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.
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.
In navigated liver surgery it is an important task to align intra-operative data to pre-operative planning data.
This work describes a method to register pre-operative 3D-CT-data to tracked intra-operative 2D US-slices.
Instead of reconstructing a 3D-volume out of the two-dimensional US-slice sequence we directly apply the registration
scheme to the 2D-slices. The advantage of this approach is manyfold. We circumvent the time consuming
compounding process, we use only known information, and the complexity of the scheme reduces drastically. As
the liver is a non-rigid organ, we apply non-linear techniques to take care of deformations occurring during the
intervention. During the surgery, computing time is a crucial issue. As the complexity of the scheme is proportional
to the number of acquired slices, we devise a scheme which starts out by selecting a few "key-slices" to
be used in the non-linear registration scheme. This step is followed by multi-level/multi-scale strategies and fast
optimization techniques. In this abstract we briefly describe the new method and show first convincing results.
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.
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.