Since the first clinical interventions in the late 1980s, Deep Brain Stimulation (DBS) of the subthalamic nucleus has evolved into a very effective treatment option for patients with severe Parkinson's disease. DBS entails the implantation of an electrode that performs high frequency stimulations to a target area deep inside the brain. A very accurate placement of the electrode is a prerequisite for positive therapy outcome. The assessment of the intervention result is of central importance in DBS treatment and involves the registration of pre- and postinterventional scans. <p> </p>In this paper, we present an image processing pipeline for highly accurate registration of postoperative CT to preoperative MR. Our method consists of two steps: a fully automatic pre-alignment using a detection of the skull tip in the CT based on fuzzy connectedness, and an intensity-based rigid registration. The registration uses the Normalized Gradient Fields distance measure in a multilevel Gauss-Newton optimization framework and focuses on a region around the subthalamic nucleus in the MR. <p> </p>The accuracy of our method was extensively evaluated on 20 DBS datasets from clinical routine and compared with manual expert registrations. For each dataset, three independent registrations were available, thus allowing to relate algorithmic with expert performance. Our method achieved an average registration error of 0.95mm in the target region around the subthalamic nucleus as compared to an inter-observer variability of 1.12 mm. Together with the short registration time of about five seconds on average, our method forms a very attractive package that can be considered ready for clinical use.
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.
Lung registration in thoracic CT scans has received much attention in the medical imaging community. Possible applications range from follow-up analysis, motion correction for radiation therapy, monitoring of air flow and pulmonary function to lung elasticity analysis. In a clinical environment, runtime is always a critical issue, ruling out quite a few excellent registration approaches. In this paper, a highly efficient variational lung registration method based on minimizing the normalized gradient fields distance measure with curvature regularization is presented. The method ensures diffeomorphic deformations by an additional volume regularization. Supplemental user knowledge, like a segmentation of the lungs, may be incorporated as well. The accuracy of our method was evaluated on 40 test cases from clinical routine. In the EMPIRE10 lung registration challenge, our scheme ranks third, with respect to various validation criteria, out of 28 algorithms with an average landmark distance of 0.72 mm. The average runtime is about 1:50 min on a standard PC, making it by far the fastest approach of the top-ranking algorithms. Additionally, the ten publicly available DIR-Lab inhale-exhale scan pairs were registered to subvoxel accuracy at computation times of only 20 seconds. Our method thus combines very attractive runtimes with state-of-the-art accuracy in a unique way.
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.
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.
In this work we evaluate a novel method for multi-modal image registration of MR images. The key feature of our approach is a new distance measure that allows for comparing modalities that are related by an arbitrary gray-value mapping. The novel measure is formulated as least square problem for minimizing the sum of squared
differences of two images with respect to changing gray-values of one of the images. It turns out that the novel measure can be computed explicitly and allows for very simple and efficient implementation. We compare our new approach to rigid registration with cross-correlation, mutual information, and normalized gradient fields as distance measure.
Image registration is an important and active area of medical image processing. Given two images, the idea is
to compute a reasonable displacement field which deforms one image such that it becomes similar to the other
image. The design of an automatic registration scheme is a tricky task and often the computed displacement
field has to be discarded, when the outcome is not satisfactory. On the other hand, however, any displacement
field does contain useful information on the underlying images.
It is the idea of this note, to utilize this information and to benefit from an even unsuccessful attempt for the
subsequent treatment of the images. Here, we make use of typical vector analysis operators like the divergence
and curl operator to identify meaningful portions of the displacement field to be used in a follow-up run. The
idea is illustrated with the help of academic as well as a real life medical example. It is demonstrated on how the
novel methodology may be used to substantially improve a registration result and to solve a difficult segmentation
In this work we present a novel approach for elastic image registration of multi-phase contrast enhanced CT
images of liver. A problem in registration of multiphase CT is that the images contain similar but complementary
structures. In our application each image shows a different part of the vessel system, e.g., portal/hepatic
venous/arterial, or biliary vessels. Portal, arterial and biliary vessels run in parallel and abut on each other
forming the so called portal triad, while hepatic veins run independent. Naive registration will tend to align
Our new approach is based on minimizing a cost function consisting of a distance measure and a regularizer.
For the distance we use the recently proposed normalized gradient field measure that focuses on the alignment
of edges. For the regularizer we use the linear elastic potential. The key feature of our approach is an additional
penalty term using segmentations of the different vessel systems in the images to avoid overlaps of complementary
structures. We successfully demonstrate our new method by real data examples.