Janine Olesch
University of Lübeck
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Featured researches published by Janine Olesch.
Physics in Medicine and Biology | 2012
Lars Ruthotto; Harald Kugel; Janine Olesch; Bernd Fischer; Jan Modersitzki; Martin Burger; Carsten H. Wolters
Diffusion-weighted magnetic resonance imaging is a key investigation technique in modern neuroscience. In clinical settings, diffusion-weighted imaging and its extension to diffusion tensor imaging (DTI) are usually performed applying the technique of echo-planar imaging (EPI). EPI is the commonly available ultrafast acquisition technique for single-shot acquisition with spatial encoding in a Cartesian system. A drawback of these sequences is their high sensitivity against small perturbations of the magnetic field, caused, e.g., by differences in magnetic susceptibility of soft tissue, bone and air. The resulting magnetic field inhomogeneities thus cause geometrical distortions and intensity modulations in diffusion-weighted images. This complicates the fusion with anatomical T1- or T2-weighted MR images obtained with conventional spin- or gradient-echo images and negligible distortion. In order to limit the degradation of diffusion-weighted MR data, we present here a variational approach based on a reference scan pair with reversed polarity of the phase- and frequency-encoding gradients and hence reversed distortion. The key novelty is a tailored nonlinear regularization functional to obtain smooth and diffeomorphic transformations. We incorporate the physical distortion model into a variational image registration framework and derive an accurate and fast correction algorithm. We evaluate the applicability of our approach to distorted DTI brain scans of six healthy volunteers. For all datasets, the automatic correction algorithm considerably reduced the image degradation. We show that, after correction, fusion with T1- or T2-weighted images can be obtained by a simple rigid registration. Furthermore, we demonstrate the improvement due to the novel regularization scheme. Most importantly, we show that it provides meaningful, i.e. diffeomorphic, geometric transformations, independent of the actual choice of the regularization parameters.
Proceedings of SPIE | 2009
Janine Olesch; Nils Papenberg; Thomas Lange; Matthias Conrad; Bernd Fischer
In navigated liver surgery the key challenge is the registration of pre-operative planing and intra-operative navigation data. Due to the patients individual anatomy the planning is based on segmented, pre-operative CT scans whereas ultrasound captures the actual intra-operative situation. In this paper we derive a novel method based on variational image registration methods and additional given anatomic landmarks. For the first time we embed the landmark information as inequality hard constraints and thereby allowing for inaccurately placed landmarks. The yielding optimization problem allows to ensure the accuracy of the landmark fit by simultaneous intensity based image registration. Following the discretize-then-optimize approach the overall problem is solved by a generalized Gauss-Newton-method. The upcoming linear system is attacked by the MinRes solver. We demonstrate the applicability of the new approach for clinical data which lead to convincing results.
Proceedings of SPIE | 2010
Janine Olesch; Lars Ruthotto; Harald Kugel; Stefan Skare; Bernd Fischer; Carsten H. Wolters
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.
Proceedings of SPIE | 2014
Johannes Lotz; J. Berger; Benedikt Müller; Kai Breuhahn; Niels Grabe; Stefan Heldmann; André Homeyer; Bernd Lahrmann; Hendrik Laue; Janine Olesch; Michael Schwier; Oliver Sedlaczek; Arne Warth
Much insight into metabolic interactions, tissue growth, and tissue organization can be gained by analyzing differently stained histological serial sections. One opportunity unavailable to classic histology is three-dimensional (3D) examination and computer aided analysis of tissue samples. In this case, registration is needed to reestablish spatial correspondence between adjacent slides that is lost during the sectioning process. Furthermore, the sectioning introduces various distortions like cuts, folding, tearing, and local deformations to the tissue, which need to be corrected in order to exploit the additional information arising from the analysis of neighboring slide images. In this paper we present a novel image registration based method for reconstructing a 3D tissue block implementing a zooming strategy around a user-defined point of interest. We efficiently align consecutive slides at increasingly fine resolution up to cell level. We use a two-step approach, where after a macroscopic, coarse alignment of the slides as preprocessing, a nonlinear, elastic registration is performed to correct local, non-uniform deformations. Being driven by the optimization of the normalized gradient field (NGF) distance measure, our method is suitable for differently stained and thus multi-modal slides. We applied our method to ultra thin serial sections (2 μm) of a human lung tumor. In total 170 slides, stained alternately with four different stains, have been registered. Thorough visual inspection of virtual cuts through the reconstructed block perpendicular to the cutting plane shows accurate alignment of vessels and other tissue structures. This observation is confirmed by a quantitative analysis. Using nonlinear image registration, our method is able to correct locally varying deformations in tissue structures and exceeds the limitations of globally linear transformations.
