Anja Hennemuth

Image fusion of coronary CT angiography and cardiac perfusion MRI: a pilot study

ObjectiveTo develop a tool for the image fusion of computed tomography coronary angiography (CTCA) and cardiac magnetic resonance imaging (CMR).MethodsSurface representations and volume-rendered images from fused CTCA/CMR data of five patients with significant coronary artery disease (CAD) on CTCA and perfusion deficits on CMR were generated using a newly developed software prototype. The spatial relationship of significant coronary artery stenosis at CTCA and myocardial defects at CMR was evaluated.ResultsRegistration of CTCA and CMR images was possible in all patients. The comprehensive three-dimensional visualisation of fused CTCA and CMR data accurately demonstrated the relationship between coronary artery stenoses and myocardial defects in all patients.ConclusionThe introduced tool enables image fusion of CTCA and CMR data sets and allows for correct superposition of the coronary arteries derived from CTCA onto the corresponding myocardial segments derived from CMR. The method facilitates the comprehensive assessment of the functionally relevant CAD by the exact allocation of culprit coronary stenoses to corresponding myocardial defects at a low radiation dose.

Motion analysis with quadrature filter based registration of tagged MRI sequences

Analysis of tagged MRI is a valuable tool for assessing regional myocardial function. One major obstacle for existing methods based on feature extraction and registration is the desaturation of the tagging grid over time. We propose a method based on quadrature filters that is invariant to changes in intensity, robust with respect to the grid geometry and provides a dense motion field that allows for the analysis of both global and local movements. A multi-scale and multi-resolution scheme is used to cover different scales of motion and to speed up registration. The described method has been integrated into a prototypical application and applied to a phantom data set and 15 volunteer data sets provided by the STACOM11. The automatic detection of the 4D motion field took about 130 minutes per MRI data set and about 90 minutes per US data set and resulted in plausible motion fields, which will be quantitatively assessed within the motion tracking challenge at MICCAI 2011.

Mixture-Model-Based segmentation of myocardial delayed enhancement MRI

Myocardial viability assessment is an important task in the diagnosis of coronary heart disease. The measurement of the delayed enhancement effect, the accumulation of contrast agent in defective tissue, has become the gold standard for detecting necrotic tissue with MRI. The purpose of the presented work was to provide a segmentation and quantification method for delayed enhancement MRI. To this end, a suitable mixture model for the myocardial intensity distribution is determined based on expectation maximization and the comparison of the fit accuracy. The subsequent watershed-based segmentation uses the intensity threshold information derived from this model. Preliminary results are derived from an analysis of datasets provided by the STACOM challenge organizers. The segmentation provided reasonable results in all datasets, but the method strongly depends on the underlying myocardium segmentation.

3D fusion of functional cardiac magnetic resonance imaging and computed tomography coronary angiography: accuracy and added clinical value

Purpose:To evaluate the accuracy and added diagnostic value of 3-dimensional (3D) image fusion of computed tomography coronary angiography (CTCA) and functional cardiac magnetic resonance (CMR) for assessing hemodynamically relevant coronary artery disease (CAD). Methods:Twenty-seven patients with significant coronary stenoses on prospectively electrocardiography-gated dual-source CTCA, confirmed by catheter angiography and perfusion defects on CMR at 1.5 T were included. Surface representations and volume-rendered images from 3D-fused CTCA/CMR data were generated using a software prototype. Fusion accuracy was evaluated by calculating surface distances of blood pools and Dice similarity coefficients. Two independent, blinded readers assigned myocardial defects to culprit coronary arteries with side-by side analysis of CTCA and CMR and using fused CTCA/CMR. Added value of fused CTCA/CMR was defined as change in assignment of culprit coronary artery to myocardial defect compared with side-by-side analysis. Results:3D fusion of CTCA/CMR was feasible and accurate (surface distance of blood pools: 4.1 ± 1.3 mm, range: 2.4–7.1 mm; Dice similarity coefficients: 0.78 ± 0.08, range: 0.51–0.86) in all patients. Side-by-side analysis of CTCA and CMR allowed no assignment of a single culprit artery to a myocardial defect in 6 of 27 (22%) patients. Fused CTCA/CMR allowed further confinement of culprit coronary arteries in 3 of these 6 patients (11%). Myocardial defects were reassigned in 2 of 27 (7%) patients using fused CTCA/CMR, whereas the results remained unchanged in 22 of 27 (81%) patients. Interobserver agreement for assignment of culprit arteries to myocardial defects increased with fused CTCA/CMR (k = 0.66–0.89). Conclusion:3D fusion of low-dose CTCA and functional CMR is feasible and accurate, and adds, at a low radiation dose, diagnostic value for the assessment of hemodynamically relevant CAD as compared with side-by-side analysis alone. This technique can be clinically useful for the following: planning of surgical or interventional procedures in patients having a high prevalence of CAD and for improved topographic assignment of coronary stenoses to corresponding myocardial perfusion defects.

