An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data

Twellmann T, Lichte O, Nattkemper TW (2005)
IEEE TRANSACTIONS ON MEDICAL IMAGING 24(10): 1256-1266.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the multi-temporal nature of the three-dimensional volume data obtained from DCE-MRI, evaluation of the image data is a challenging task and tools are required to support the human expert. We investigate an approach for automatic localization and characterization of suspicious lesions in DCE-MRI data. It applies an artificial neural network (ANN) architecture which combines unsupervised and supervised techniques for voxel-by-voxel classification of temporal kinetic signals. The algorithm is easy to implement, allows for fast training and application even for huge data sets and can be directly used to augment the display of DCE-MRI data. To demonstrate that the system provides a reasonable assessment of kinetic signals, the outcome is compared with the results obtained from the model-based three-time-points (3TP) technique which represents a clinical standard protocol for analysing breast cancer lesions. The evaluation based on the DCE-MRI data of 12 cases indicates that, although the ANN is trained with imprecisely labeled data, the approach leads to an outcome conforming with 3TP without presupposing an explicit model of the underlying physiological process.
Stichworte
dynamic contrast-enhanced magnetic resonance; visualization; imaging; artificial neural network
Erscheinungsjahr
2005
Zeitschriftentitel
IEEE TRANSACTIONS ON MEDICAL IMAGING
Band
24
Ausgabe
10
Seite(n)
1256-1266
ISSN
0278-0062
Page URI
https://pub.uni-bielefeld.de/record/1602177

Zitieren

Twellmann T, Lichte O, Nattkemper TW. An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE TRANSACTIONS ON MEDICAL IMAGING. 2005;24(10):1256-1266.
Twellmann, T., Lichte, O., & Nattkemper, T. W. (2005). An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE TRANSACTIONS ON MEDICAL IMAGING, 24(10), 1256-1266. doi:10.1109/TMI.2005.854517
Twellmann, T., Lichte, O., and Nattkemper, T. W. (2005). An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE TRANSACTIONS ON MEDICAL IMAGING 24, 1256-1266.
Twellmann, T., Lichte, O., & Nattkemper, T.W., 2005. An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE TRANSACTIONS ON MEDICAL IMAGING, 24(10), p 1256-1266.
T. Twellmann, O. Lichte, and T.W. Nattkemper, “An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data”, IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 24, 2005, pp. 1256-1266.
Twellmann, T., Lichte, O., Nattkemper, T.W.: An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE TRANSACTIONS ON MEDICAL IMAGING. 24, 1256-1266 (2005).
Twellmann, Thorsten, Lichte, O, and Nattkemper, Tim Wilhelm. “An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data”. IEEE TRANSACTIONS ON MEDICAL IMAGING 24.10 (2005): 1256-1266.

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PMID: 16229413
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