DocumentCode :
140388
Title :
Closed-form solution of the convolution integral in the magnetic resonance dispersion model for quantitative assessment of angiogenesis
Author :
Turco, S. ; Janssen, A.J.E.M. ; Lavini, C. ; de la Rosette, J.J. ; Wijkstra, H. ; Mischi, Massimo
Author_Institution :
Dept. of Electr. Eng., Eindhoven Univ. of Technol., Eindhoven, Netherlands
fYear :
2014
fDate :
26-30 Aug. 2014
Firstpage :
4272
Lastpage :
4275
Abstract :
Prostate cancer (PCa) diagnosis and treatment is still limited due to the lack of reliable imaging methods for cancer localization. Based on the fundamental role played by angiogenesis in cancer growth and development, several dynamic contrast enhanced (DCE) imaging methods have been developed to probe tumor angiogenic vasculature. In DCE magnetic resonance imaging (MRI), pharmacokinetic modeling allows estimating quantitative parameters related to the physiology underlying tumor angiogenesis. In particular, novel magnetic resonance dispersion imaging (MRDI) enables quantitative assessment of the microvascular architecture and leakage, by describing the intravascular dispersion kinetics of an extravascular contrast agent with a dispersion model. According to this model, the tissue contrast concentration at each voxel is given by the convolution between the intravascular concentration, described as a Brownian motion process according to the convective-dispersion equation, with the interstitium impulse response, represented by a mono-exponential decay, and describing the contrast leakage in the extravascular space. In this work, an improved formulation of the MRDI method is obtained by providing an analytical solution for the convolution integral present in the dispersion model. The performance of the proposed method was evaluated by means of dedicated simulations in terms of estimation accuracy, precision, and computation time. Moreover, a preliminary clinical validation was carried out in five patients with proven PCa. The proposed method allows for a reduction by about 40% of computation time without any significant change in estimation accuracy and precision, and in the clinical performance.
Keywords :
Brownian motion; biomedical MRI; cancer; convolution; image enhancement; medical image processing; parameter estimation; physiological models; tumours; Brownian motion process; DCE magnetic resonance imaging; closed-form solution; convective-dispersion equation; convolution integral; dynamic contrast enhanced imaging methods; interstitium impulse response; intravascular dispersion kinetics; magnetic resonance dispersion model; monoexponential decay; pharmacokinetic modeling; physiology; prostate cancer diagnosis; prostate cancer treatment; quantitative angiogenesis assessment; quantitative microvascular architecture assessment; quantitative parameter estimation; tissue contrast concentration; tumor angiogenic vasculature; Convolution; Dispersion; Magnetic resonance imaging; Mathematical model; Prostate cancer;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
ISSN :
1557-170X
Type :
conf
DOI :
10.1109/EMBC.2014.6944568
Filename :
6944568
Link To Document :
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