Abstract
Quantitative perfusion imaging using Dynamic Susceptibility Contrast (DSC) MRI method requires to measure the Arterial Input Function (AIF) and deconvolve it from the measured tissue signal. We present a method for automatic recognition of the global AIF based on multistage algorithm. The method is validated using real world (clinically measured) DSC-MRI image series. Only 5% of all automatically generated AIFs (one series) were rejected by the expert. The method can be easily extended to produce a set of local AIFs and can be used as fully automatic or as an intelligent assistant tool for a neuroradiologist.
This work was partly supported by the grant of Polish State Committee for Scientific Research (2003–2006) 4 T11E 042 25.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Calamante F, Gadian DG, Connelly A (2002) Quantification of Perfusion Using Bolus Tracking Magnetic Resonance Imaging in Stroke. Assumptions, Limitations, and Potential Implications for Clinical Use. Stroke 33:1146–1151
Wang J, Alsop DC, Li L, Listerud J, Gonzalez-At JB, Schnall MD, Detre JA (2002) Comparison of Quantitative Perfusion Imaging Using Arterial Spin Labeling at 1.5 and 4.0 Tesla. Magnetic Resonance in Medicine 48:242–254
Cai W, Feng DD, Fulton R (2000) Content based retrieval of dynamic PET func-tional images. IEEE Transactions on Information Technology in Biomedicine 4(2)152–158
Ruminski J, Kaczmarek M, Nowakowski A (2001) Medical Active Thermography-A New Image Reconstruction Method. Lecture Notes in Computer Science LNCS2124 Springer, 274–181
Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages: I. Mathematical approach and statistical analysis, II. Experi-mental comparison and preliminary results. Magn. Reson. Med. 36 715–36
Calamante F, Gadian DG, Connelly A (2000) Delay and dispersion effects in dynamic susceptibility contrast MRI: simulations using singular value decomposition. Magn. Reson. Med. 44 466–73
Ruminski J, Bobek-Billewicz B (2004) Parametric imaging in Dynamic Susceptibility Contrast MRI-phantom and in vivo studies, Proc. of the 26th Int. Conference of the IEEE EMBS, CD-ROM edition, San Francisco
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ruminski, J., Karczewski, B. (2005). Automatic Recognition of the Arterial Input Function in MRI Studies. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_79
Download citation
DOI: https://doi.org/10.1007/3-540-32390-2_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25054-8
Online ISBN: 978-3-540-32390-7
eBook Packages: EngineeringEngineering (R0)