Bildverarbeitung für die Medizin | 2009
Nils Papenberg; Janine Olesch; Thomas Lange; Peter-Michael Schlag; Bernd Fischer
The incorporation of additional user knowledge into a nonrigid registration process is a promising topic in modern registration schemes. The combination of intensity based registration and some interactively chosen landmark pairs is a major approach in this direction. There exist different possibilities to incorporate landmark pairs into a variational non-parametric registration framework. As the interactive localization of point landmarks is always prone to errors, a demand for precise landmark matching is bound to fail. Here, the treatment of the distances of corresponding landmarks as penalties within a constrained optimization problem offers the possibility to control the quality of the matching of each landmark pair individually. More precisely, we introduce inequality constraints, which allow for a sphere-like tolerance around each landmark. We illustrate the performance of this new approach for artificial 2D images as well as for the challenging registration of preoperative CT data to intra-operative 3D ultrasound data of the liver.
IEEE Transactions on Biomedical Engineering | 2016
Johannes Lotz; Janine Olesch; Benedikt Müller; Thomas Polzin; P. Galuschka; J. M. Lotz; Stefan Heldmann; Hendrik Laue; Margarita Gonzalez-Vallinas; Arne Warth; Bernd Lahrmann; Niels Grabe; Oliver Sedlaczek; Kai Breuhahn; Jan Modersitzki
Objective: Image registration of whole slide histology images allows the fusion of fine-grained information-like different immunohistochemical stains-from neighboring tissue slides. Traditionally, pathologists fuse this information by looking subsequently at one slide at a time. If the slides are digitized and accurately aligned at cell level, automatic analysis can be used to ease the pathologists work. However, the size of those images exceeds the memory capacity of regular computers. Methods: We address the challenge to combine a global motion model that takes the physical cutting process of the tissue into account with image data that is not simultaneously globally available. Typical approaches either reduce the amount of data to be processed or partition the data into smaller chunks to be processed separately. Our novel method first registers the complete images on a low resolution with a nonlinear deformation model and later refines this result on patches by using a second nonlinear registration on each patch. Finally, the deformations computed on all patches are combined by interpolation to form one globally smooth nonlinear deformation. The NGF distance measure is used to handle multistain images. Results: The method is applied to ten whole slide image pairs of human lung cancer data. The alignment of 85 corresponding structures is measured by comparing manual segmentations from neighboring slides. Their offset improves significantly, by at least 15%, compared to the low-resolution nonlinear registration. Conclusion/Significance: The proposed method significantly improves the accuracy of multistain registration which allows us to compare different antibodies at cell level.
Archive | 2009
Thomas Lange; Nils Papenberg; Janine Olesch; Bernd Fischer; Peter M. Schlag
The registration of medical images containing soft tissue like inner organs, muscles, fat , etc., is challenging due to complex deformations between different image acquisitions. Despite different approaches to get smooth transformations the number of feasible transformations is still huge and ambiguous local image contents may lead to unwanted results. The incorporation of additional user knowledge is a promising way to restrict the number of possible non-rigid transformations and to increase the probability to find a clinically reasonable solution. A small number of pre-operatively and interactively defined landmarks is a straight forward example for such expert knowledge. Typically, when vessels appear in the image data, a natural way is to determine landmarks as vessel branchings. Here, we present a generalization that allows also the usage of corresponding vessel segments. Therefor, we introduce a registration scheme that can handle anisotropic localization uncertainties. The contribution of this work is a consistent modeling of a combined intensity and landmark registration approach as an inequality constrained optimization problem. This guarantees that each reference landmark lies within an error ellipsoid around the corresponding template landmark at the end of the registration process. First results are presented for the registration of preoperative CT images to intra-operative 3D ultrasound data of the liver as an important issue in an intra-operative navigation system.
