Malign lymphoma are the seventh common death cause in the
western
hemisphere. The therapy of the patients and the prognosis
are highly
related to the dispersal pattern of the lymphom node
disease. For
this reason imaging diagnostics of the whole body are
required on a
regular basis. Prospectively whole-body magnetic
resonance imaging
(MRI) will gain more and more importance, as with it
images can be
acquired without repositioning of the patient. Though a
typical
data-set of a whole-body MRI consists of 512x410x1400
voxel in
average. Such data-sets cannot be evaluated completely
in a
contemporary and reliable manner without computational
aid. This is
especially the case if the volumes have to be compared to
previous
studies (follow-up studies). The project deals with the
development
of efficient methodologies for computer-aided
interpretation of
large medical data-sets as well as time series of those.
By
highlighting medical relevant regions in the image data,
the
physician is assisted and with that a higher
effectiveness and cost
efficiency in clinical routine can be achieved. The main
topic of
the project is the treatment of lymphom node disease
patients,
however a generalization of the proposed methods has to
be possible. In order to successfully finish the project, it requires
a tight
cooperation of computer scientists and physicians. The
involved
groups are the institute of pattern recognition
(Informatik 5) of
the Friedrich-Alexander University Erlangen-Nuremberg and
the
radiology and nuclear medicine of the Charité, Campus
Benjamin-Franklin, Berlin. The workload for the institute
of pattern
recognition is the development of novel efficient
methodologies to
handle large medical data-sets. Their practicability in
the clinical
environment and their validity are evaluated by the
involved
physicians.
Conceptually the project can be separated in two disjunct
approaches. First, lymph nodes are detected in MRI images
of a
single study. The second phase deals with the
localization of the
nodes in time sequences of whole-body MRI scans.
Detection of lymph nodes in a single MRI study
The detection of lymph nodes in a single MRI study bases
on the
evaluation of several weightings of MRI data-sets. The
evaluated
sequences are all studies used in clinical daily routine
(e.g.
T1-weighted, T2-weighted, FLAIR and TIRM sequences). A
very
important fact for the choice of the sequences was their
acquisition
time. First experiments show, that especially T1-weighted
and TIRM
sequences yield promising segmentation and localization
results. In
order to compare both data-sets, they have to be
registered in an
initial preprocessing step. As the acquisition time point
does not
vary very much between the two scans, the volumes are
assumed to be already
matched nearly perfectly. Nevertheless, to correct
slight
movements of the patient, a non-rigid registration of the
images is
performed. As the regarded data-sets are from the same
modality but
have different weightings, the distance measures used
are
originally designed for the alignment of multi-modal
volumes (e.g.
mutual information, normalized cross coefficient).
However, due to
the properties of non-rigid registration the plausibility
of the
matching has to be carefully watched, to guarantee a more
simple
segmentation problem. For the localization of the lymph
nodes
statistical methods are used only. This has two
advantages: first,
using these approaches usually leads to a probability of
the
detection success of the regarded structures, like lymph
nodes for
instance, correlating with the goals of the project.
Second,
statistical methods are more general and with that can be
adapted to
other localization problems more easily. For this
purposes different
classes of approaches are used. For further preprocessing
of the
data-sets, methods like probabilistic intensity adaption
between the
volumes and probabilistic subtraction imaging are
utilized. For the
detection of the nodes, the methods basically rely on
the
clustering of the data-sets by classifying all voxels
utilizing
fuzzy c-means or Markov random fields based methods for
instance.
Detection of lymph nodes in time sequences
A further main topic of the project is the detection of
lymph nodes
in time series of MRI images. For follow up studies the
evaluation
of the required data-sets is very time consuming as more
than one
whole-body scan has to be treated in parallel. Hence the
automatic
localization step is desirable for the physician. As the
single
volumes are acquired at different time points, they have
to be
rigidly pre-registered so that they fit to each other.
This is
followed by a non-rigid registration to compensate non-
linear
deformations. The result of the alignment is a vector
field that
describes the deformations between the two data-sets
according to a
distance measure. With that the deformation field
describes volume
changes of evolving structures, like malign lesions for
instance,
too. There, growing structures are represented by
mathematical
sources while shrinking lesions can be detected as
mathematical
sinks. Together with the information gained from the
previous
localization step in MRI sequences of a single time
point, this is
used to detect changes of the lymph nodes between the
acquired
data-sets. Another possibility to gain information about
local
changes from registration results is to analyze the
differences
between the fixed and the transformed moving images. In
order to
achieve usable results the regularization of the non-
rigid
registration has to be very strict.
Presentation of the localization results
The main goal of the project is not to draw final medical
conclusions but to support the physician in the
evaluation of the
acquired MRI data-sets. This is achieved by marking
clinical
relevant areas in the volumes. This is done by generating
a
probability map of the localization results. The map is
presented as
an overlay to the original data-sets. By choosing a
threshold the
physician is able to create confidence intervals and with
that
adjust the illustration to his needs.