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Warianty tytułu
Virtual DSC-MRI brain research - part 2: model sequence of MRI scans and possibilities of modifications
Języki publikacji
Abstrakty
Utworzone w pierwszej części artykułu modelowe krzywe stężenia znacznika wykorzystano do stworzenia wirtualnego badania mózgu metodą DSC-MRI. Pokazano również modyfikacje badania polegające na wprowadzeniu do przekroju mózgu przykładów symulowanych patologii i szumu. Utworzony model może zostać wykorzystany do porównania różnych podejść do obliczania parametrów perfuzji, a w szczególności czułości poszczególnych metod na zmiany charakterystyczne dla różnych chorób.
The paper proposes the virtual DSC-MRI research of the brain. The curves corresponding to perfusion of different brain regions (Fig. 1) in a suitable arrangement (consistent with human anatomy) form a model of the research (Fig. 3). In the created model one knows in advance the values of the complex perfusion parameters and basic perfusion descriptors. Model study can be disturbed in a controlled manner - not only by adding noise of the assumed level and characteristics only to the DSC-MRI signals, but also determining location of disturbances. With the introduction of only noise to DSC-MRI signals, which is the classic approach proposed in the literature, it is impossible to evaluate methods for determining the perfusion parameters in presence of certain types of disturbances, especially those characteristic for the image data. The described model of the research allows avoiding this problem and helps to assess different approaches to the calculation of perfusion parameters reliably, objectively and taking into account differences in the perfusion of different brain areas. The model allows inserting the disturbed signals, characteristic for certain pathologies, to the sequence (Fig. 5). In that case the criterion for evaluating the approach to determine the perfusion parameters is to verify the threshold (defined for example as the diameter of introduced pathology) of detecting perfusion abnormalities in the presence of different types and levels of disturbances.
Wydawca
Czasopismo
Rocznik
Tom
Strony
1133--1136
Opis fizyczny
Bibliogr. 22 poz., rys., rys., wykr
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] Tofts P. (ed.): Quantitative MRI of the brain. Measuring changes caused by disease, John Wiley and Sons 2004.
- [2] Sorensen A. G., Reimer P.: Cerebral MR Perfusion Imaging. Principles and Current Applications, Georg Thieme Verlag, Stuttgart 2000.
- [3] 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.
- [4] 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.
- [5] Jackson D.: Analysis of dynamic contrast enhanced MRI. Br J Radiol 2004, 77, S154-S166.
- [6] 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.
- [7] 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.
- [8] Salluzzi M., Frayne R., Smith M. R.: Is correction necessary when clinically determining quantitative cerebral perfusion parameters from multi-slice dynamic susceptibility contrast MR studies?. Phys Med Biol 2006, 51, 407-424.
- [9] Smith M. R., Lu H., Frayne R.: Signal-to-Noise Ratio Effects in Quantitative Cerebral perfusion Using Dynamic Susceptibility Contrast Agents. Magn Reson Med 2003, 49, 122-128.
- [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] Kalicka R., Lipiński S.: Wirtualne badanie DSC-MRI mózgu - część 1: krzywe stężenia znacznika charakterystyczne dla różnych rejonów mózgu. Pomiary Automatyka Kontrola.
- [12] Forsting M., Weber J.: MR perfusion imaging: a tool for more than a stroke. Eur Radiol Suppl 2004, 14 (suppl 5), M2-M7.
- [13] Calamante F., Ganesan V., Kirkham F. J., Chir B., Jan W., Chong W. K., Gadian D. G., Connelly A.: MR Perfusion Imaging in Moyamoya Syndrome. Stroke 2001, 32, 2810-2816.
- [14] Akella N. S., Twieg D. B, Mikkelsen T., Hochberg F. H., Grossman S., Cloud G. A., Nabors L. B.: Assessment of Brain Tumor Angiogenesis Inhibitors Using Perfusion Magnetic Resonance Imaging: Quality and Analysis Results of a Phase I Trial. J Magn Reson Imaging 2004, 20, 913-922.
- [15] Grandin C. B., Duprez T. P., Smith A. M., Oppenheim C., Peeters A., Robert A. R., Cosnard G.: Which MR-derived perfusion parameters are the best predictors of infarct growth in hyperacute stroke? Comparative study between relative and quantitative measurements. Radiology 2002, 223 (2), 361-370.
- [16] Rumiński J., Kalicka R., Bobek-Billewicz B.: Obrazowanie parametryczne w badaniach mózgu metodami MRI/PET. Wydawnictwo Gdańskie, Gdańsk 2006.
- [17] Wintermark M., Sesay M., Barbier E., Borbély K., Dillon W. P., Eastwood J. D., Glenn T. C., Grandin C. B., Pedraza S., Soustiel J. F., Nariai T., Zaharchuk G., Caillé J. -M., Dousset V., Yonas H.: Comparative Overview of Brain Perfusion Imaging Techniques. Stroke 2005, 36, e83-e99.
- [18] Ostergaard L., Weisskoff R. M., Chesler D. A., Gyldensted C., Rosen B. R.: High resolution Measurement of Cerebral Blood Flow using Intravascular Tracer Bolus Passages. Part I: Mathematical Approach and Statistical Analysis. Magn Reson Med 1996, 36, 715-725.
- [19] Cocosco C. A., Kollokian V., Kwan R. K. -S., Evans A. C.: BrainWeb: Online Interface to a 3D MRI Simulated Brain Database. NeuroImage 1997, 5 (3), 1996.
- [20] Kwan R. K. -S., Evans A. C., Pike G. B.: An Extensible MRI Simulator for Post-Processing Evaluation. Lect Notes Comput Sci 1996, 1131, 135-140.
- [21] Aubert-Broche B., Griffin M., Pike G. B., Evans A. C., Collins D. L.: Twenty new digital brain phantoms for creation of validation image data bases. IEEE Trans Med Imaging. 2006, 25 (11), 1410-1416.
- [22] Aubert-Broche B., Evans A. C., Collins D. L.: A new improved version of the realistic digital brain phantom. NeuroImage 2006, 32, 138-145.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW4-0125-0025