Bildverarbeitung für die Medizin | 2011
Janine Olesch; Bernd Fischer
The paper deals with the registration of pre-operative 3DCT- data to tracked intra-operative 2D-US-slices in the context of liver surgery. To bring such a method to clinical practice, it has to be fast and robust. In order to meet these demanding criteria, we propose two strategies. Instead of applying a time-consuming compounding process to obtain a 3D-US image, we use the 2D-slices directly and thereby drastically reduce the complexity and enhance the robustness of the scheme. Naturally, the surgeon does not need the same high resolution for the whole liver. We make use of this fact by applying a focusing technique to regions of special interest. With this, we reduce the overall amount of data to register significantly without sacrificing the accuracy in the ROIs. In contrast to other attempts, the high resolution result in the ROI is combined in a natural way with a global deformation field to obtain a smooth registration of the whole liver. Overall we arrive at a method with a favorable timing. The proposed algorithm was applied to four different patient data-sets and evaluated with respect to the reached vessel-overlap on validation slices. The obtained results are very convincing and will help to bring non-linear registration techniques to the operation theater.
nuclear science symposium and medical imaging conference | 2016
Yi Yin; Oliver Sedlaczek; Johannes Lotz; Janine Olesch; Kai Breuhahn; Dirk Drasdo; Irene E. Vignon-Clementel
Angiogenesis is critically important in invasive tumor growth and metastasis. Many recent studies have focused on the microvasculature in tumor. The most often studied tumor tissues are from animals; rarely human tumors are selected. The analysis of microvasculature may provide useful information to understand the microperfusion of blood in tumor. One question is whether integration of histological analysis of the microvasculature in combination with diffusion-weighted MRI (DWI) gives coherent information about microperfusion. In this work, we aim to achieve the quantitative analysis of microvasculature in human tumor and attempt to compare the vessel features with blood perfusion in tumor. We applied a novel automatic procedure to extract blood vessels and reconstruct the 3D vessel structures in lung tumor based on serial histological tumor tissue slides. The digitized histological tissue slide is in 2D with fine resolution down to cellular level. The method was tested on a stack of 288 serial slides of a human lung tumor. We compared the vessel area fraction in two specific lung tumor regions with the perfusion-related parameter from IVIM (intravoxel incoherent motion) of DWI. High perfusion was observed in tumor sample with high vessel area fraction, hence our preliminary results indicate a possible relation between DWI and histological parameters, which shall be consolidated in future studies.
Proceedings of SPIE | 2013
Johannes Lotz; Bernd M. Fischer; Janine Olesch; Matthias Günther
Motion, like tumor movement due to respiration, constitutes a major problem in radiotherapy and/or diagnostics. A common idea to compensate for the motion in 4D imaging, is to invoke a registration strategy, which aligns the images over time. This approach is especially challenging if real time processing of the data and robustness with respect to noise and acquisition errors is required. To this end, we present a novel method which is based only on selected image features and uses a probabilistic approach to compute the wanted transformations of the 3D images. Moreover, we restrict the search space to rotation, translation and scaling. In an initial phase, landmarks in the first image of the series have to be identified, which are in the course of the scheme automatically transferred to the next image. To find the associated transformation parameters, a probabilistic approach, based on factored sampling, is invoked. We start from a state set containing a fixed number of different candidate parameters whose probabilities are approximated based on the image information at the landmark positions. Subsequent time frames are analyzed by factored sampling from this state set and by superimposing a stochastic diffusion term on the parameters. The algorithm is successfully applied to clinical 4D CT data. Landmarks have been placed manually to mark the tumor or a similar structure in the initial image whose position is then tracked over time. We achieve a processing rate of up to 12 image volumes per second. The accuracy of the tracking after five time steps is measured based on expert placed landmarks. We achieve a mean landmark error of less than 2 mm in each dimension in a region with radius of 25 mm around the target structure.