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Virtual DSC-MRI brain research - part 1: tracer concentration curves characteristic for different brain regions
Języki publikacji
Abstrakty
W pracy pokazano metodę tworzenia typowych krzywych stężenia znacznika w badaniu DSC-MRI, charakterystycznych dla trzech obszarów mózgu: substancji białej, szarej oraz naczyń krwionośnych. Na podstawie zależności między wybranymi deskryptorami perfuzji krzywe pomiarowe z rzeczywistego badania DSC-MRI zaklasyfikowano do każdego z tych trzech zbiorów i uśredniono, uzyskując typowe przebiegi zmian stężenia znacznika dla każdego z wyróżnionych obszarów.
DSC-MRI (Dynamic Susceptibility Contrast - Magnetic Resonance Imaging) research is one of the most modern methods for diagnosis of brain diseases, such as: tumours, stroke, epilepsy, migraine and dementia [1, 2]. For this purpose, the so called perfusion parameters are defined, of which the most commonly used are: CBF (Cerebral Blood Flow), CBV (Cerebral Blood Volume) and MTT (Mean Transit Time) [5, 6]. There are many approaches to determine these parameters [4, 5, 8, 9], but regardless of the approach, there is a problem with a quality assessment of particular methods, as well as comparison with others. The paper shows the method of creating typical curves from DSC-MRI studies, characteristic for different regions of the brain: the grey and white matter, and for blood vessels. Using perfusion descriptors, curves were classified into three sets, which after averaging them, gave the model curves for each of the three regions of the brain (Fig. 2). The method was verified by comparing the perfusion parameters calculated on the basis of the obtained characteristic curves with the values presented in the literature (Tab. 1). In Section 2 of the paper the way of using the created curves is shown, i.e. creating a virtual DSC-MRI brain study. Introduction of pathology to the created study and then its identification with use of different methods will allow comparing their effectiveness and quality of created perfusion maps.
Wydawca
Czasopismo
Rocznik
Tom
Strony
1129--1132
Opis fizyczny
Bibliogr. 27 poz., rys., tab., wzor
Twórcy
autor
autor
- Uniwersytet Warmińsko-Mazurski w Olsztynie, Wydział Nauk Technicznych, Katedra Elektrotechniki, Energetyki i Automatyki, ul. Oczapowskiego 11, 10-736 Olsztyn, seweryn.lipinski@uwm.edu.pl
Bibliografia
- [1] Forsting M., Weber J.: MR perfusion imaging: a tool for more than a stroke. Eur Radiol Suppl 2004, 14 (suppl 5), M2-M7.
- [2] Petrella J. R., Provenzale J. M.: MR Perfusion Imaging of the Brain: Techniques and Applications. Am J Roentgenol 2000, 175, 207-219.
- [3] Calamante F., Thomas D. L., Pell G. S., Wiersma J., Turner R.: Measuring Cerebral Blood Flow Using Magnetic Resonance Imaging Techniques. J Cereb Blood Flow Metab 1999, 19, 701-735.
- [4] Jackson D.: Analysis of dynamic contrast enhanced MRI. Br J Radiol 2004, 77, S154-S166.
- [5] van Osch T.: Evaluation of cerebral hemodynamics by quantita-tive perfusion MRI, PrintPartners Ipskamp, Enschede 2002.
- [6] Sorensen A. G., Reimer P.: Cerebral MR Perfusion Imaging. Principles and Current Applications, Georg Thieme Verlag, Stuttgart 2000.
- [7] Kane I., Carpenter T., Chappell F., Rivers C., Armitage P., Sandercock P., Wardlaw J.: Comparison of 10 Different Magnetic Resonance Perfusion Imaging Processing Methods in Acute Ischemic Stroke. Stroke 2007, 38, 3158-3164.
- [8] Perthen J. E., Calamante F., Gadian D. G., Connelly A.: Is Quantification of Bolus Tracking MRI Reliable Without Deconvolution?. Magn Reson Med 2002, 47, 61-67.
- [9] Kalicka R., Pietrenko-Dąbrowska A.: Parametric Modeling of DSC-MRI Data with Stochastic Filtration and Optimal Input Design Versus Non-Parametric Modeling. Ann Biomed Eng 2007, 35 (3), 453-464.
- [10] Knutsson L., Stahlberg F., Wirestam R.: Aspects on the accuracy of cerebral perfusion parameters obtained by dynamic susceptibility contrast MRI: a simulation study. Magn Reson Imaging 2004, 22, 789-798.
