One of the future-oriented areas of medical image processing is to develop fast and exact algorithms for image
registration. By joining multi-modal images we are able to compensate the disadvantages of one imaging modality
with the advantages of another modality. For instance, a Computed Tomography (CT) image containing the
anatomy can be combined with metabolic information of a Positron Emission Tomography (PET) image. It is
quite conceivable that a patient will not have the same position in both imaging systems. Furthermore some
regions for instance in the abdomen can vary in shape and position due to different filling of the rectum. So a
multi-modal image registration is needed to calculate a deformation field for one image in order to maximize the
similarity between the two images, described by a so-called distance measure.
In this work, we present a method to adapt a multi-modal distance measure, here mutual information (MI),
with weighting masks. These masks are used to enhance relevant image structures and suppress image regions
which otherwise would disturb the registration process. The performance of our method is tested on phantom
data and real medical images.
Due to the long imaging times in SPECT, patient motion is inevitable and constitutes a serious problem for any
reconstruction algorithm. The measured inconsistent projection data leads to reconstruction artefacts which can
significantly affect the diagnostic accuracy of SPECT, if not corrected. Among the most promising attempts
for addressing this cause of artefacts, is the so-called data-driven motion correction methodology. To use this
approach it is necessary to automatically detect patient motion and to subdivide the acquired data in projection
sets accordingly. In this note, we propose three different schemes for automatically detecting patient motion. All
methods were tested on 3D academic examples with different rigid motions, motion times, and camera systems.
On the whole, every method was tested with approximately 400 to 600 test cases. One of the proposed new
methods does show promising results.
Registration of images is a crucial part of many medical imaging tasks. The problem is to find a transformation which aligns two given images. The resulting displacement fields may be for example described as a linear combination of pre-selected basis functions (parametric approach), or, as in our case, they may be computed as the solution of an associated partial differential equation (non-parametric approach). Here, the underlying functional consists of a
smoothness term ensuring that the transformation is anatomically
meaningful and a distance term describing the similarity between the two images. To be successful, the registration scheme has to be tuned for the problem under consideration. One way of incorporating user
knowledge is the employment of weighting masks into the distance
measure, and thereby enhancing or hiding dedicated image parts. In
general, these masks are based on a given segmentation of both images. We present a method which generates a weighting mask for the second image, given the mask for the first image. The scheme is based on active contours and makes use of a gradient vector flow method.
As an example application, we consider the registration of abdominal
computer tomography (CT) images used for radiation therapy. The reference image is acquired well ahead of time and is used for setting up the radiation plan. The second image is taken just before the treatment and its processing is time-critical. We show that the proposed automatic mask generation scheme yields similar results as compared to the approach based on a pre-segmentation of both images. Hence for time-critical applications, as intra-surgery registration, we are able to significantly speed up the computation by avoiding a
pre-segmentation of the second image.