Showing papers by "Bernd Fischer published in 2010"

A variational approach for the correction of field-inhomogeneities in EPI sequences

Abstract: A wide range of medical applications in clinic and research exploit images acquired by fast magnetic resonance imaging (MRI) sequences such as echo-planar imaging (EPI), e.g. functional MRI (fMRI) and diffusion tensor MRI (DT-MRI). Since the underlying assumption of homogeneous static fields fails to hold in practical applications, images acquired by those sequences suffer from distortions in both geometry and intensity. In the present paper we propose a new variational image registration approach to correct those EPI distortions. To this end we acquire two reference EPI images without diffusion sensitizing and with inverted phase encoding gradients in order to calculate a rectified image. The idea is to apply a specialized registration scheme which compensates for the characteristical direction dependent image distortions. In addition the proposed scheme automatically corrects for intensity distortions. This is done by evoking a problem dependent distance measure incorporated into a variational setting. We adjust not only the image volumes but also the phase encoding direction after correcting for patients head-movements between the acquisitions. Finally, we present first successful results of the new algorithm for the registration of DT-MRI datasets.

Recursive green's function registration

Abstract: Non-parametric image registration is still among the most challenging problems in both computer vision and medical imaging. Here, one tries to minimize a joint functional that is comprised of a similarity measure and a regularizer in order to obtain a reasonable displacement field that transforms one image to the other. A common way to solve this problem is to formulate a necessary condition for an optimizer, which in turn leads to a system of partial differential equations (PDEs). In general, the most time consuming part of the registration task is to find a numerical solution for such a system. In this paper, we present a generalized and efficient numerical scheme for solving such PDEs simply by applying 1-dimensional recursive filtering to the right hand side of the system based on the Green's function of the differential operator that corresponds to the chosen regularizer. So in the end we come up with a general linear algorithm. We present the associated Green's function for the diffusive and curvature regularizers and show how one may efficiently implement the whole process by using recursive filter approximation. Finally, we demonstrate the capability of the proposed method on realistic examples.

Tensor grid based image registration with application to ventilation estimation on 4D CT lung data.

Abstract: For many image registration tasks, the information contained in the original resolution of the image data is crucial for a subsequent medical analysis, e.g. accurate assessment of local pulmonary ventilation. However, the complexity of a non-parametric registration scheme is directly connected to the resolution of the images. Therefore, the registration is often performed on a downsampled version in order to meet runtime demands and thereby producing suboptimal results. To enable the application of the highest resolution at least in regions of high clinical importance, an approach is presented replacing the usually taken equidistant grids by tensor grids for image representation. We employ a non-parametric approach for the registration of a respiratory-gated 4D CT thorax scan. Tensor grids are introduced for the registration setting and compared to equidistant grids. For ventilation assessment, the Jacobian metric is explored. The application of the tensor grid approach makes the local usage of the original resolution feasible; thereby a smaller registration error is achieved in regions of higher resolution using the tensor grids, while the two types of grids perform similar in regions of equal resolution. Concerning the ventilation assessment, the Jacobian metric yields reasonable results, showing more detail using the tensor grids due to the higher resolution. The proposed approach using tensor grids preserves registration accuracy, while reducing computational demands. The application of the Jacobian metric for ventilation assessment in conjunction with tensor grids is promising; however, due to a missing ground-truth the medical relevance could not be established for the ventilation estimation so far.

Volume-constrained image registration for pre- and post-operative CT liver data

Abstract: The resection of a tumor is one of the most common tasks in liver surgery. Here, it is of particular importance to resect the tumor and a safety margin on the one hand and on the other hand to preserve as much healthy liver tissue as possible. To this end, a preoperative CT scan is taken in order to come up with a sound resection strategy. It is the purpose of this paper to compare the preoperative planning with the actual resection result. Obviously the pre- and postoperative data is not straightforward comparable, a meaningful registration is required. In the literature one may find a rigid and a landmark-based approach for this task. Whereas the rigid registration does not compensate for nonlinear deformation the landmark approach may lead to an unwanted overregistration. Here we propose a fully automatic nonlinear registration with volume constraints which seems to overcome both aforementioned problems and does lead to satisfactory results in our test cases.