- [11] Perkio J., Aronen H. J., Kangasmaki A., Liu Y., Karonen J., Savolainen S., Ostergaard L.: Evaluation of Four Postprocessing Methods for Determination of Cerebral Blood Volume and Mean Transit Time by Dynamic Susceptibility Contrast Imaging. Magn Reson Med 2002, 47, 973-981.
- [12] Kalicka, R., Lipiński, S.: A Fast Method of Separation of the Noisy Background from the Head-Cross Section in the Sequence of MRI Scans. Biocybern Biomed Eng 2010, 30 (2), 15-27.
- [13] Rumiński J., Kalicka R., Bobek-Billewicz B.: Obrazowanie parametryczne w badaniach mózgu metodami MRI/PET. Wydawnictwo Gdańskie, Gdańsk 2006.
- [14] Mouridsen K., Christensen S., Gyldensted L., Ostergaard L.: Automatic Selection of Arterial Input Function Using Cluster Analysis. Magn Reson Med 2006, 55, 524-531.
- [15] Koshimoto Y., Yamada H., Kimura H., Maeda M., Tsuchida C., Kawamura Y., Ishii Y.: Quantitative Analysis of Cerebral Microvascular Hemodynamics with T2-Weighted Dynamic MR Imaging. J Magn Reson Imaging 1999, 9, 462-467.
- [16] Artzi M., Aizenstein O., Hendler T., Bashat D.B.: Unsupervised multiparametric classification of dynamic susceptibility contrast imaging: Study of a healthy brain. Neuroimage 2011, 56, 858-864.
- [17] Kao Y. H., Guo W. Y., Wu Y. T., Liu K. C., Chai W. Y., Lin C. Y., Hwang Y. S., Liou A. J. K., Wu H. M., Cheng H. C., Yeh T. C., Hsieh J. C.: Hemodynamic Segmentation of MR Brain Perfusion Images Using Independent Component Analysis, Thresholding, and Bayesian Estimation. Magn Reson Med 2003, 49, 885-894.
- [18] Bjornerud A., Emblem K. E.: A fully automated method for quantitative cerebral hemodynamic analysis using DSC-MRI. J Cereb Blood Flow Metab 2010, 30, 1066-1078.
- [19] Kalicka R., Lipiński, S.: Ocena przydatności filtracji Kalmana do poprawy właściwości szumowych danych uzyskiwanych w badaniach DSC-MRI mózgu. Pomiary Automatyka Kontrola 2008, 54 (3), 118-121.
- [20] Theodoridis S., Pikrakis A., Kotroumbas K., Cavouras D.: An Introduction to Pattern recognition: A MATLAB Approach. Elsevier Academic Press 2010.
- [21] Jain A. K., Duin R. P.W., Mao J.: Statistical Pattern Recognition: A review. IEEE Trans Pattern Anal 2000, 22 (1), 4-37.
- [22] Semmlow J. L.: Circuits, Systems, and Signals for Bioengineers: A MATLAB-based Introduction. Elsevier Academic Press 2005.
- [23] Calamante F., Gadian D. G., Connelly A.: Quantification of Perfusion Using Bolus Tracking Magnetic Resonance Imaging in Stroke: Assumptions, Limitations, and Potential Implications for Clinical Use. Stroke 2002, 33, 1146-1151.
- [24] Ibaraki M., Ito H., Shimosegawa E., Toyoshima H., Ishigame K., Takahashi K., Kanno I., Miura S.: Cerebral vascular mean transit time in healthy humans: a comparative study with PET and dynamic susceptibility contrast-enhanced MRI. J Cereb Blood Flow Metab 2007, 27, 404-413.
- [25] Schreiber W. G., Guckel F., Stritzke P., Schmiedek P., Schwartz A., Brix G.: Cerebral Blood Flow and Cerebrovascular Reserve Capacity: Estimation by Dynamic Magnetic Resonance Imaging. J Cereb Blood Flow Metab 1998, 18, 1143-1156.
- [26] Wenz F., Rempp K., Brix G., Knopp M. V., Gückel F., Hess T., van Kaick G.: Age dependency of the regional cerebral blood volume (rCBV) measured with dynamic susceptibility contrast MR imaging (DSC). Magn Reson Imaging 1996, 14 (2), 157-62.
- [27] Fuss M., Wenz F., Scholdei R., Essig M., Debus J., Knopp M. V., Wannenmacher M.: Radiation-induced regional cerebral blood volume (rCBV) changes in normal brain and low-grade astrocytomas: quantification and time and dose-dependent occurrence. Int J Radiat Oncol Biol Phys 2000, 48 (1), 53-8.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW4-0125-0024