Respiratory motion is a challenging factor for image-guided procedures in the abdominal region. Target localization,
an important issue in applications like radiation therapy, becomes difficult due to this motion. Therefore,
it is necessary to detect the respiratory signal to have a higher accuracy in planning and treatment. We propose
a novel image-based breathing gating method to recover the breathing signal directly from the image data. For
the gating we use Laplacian eigenmaps, a manifold learning technique, to determine the low-dimensional manifold
embedded in the high-dimensional space. Since Laplacian eigenmaps assign each 2D MR slice a coordinate
in a low-dimensional space by respecting the neighborhood relationship, they are well suited for analyzing the
respiratory motion. We perform the manifold learning on MR slices acquired from a fixed location. Then,
we use the resulting respiratory signal to derive a similarity criterion to be used in applications like 4D MRI
reconstruction. We perform experiments on liver data using one and three dimensions as the dimension of the
manifold and compare the results. The results from the first case show that using only one dimension as the
dimension of the manifold is not enough to represent the complex motion of the liver caused by respiration. We
successfully recover the changes due to respiratory motion by using three dimensions. The proposed method
has the potential of reducing the processing time for the 4D reconstruction significantly by defining a search
window for a subsequent registration approach. It is fully automatic and does not require any prior information
or training data.
Recent technological advances in magnetic resonance imaging (MRI) lead to shorter acquisition times and consequently
make it an interesting whole-body imaging modality. The acquisition time can further be reduced by acquiring images
with a large field-of-view (FOV), making less scan stations necessary. Images with a large FOV are however disrupted by
severe geometric distortion artifacts, which become more pronounced closer to the boundaries. Also the current trend in
MRI, towards shorter and wider bore magnets, makes the images more prone to geometric distortion.
In a previous work,<sup>4</sup> we proposed a method to correct for those artifacts using simultaneous deformable registration.
In the future, we would like to integrate previous knowledge about the distortion field into the process. For this purpose
we scan a specifically designed phantom consisting of small spheres arranged in a cube. In this article, we focus on the
automatic extraction of the centers of the spheres, wherein we are particularly interested, for the calculation of the distortion
The extraction is not trivial because of the significant intensity inhomogeneity within the images. We propose to use
the local phase for the extraction purposes. The phase has the advantage that it provides structural information invariant
to intensity. We use the monogenic signal to calculate the phase. Subsequently, we once apply a Hough transform and
once a direct maxima search, to detect the centers. Moreover, we use a gradient and variance based approach for the radius
estimation. We performed our extraction on several phantom scans and obtained good results.
The introduction of 2D array ultrasound transducers enables the instantaneous acquisition of ultrasound volumes in the
clinical practice. The next step coming along is the combination of several scans to create compounded volumes that
provide an extended field-of-view, so called mosaics. The correct alignment of multiple images, which is a complex task,
forms the basis of mosaicing. Especially the simultaneous intensity-based registration has many properties making it a
good choice for ultrasound mosaicing in comparison to the pairwise one.
Fundamental for each registration approach is a suitable similarity measure. So far, only standard measures like SSD,
NNC, CR, and MI were used for mosaicing, which implicitly assume an additive Gaussian distributed noise. For ultrasound
images, which are degraded by speckle patterns, alternative noise models based on multiplicative Rayleigh distributed noise
were proposed in the field of motion estimation.
Setting these models into the maximum likelihood estimation framework, which enables the mathematical modeling
of the registration process, led us to ultrasound specific bivariate similarity measures. Subsequently, we used an extension
of the maximum likelihood estimation framework, which we developed in a previous work, to also derive multivariate
measures. They allow us to perform ultrasound specific simultaneous registration for mosaicing. These measures have
a higher potential than afore mentioned standard measures since they are specifically designed to cope with problems
arising from the inherent contamination of ultrasound images by speckle patterns. The results of the experiments that we
conducted on a typical mosaicing scenario with only partly overlapping images confirm this assumption.