Quadrature filter based motion analysis for 3d ultrasound sequences

Analysis of echocardiograms is a valuable tool for assessing myocardial function and diseases. Processing of ultrasound data is challenging due to noise levels and depth-dependent quality of structure edges. We propose to adapt a method based on quadrature filters that is invariant to changes in intensity and has been successfully applied to MRI data earlier. Quadrature-filter-based registration derives the spatial deformation between two images from the local phase shift. Because the local phase is intensity-invariant and requires inhomogeneity, e.g., noise and intensity variations, to properly pick up phase shifts, it is well suited for ultrasound data. A multi-resolution and multi-scale scheme is used to cover different scales of deformations. The type and strength of regularization of the dense deformation field can be specified for each level, allowing for weighting of global and local motion. To speed up the registration, deformation fields are determined slice-wise for three orientations of the original data and subsequently combined into a true 3D deformation field. The method is evaluated with the data and ground truth provided by the Cardiac Motion Analysis Challenge at STACOM 2012.

Image-based assessment of uncertainty in quantification of carotid lumen.

Measurements of the vessel lumen diameter are often used to determine the degree of atherosclerotic disease in carotid arteries. However, quantification results vary with imaging technique and acquisition settings. In this work, we aim at providing a tool, that quantifies the lumen diameter on different image datasets and gives an estimate of quantification uncertainties, so that they can be taken into consideration when evaluating and comparing measurements. For the segmentation of the vessel lumen we present an algorithm using ray-casting techniques and partial volume correction. We furthermore propose a scheme for analysis and exploration of the lumen diameter. Finally, we present clinically relevant application scenario, in which we explore agreement between lumen diameter estimations in corresponding CTA, CEMRA, TOF and subtraction images of carotid vessels with severe carotid atherosclerotic plaques.

Extraction of open-state mitral valve geometry from CT volumes

PurposeThe importance of mitral valve therapies is rising due to an aging population. Visualization and quantification of the valve anatomy from image acquisitions is an essential component of surgical and interventional planning. The segmentation of the mitral valve from computed tomography (CT) acquisitions is challenging due to high variation in appearance and visibility across subjects. We present a novel semi-automatic approach to segment the open-state valve in 3D CT volumes that combines user-defined landmarks to an initial valve model which is automatically adapted to the image information, even if the image data provide only partial visibility of the valve.MethodsContext information and automatic view initialization are derived from segmentation of the left heart lumina, which incorporates topological, shape and regional information. The valve model is initialized with user-defined landmarks in views generated from the context segmentation and then adapted to the image data in an active surface approach guided by landmarks derived from sheetness analysis. The resulting model is refined by user landmarks.ResultsFor evaluation, three clinicians segmented the open valve in 10 CT volumes of patients with mitral valve insufficiency. Despite notable differences in landmark definition, the resulting valve meshes were overall similar in appearance, with a mean surface distance of

Semi-Automatic 4D Fuzzy Connectedness Segmentation of Heart Ventricles in Cine MRI

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