Model-based registration for dynamic cardiac perfusion MRI

Journal of Magnetic Resonance Imaging

Volumn 24, Issue 5, 2006, Pages 1062-1070

  Adluru, Ganesh ^a,b   DiBella, Edward V R ^a,b   Schabel, Matthias C ^b  

^a Department of Electrical and Computer Engineering   (United States)

^b UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

Author keywords

Cardiac perfusion; Compartment models; Dynamic MR; Motion correction; Registration; Respiratory motion

Indexed keywords

ARTICLE; AXIS; CLINICAL ARTICLE; COMPARATIVE STUDY; HEART MOVEMENT; HEART MUSCLE; HEART PERFUSION; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; METHODOLOGY; PERFUSION WEIGHTED IMAGING; PRIORITY JOURNAL; PROCESS MODEL; QUANTITATIVE ANALYSIS;

ALGORITHMS; COMPUTER SIMULATION; CORONARY ARTERIOSCLEROSIS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, CARDIOVASCULAR; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; VENTRICULAR DYSFUNCTION, LEFT;

EID: 33750474869     PISSN: 10531807     EISSN: 15222586     Source Type: Journal    
DOI: 10.1002/jmri.20756     Document Type: Article

References (18)

1. Schwitter J, Nanz D, Kneifel S. et al. Assessment of myocardial perfusion in coronary artery disease by magnetic resonance: a comparison with positron emission tomography and coronary angiography. Circulation 2001;103:2230-2235.
2. Al-Saadi N, Nagel E, Gross M, et al. Noninvasive detection of myocardial ischemia from perfusion reserve based on cardiovascular magnetic resonance. Circulation 2000;101:1379-1383.
3. Bertschinger KM, Nanz D, Buechi M, et al. Magnetic resonance myocardial first-pass perfusion imaging: parameter optimization for signal response and cardiac coverage. J Magn Reson Imaging 2001;14:556-562.
4. Vallee JP, Sostman HD, MacFall JR, et al. Quantification of myocardial perfusion by MRI after coronary occlusion. Magn Reson Med 1998;40:287-297.
5. Cullen JH, Horsfield MA, Reek CR, Cherryman GR, Barnett DB, Samani NJ. A myocardial perfusion reserve index in humans using first-pass contrast-enhanced magnetic resonance imaging. J Am Coll Cardiol 1999;33:1386-1394.
6. Bidaut LM, Vallee JP. Automated registration of dynamic MR images for the quantification of myocardial perfusion. J Magn Reson Imaging 2001;13:648-655.
7. Gallippi CM, Kramer CM, Hu YL, Vido DA, Reichek N, Rogers WJ. Fully automated registration and warping of contrast-enhanced first-pass perfusion images. J Cardiovasc Magn Reson 2002;4:459-469.
8. Dornier C, Ivancevic MK, Thevenaz P, Vallee JP. Improvement in the quantification of myocardial perfusion using an automatic spline-based registration algorithm. J Magn Reson Imaging 2003;18:160-168.
9. Thevenaz P, Ruttimann UE, Unser M. A pyramid approach to subpixel registration based on intensity. IEEE Trans Image Process 1998;7:27-41.
10. Bracoud L, Vincent F, Pachai C, Canet E, Croisille P, Revel D. Automatic registration of MR first-pass myocardial perfusion images. Lect Notes Comput Sci 2003;2674:215-223.
11. Viola P, Wells III WM. Alignment by maximization of mutual information. Int J Comput Vis 1997;24:137-154.
12. Stegmann MB, Olafsdottir H, Larsson HBW. Unsupervised motion-compensation of multi-slice cardiac perfusion MRI. Med Image Anal 2005;9:394-410.
13. Buonaccorsi GA, Roberts C, Cheung S, Watson Y, Parker GJ. A novel method of model-based rigid registration for dynamic contrast enhanced MRI studies. In: Proceedings of 13th Annual Meeting of ISMRM, Miami Beach, FL, USA, 2005. Abstract 742.
14. Tofts PS, Brix G, Buckley DL, Evelhoch JL, et al. Estimating kinetic parameters from dynamic contrast-enhanced T1-weighted MRI of a diffusable tracer: Standardized quantities and symbols. J Magn Reson Imaging 1999;10:223-232.
15. Jerosch-Herold M, Swingen C, Seethamraju RT. Myocardial blood flow quantification with MRI by model-independent deconvolution. Med Phys 2002;29:886-897.
16. Choyke PL, Dwyer AJ, Knopp MV. Functional tumor imaging with dynamic contrast-enhanced magnetic resonance imaging. J Magn Reson Imaging 2003;17:509-520.
17. Murase K. Efficient method for calculating kinetic parameters using T1-weighted dynamic contrast-enhanced magnetic resonance imaging. Magn Reson Med 2004;51:858-862.
18. Feng D, Wang ZZ, Huang SC. A study on statistically reliable and computationally efficient algorithms for generating local cerebral blood flow parametric images with positron emission tomography. IEEE Trans Med Imaging 1993;12:182-188.