The heart is one of the most important organs within the human body. It maintains the blood circulation
which is crucial to transport substances through the body to e.g. oxygenate the brain or muscles.
Dysfunctions can lead to death. Cardiovascular and circulatory diseases are one of the most common
causes of mortality [1]. To indicate such a disease a cardiac functional analysis is often performed. A
cardiac functional analysis assesses the vitality of the heart based on objective criteria such as ejection
fraction and motion evaluation. This analysis helps to identify, localize and treat dysfunctions. Especially
tracing the dynamics of the heart, referred to as myocardial strain analysis, gives diagnostic
hints. The movement of the heart and each chamber can be tracked using cardiac imaging. There are
three possible directions for the motion: radial, longitudinal and circumferential, which can be seen
as intrinsic dynamics. For instance in case of acute myocarditis the intrinsic dynamics differ significantly
from those of healthy patients. As the left ventricle is the largest of the four heart chambers –
dysfunctions might have the highest impact on its function and therefore on its dynamics [2, 3].
To perform a cardiac functional analysis or to be precise a strain analysis of the left ventricle noninvasive
imaging modalities are used. Cardiac magnetic resonance and echocardiography are commonly
used and build the state of the art methods [4, 5]. Up to now computed tomography is barely used for
assessing the cardiac function but more for identifying diseases as coronary atherosclerosis [6]. The
myocardium tends to have a low contrast in computed tomography images of the heart. Therefore
tracking of endo- and epicardium becomes more complicated. Due to this, the complexity of performing
a cardiac functional analysis increases. But aspects like high resolution, time- and cost efficiency
motivate to overcome these difficulties.
Within this project it will be investigate whether computed tomography is capable to perform a
cardiac functional analysis. The goal is to automate the strain analysis of the left ventricle using
computed tomography.
Given several 3D CT images of a heartbeat cycle the strain analysis will be performed in the following
way:
1. The myocardium of the left ventricle has to be localized and segmented using an active shape
model.
2. For the motion tracking different registration algorithms will be evaluated. The range of approaches
is broad. Possible methods for landmark based registration would be thin-plate splines [7]. As the
complexity increases with the dimension 2D intensity based registration could be performed on sliced
images [8]. Using the whole 3D image there are more classic algorithms as well as deep learning based
approaches are conceivable [9, 10].
3. The intrinsic dynamics, namely longitudinal, radial and circumferential strain, are calculated by
projecting the transformation vectors provided by the registration algorithm onto the main axes of an
intrinsic coordinate system.
4. The results will be visualized as color coded strain magnitude within the CT image or in a polar
map [3].
5. The results of different registration algorithms are qualitatively and quantitatively compared using
the visualizations as well as different metrics, possibly such as the correlation coefficient [11] and the
Hausdorff distance [12].
References
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update from the gbd 2019 study. Journal of the American College of Cardiology, 76(25):2982–
3021, 2020.
[2] Otto A Smiseth, Hans Torp, Anders Opdahl, Kristina H Haugaa, and Stig Urheim. Myocardial
strain imaging: how useful is it in clinical decision making? European Heart Journal, 37(15):1196–
1207, 2016.
[3] Aldostefano Porcari et al. Strain analysis reveals subtle systolic dysfunction in confirmed and
suspected myocarditis with normal lvef. a cardiac magnetic resonance study. Clinical Research
in Cardiology, 109(7):869–880, 2020.
[4] Dagmar F Hernandez-Suarez and Angel L´opez-Candales. Strain imaging echocardiography: what
imaging cardiologists should know. Current Cardiology Reviews, 13(2):118–129, 2017.
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Computed tomography and cardiac magnetic resonance in ischemic heart disease. Journal of the
American College of Cardiology, 68(20):2201–2216, 2016.
[7] Rainer Sprengel, Karl Rohr, and H Siegfried Stiehl. Thin-plate spline approximation for image
registration. In Proceedings of 18th annual international conference of the IEEE Engineering in
Medicine and Biology Society, volume 3, pages 1190–1191. IEEE, 1996.
[8] Christoph Guetter, Hui Xue, Christophe Chefd’Hotel, and Jens Guehring. Efficient symmetric
and inverse-consistent deformable registration through interleaved optimization. In 2011 IEEE
international symposium on biomedical imaging: from nano to macro, pages 590–593. IEEE, 2011.
[9] Huajun Song and Peihua Qiu. Intensity-based 3d local image registration. Pattern Recognition
Letters, 94:15–21, 2017.
[10] Guha Balakrishnan, Amy Zhao, Mert R Sabuncu, John Guttag, and Adrian V Dalca. Voxelmorph:
a learning framework for deformable medical image registration. IEEE Transactions on Medical
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[11] Richard Taylor. Interpretation of the correlation coefficient: a basic review. Journal of Diagnostic
Medical Sonography, 6(1):35–39, 1990.
[12] D.P. Huttenlocher, G.A. Klanderman, and W.J. Rucklidge. Comparing images using the hausdorff
distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9):850–863, 1993.