic sequence of the heart, leaving a clean layer of moving coronary arteries. Our method combines the techniques of learning-based vessel detection and robust motion estimation to achieve reliable motion compensation for coronary sequences. Encouraging results have been achieved on clinically acquired coronary sequences, where the proposed method considerably improves the visibility and perceptibility of coronary arteries undergoing breathing and cardiac movement. Perceptibility improvement is significant especially for very thin vessels. The potential clinical benefit is expected in the context of obese patients and deep angulation, as well as in the reduction of contrast dose in normal size patients.