TY - JOUR
T1 - Fractal frontiers in cardiovascular magnetic resonance
T2 - Towards clinical implementation
AU - Captur, Gabriella
AU - Karperien, Audrey L.
AU - Li, Chunming
AU - Zemrak, Filip
AU - Tobon-Gomez, Catalina
AU - Gao, Xuexin
AU - Bluemke, David A.
AU - Elliott, Perry M.
AU - Petersen, Steffen E.
AU - Moon, James C.
N1 - Publisher Copyright:
© 2015 Captur et al.
PY - 2015/9/7
Y1 - 2015/9/7
N2 - Many of the structures and parameters that are detected, measured and reported in cardiovascular magnetic resonance (CMR) have at least some properties that are fractal, meaning complex and self-similar at different scales. To date however, there has been little use of fractal geometry in CMR; by comparison, many more applications of fractal analysis have been published in MR imaging of the brain. This review explains the fundamental principles of fractal geometry, places the fractal dimension into a meaningful context within the realms of Euclidean and topological space, and defines its role in digital image processing. It summarises the basic mathematics, highlights strengths and potential limitations of its application to biomedical imaging, shows key current examples and suggests a simple route for its successful clinical implementation by the CMR community. By simplifying some of the more abstract concepts of deterministic fractals, this review invites CMR scientists (clinicians, technologists, physicists) to experiment with fractal analysis as a means of developing the next generation of intelligent quantitative cardiac imaging tools.
AB - Many of the structures and parameters that are detected, measured and reported in cardiovascular magnetic resonance (CMR) have at least some properties that are fractal, meaning complex and self-similar at different scales. To date however, there has been little use of fractal geometry in CMR; by comparison, many more applications of fractal analysis have been published in MR imaging of the brain. This review explains the fundamental principles of fractal geometry, places the fractal dimension into a meaningful context within the realms of Euclidean and topological space, and defines its role in digital image processing. It summarises the basic mathematics, highlights strengths and potential limitations of its application to biomedical imaging, shows key current examples and suggests a simple route for its successful clinical implementation by the CMR community. By simplifying some of the more abstract concepts of deterministic fractals, this review invites CMR scientists (clinicians, technologists, physicists) to experiment with fractal analysis as a means of developing the next generation of intelligent quantitative cardiac imaging tools.
KW - Cardiovascular magnetic resonance
KW - Image processing
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=84940824012&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84940824012&partnerID=8YFLogxK
U2 - 10.1186/s12968-015-0179-0
DO - 10.1186/s12968-015-0179-0
M3 - Review article
C2 - 26346700
AN - SCOPUS:84940824012
SN - 1097-6647
VL - 17
JO - Journal of Cardiovascular Magnetic Resonance
JF - Journal of Cardiovascular Magnetic Resonance
IS - 1
M1 - 80
ER -