Image-guided radiofrequency ablation (RFA) is becoming a standard procedure for minimally invasive tumor
treatment in clinical practice. To verify the treatment success of the therapy, reliable post-interventional assessment
of the ablation zone (coagulation) is essential. Typically, pre- and post-interventional CT images have to
be aligned to compare the shape, size, and position of tumor and coagulation zone. In this work, we present
an automatic workflow for masking liver tissue, enabling a rigid registration algorithm to perform at least as
accurate as experienced medical experts. To minimize the effect of global liver deformations, the registration is
computed in a local region of interest around the pre-interventional lesion and post-interventional coagulation
necrosis. A registration mask excluding lesions and neighboring organs is calculated to prevent the registration
algorithm from matching both lesion shapes instead of the surrounding liver anatomy. As an initial registration
step, the centers of gravity from both lesions are aligned automatically. The subsequent rigid registration method
is based on the Local Cross Correlation (LCC) similarity measure and Newton-type optimization. To assess the
accuracy of our method, 41 RFA cases are registered and compared with the manually aligned cases from four
medical experts. Furthermore, the registration results are compared with ground truth transformations based on
averaged anatomical landmark pairs. In the evaluation, we show that our method allows to automatic alignment
of the data sets with equal accuracy as medical experts, but requiring significancy less time consumption and
Radiofrequency (RF) ablation is an image-guided minimally invasive therapy which destroys a tumor by locally
inducing electrical energy into the malignant tissue through a radiofrequency applicator. Treatment success is essentially
dependent on the accurate placement of the RF applicator. In the case of CT-guided RF ablation of liver tumors, a central
problem during monitoring is the reduced quality and information content in the peri-interventional images compared to
the images used for planning. Therefore, the question of how to effectively transfer information from the planning scan
into the peri-interventional scan in order to support the interventionalist is of high interest. Key to such an enhancement
of peri-interventional scans is an adequate registration of the pre- and peri-interventional image, which also needs to be
fast since intervention duration is still a challenge. We present an approach for the fast and automatic registration of a
high quality CT volume scan of the liver to a spiral CT scan of lower quality. Our method combines an approximate pre-registration
to compensate large displacements and a rigid registration of a liver subvolume for further refinement. The
method focuses on the position of the tumor to be ablated and the corresponding access path. Thereby, it achieves both
fast and precise results in the region of interest. A preliminary evaluation, on 37 data sets from 20 different patients,
shows that the registration is performed within a maximum of 18 seconds, while obtaining high accuracy in the relevant
region of the liver comprising tumor and the planned access path.
Sensors used for security purposes have to cover the non-invasive control of men and direct surroundings of buildings
and camps to detect weapons, explosives and chemical or biological threat material. Those sensors have to cope with
different environmental conditions. Ideally, the control of people has to be done at a longer distance as standoff
detection. The work described in this paper concentrates on passive radiometric sensors at 0.1 and 0.2 THz which are
able to detect non-metallic objects like ceramic knifes. Also the identification of objects like mobile phones or PDAs will
be shown. Additionally, standoff surveillance is possible, which is of high importance with regard to suicide bombers.
The presentation will include images at both mentioned frequencies comparing the efficiency in terms of range and
resolution. In addition, the concept of the sensor design showing a Dicke-type 220GHz radiometer using new LNAs and
the results along with image enhancement methods are shown.
2.1 Main principle
Correction of patient motion is a fundamental preprocessing step for dynamic contrast-enhanced (DCE) breast MRI, removing artifacts induced by involuntary movement and facilitating quantitative analysis of contrast agent kinetics. Image registration algorithms commonly employed for this task align subsequent temporal images of the dynamic MRI by maximizing intensity-, correlation- or entropy-based similarity measures between image pairs. To compensate for global patient motion, frequently an initial affine linear or rigid transformation is estimated. Subsequently, local image variablity is reduced by maximizing local similarity measures and using viscous fluid or elastic regularization terms. We present a novel iterative scheme combining local and global registration into one single algorithm, limiting computational overhead, reducing interpolation artifacts and generally improving the quality of registration results. The relation between local and global motion is adjusted by the introduction of corresponding flexible weighting functions, allowing for a sound combination of both registration types and a potentially wider range of computable transformations. The proposed method is evaluated on both synthetic images and clinical breast MRI data. The results demonstrate that our method works stable and reliably compensates for common motion artifacts typical to DCE MR mammography.
In image registration of medical data a common and challenging problem is handling intensity-inhomogeneities. These inhomogeneities appear for instance in images of serially sectioned brains caused by the histological staining process or in medical imaging with contrast agents. Beneath this, natural outliers (for instance cells or vessels) produced by the underlying material itself may be mistaken as noise. Both image registration applications have in common that the well known sum of squared differences (SSD) measure would detect false differences. To deal with these kinds of problems, we supplement the common SSD-measure with image derivatives of higher order. Additionally we introduce a non-quadratic penalizer function to the distance measure leading to robust energy. The concepts are well known in optical flow. Overall, we present a variational model which combines all of these properties. This formulation leads to a fast and efficient algorithm. We demonstrate its applicability at the problems described above.
We present a super-fast and parameter-free algorithm for non-rigid elastic registration of images of a serially sectioned whole rat brain. The purpose is to produce a three-dimensional high-resolution reconstruction. The registration is modelled as a minimization problem of a functional consisting of a distance measure and a regularizer based on the elastic potential of the displacement field. The minimization of the functional leads to a system of non-linear partial differential equations, the so-called Navier-Lame equations (NLE). Discretization of the NLE and a fixed point type iteration method lead to a linear system of equations, which has to be solved at each iteration step. We not only present a super-fast solution technique for this system, but also come up with sound strategies for accelerating the outer iteration. This does include a multi-scale approach based on a Gaussian pyramid as well as a clever estimation of the material constants for the elastic potential. The results of the registration process were controlled by an expert who was able to recognize histological details like laminations which was not possible before. Therefore, it is essential to apply elastic registration to this kind of imaging problem. Finally, the visually pleasing results were quantified by a distance measure leading to an improvement of about 79% after just 35 iteration steps.