Diseases of the intestinal tract often begin with changes altering the bowel tissue elasticity. Therefore, quantification
of bowel motion would be desirable for diagnosis, treatment monitoring and follow-up. Dynamic MRI can
capture such changes, but quantification requires non-rigid registration.
Towards a computer-assisted quantification for bowel diseases, two innovative methods for detection of bowel
motility restrictions have been developed and evaluated. Therefore a coronal 2D+t image will be extracted from
a dynamic 3D MRI dataset and registered non-rigidly over multiple time steps. The first method generates a
new image from the resulting motion maps by adding the absolute value of the vector for each pixel to the
corresponding values in following time steps. The second method calculates the absolute values only from the
lateral part of the vectors, skipping the coronal part, and thus removes large distortions due to movements caused
by breathing. In this preliminary evaluation both methods will be compared in regard to 5 healthy subjects
(volunteers) and 5 patients with proven restrictions in bowel motility.
It was shown, that for the first method with respiration a classification of volunteers and patients is only
partly possible. However, the second method turns out to be capable of classifying normal and restricted bowel
peristalsis. For the second method the mean motion from patients motion maps are about 34.4% lower than
that from volunteers motion maps. Therefore, for the first time such a classification is possible.
Diseases of the lung often begin with regionally limited changes altering the tissue elasticity. Therefore, quantification
of regional lung tissue motion would be desirable for early diagnosis, treatment monitoring, and follow-up.
Dynamic MRI can capture such changes, but quantification requires non-rigid registration. However, analysis of
dynamic MRI data of the lung is challenging due to inherently low image signal and contrast.
Towards a computer-assisted quantification for regional lung diseases, we have evaluated two Demons-based
registration methods for their accuracy in quantifying local lung motion on dynamic MRI data. The registration
methods were applied on masked image data, which were pre-segmented with a graph-cut algorithm.
Evaluation was performed on five datasets from healthy humans with nine time frames each. As gold standard,
manually defined points (between 8 and 24) on prominent landmarks (essentially vessel structures) were
used. The distance between these points and the predicted landmark location as well as the overlap (Dice
coefficient) of the segmentations transformed with the deformation field were calculated. We found that the
Demons algorithm performed better than the Symmetric Forces Demons algorithm with respect to average
landmark distance (6.5 mm ± 4.1 mm vs. 8.6 mm ± 6.1 mm), but comparable regarding the Dice coefficient
(0.946 ± 0.018 vs. 0.961 ± 0.018). Additionally, the Demons algorithm computes the deformation in only
10 seconds, whereas the Symmetric Forces Demons algorithm takes about 12 times longer.